Preface - Chair of Systems Design - ETH Z¼rich

Preface

In October 2004, I started to build up the newly founded Chair of Sys-tems Design at ETH Zurich – at that time relying on only one formercollaborator. Now, after five years of efforts, our group consists of morethan 15 collaborators with a broad range of research topics and exter-nally funded projects.

This is a good reason to look back and to draw some conclusions:what happened to the ambitious goals of that time? In what directiondid our research develop? How did we form scientific connections –within ETH, the Swiss research community, across Europe? But it isalso a good reason to look ahead: where do we want to be in the nextfive years? What is our research agenda in the long run?

In order to prepare for such a discussion, it is certainly helpful torealize in detail where exactly we spent our time and effort and whatwe achieved during the last five years. This report was jointly preparedfor this purpose. It reflects the research profile, we had to develop fromscratch when starting in the newly founded Department of Management,Technology, and Economics (D-MTEC) of ETH Zurich. It also gives in-formation about our teaching, in particular about the courses we devel-oped for our Master program. Much effort during these five years wasalso spent on events, in order to reach some visibility. These include talkspresented at conferences, workshops organized by us, colloquium talksof invited guests, etc. These activities are complemented by a broadrange of services we provide to the scientific community, from runningscientific journals, reviewing papers for conferences, to the developmentof free software to enhance our everyday tasks.

I very much hope that this overview of our activities may stimulatenew ones – possibly involving a collaboration with you. Thus, we arelooking forward to the next five years of productive relationships.

30 October 2009 Frank Schweitzer

Contents

1 Our Team 51.1 People . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51.2 Theses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71.3 IT Infrastructure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

2 Research Topics 132.1 Economic Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

2.1.1 Systemic Risk . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152.1.2 Ownership Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 162.1.3 Knowledge Transfer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

2.2 Social Organizations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 182.2.1 Opinion Dynamics and Emergence of Norms . . . . . . . . . . . . . . . . . 192.2.2 Trust-based Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 212.2.3 Structure and Dynamics of Open Source Software . . . . . . . . . . . . . . 222.2.4 Empirics of User Ratings and Herding Behavior . . . . . . . . . . . . . . . 232.2.5 Collective Emotions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 242.2.6 Social Structure of Animal Societies . . . . . . . . . . . . . . . . . . . . . 25

2.3 Further Research Topics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 262.3.1 Agent-based Models of Socio-economic Dynamics . . . . . . . . . . . . . . 262.3.2 Structure and Dynamics of Complex Networks . . . . . . . . . . . . . . . 272.3.3 Geographical Localization and Clustering . . . . . . . . . . . . . . . . . . 282.3.4 Agent-Based Models of Biological Motion . . . . . . . . . . . . . . . . . . 29

3 Publications 31

4 Talks 37

5 Funded Projects 475.1 Measuring and Modeling Complex Networks Across Domains . . . . . . . . . . . 485.2 Physics of Risk . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 495.3 Physics of Competition, Cooperation and Conflicts . . . . . . . . . . . . . . . . . 515.4 Ownership Networks and Corporate Control . . . . . . . . . . . . . . . . . . . . . 525.5 Proof-of-Concept of a Trust-based Recommender System . . . . . . . . . . . . . . 535.6 CCSS: Coping with Crises in Complex Socio-Economic Systems . . . . . . . . . . 545.7 Cyberemotions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 555.8 Open Source Software . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 565.9 R&D Network Life Cycles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 575.10 OTC Derivatives and Systemic Risk in Financial Networks . . . . . . . . . . . . . 58

4 Contents

6 Teaching 596.1 Systems Dynamics and Complexity . . . . . . . . . . . . . . . . . . . . . . . . . . 606.2 Collective Dynamics of Firms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 636.3 Complex Adaptive Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66

7 Events 697.1 Workshops and Schools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69

7.1.1 2010: Emergent Intelligence on Networked Agents . . . . . . . . . . . . . 697.1.2 2009: Coping with Crises in Complex Socio-Economic Systems . . . . . . 707.1.3 2008: The Physics Approach To Risk: Agent-Based Models and Networks 727.1.4 2008: Challenges and Visions in the Social Sciences . . . . . . . . . . . . . 747.1.5 2007: Enhancing Social Interaction (ECCS’07 Satellite Workshop) . . . . 777.1.6 2006: Trust-Based Networks and Robustness in Organisations . . . . . . . 787.1.7 2005: Dynamics Of Socio-Economic Systems: A Physics Perspective . . . 807.1.8 2005: DPG Annual Meeting . . . . . . . . . . . . . . . . . . . . . . . . . . 82

7.2 Colloquia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 837.3 Seminars . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 907.4 CCSS Seminar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91

8 Services 958.1 Journals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95

8.1.1 European Physical Journal B: Condensed Matter and Complex Systems . 968.1.2 ACS – Advances in Complex Systems . . . . . . . . . . . . . . . . . . . . 99

8.2 Boards and Program Committees . . . . . . . . . . . . . . . . . . . . . . . . . . . 1028.3 Reviews . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1038.4 Software . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104

8.4.1 Cuttlefish: Dynamic Network Visualization . . . . . . . . . . . . . . . . . 1048.4.2 Working paper series . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105

1 Our Team

1.1 People

This report should start by acknowledging ourmost valuable resource: the collaborators whohave worked, or are still working, in our teamover the last five years.

Building up a team rests on variouspremises which cannot be taken for granted.First of all, it needs a considerable budget tohire personal – and in this respect successfulgrant applications add to the ETH endowmentof the chair, which is indispensable and greatlyappreciated. But finding the right people isstill another challenge. In October 2005 westarted with only one team member (in addi-tion to the professor), today we count between15 to 20 people in our team (depending on thecounting method). Our current team is a re-ally interdisciplinary one: we have group mem-bers with a background in computer science,mathematics, physics, economics, engineering.But it is also an international team with peo-ple from Switzerland, Germany, France, Italy,Spain, Romania, Argentina, Brazil. A list ofthose who worked with us during the past fiveyears can be found below:

Professor

Frank Schweitzer (10/2004–present)

Senior Assistant

Battiston, Stefano, Dr. (08/2009–present)

PostDocs

Battiston, Stefano, Dr. (07/2005–07/2009)Feiler, Matthias, Dr. (08/2006–08/2007)Fent, Thomas, Dr. (02/2005–01/2006)Geipel, Markus M., Dr. (10/2009–present)

Hogan, Patrick, Dr. (10/2008–09/2009)König, Michael D., Dr. (04/2008–present)von Ledebur, Sidonia, Dr. (08/2009–present)Lorenz, Jan, Dr. (04/2007–10/2009)Pich, Christian, Dr. (08/2009–present)Press, Kerstin, Dr. (04/2006–01/2008)Napoletano, Mauro, Dr. (04/2006–12/2007)Sousa, Adriano, Dr. (06/2005–12/2005)Tessone, Claudio J., Dr. (03/2007–present)Walter, Frank E., Dr. (10/2009–present)

PhD Students

Baumann, Christiane (05/2005–12/2005)Birbaumer, Mirko (08/2008–present)Garcia, David (09/2009–present)Geipel, Markus Michael (06/2006–09/2009)Gerber, Daniel (12/2007–06/2008)Ghonghadze, Jaba (09/2008–03/2009)Glattfelder, James B. (11/2006–present)Groeber, Patrick (04/2006–08/2009)Knellwolf, Simon (01/2009–05/2009)König, Michael D. (09/2005–02/2008)Mach, Robert (10/2004–05/2006)Perony, Nicolas (08/2008–present)Rodrigues, João (07/2005–12/2006)Stark, Hans-Ulrich (06/2005–08/2008)Tanase, Dorian Mihai (05/2008–present)Tasca, Paolo (01/2009–present)Vitali, Stefania (12/2006–present)Walter, Frank Edward (11/2005–09/2009)Yildirim, Mahir (03/2006–04/2007)

Office Administration

Dulik, Rahel (08/2007–present)Siegfried, Carole (11/2004–02/2007)Weis, Bettina (01/2007–07/2007)

6 1. Our Team

System Administration

Brünggel, Nils (07/2006–07/2008)Dreker, Patrick (03/2005–08/2006)Gasser, Michael (11/2009–present)Kis, Rita (01/2007–08/2009)Malhotra, Siddharth (04/2009–present)

Student Assistants

Beer, Stephane (10/2006–08/2007)Bucher, Hilmar (09/2007–08/2009)Bürkler, Nicolas (04/2005–07/2006)Burkhard, Oliver (07/2008–present)Calderara, Mauro (02/2008–02/2009)Forster, Manuel (11/2005–08/2006)Garcia, David (11/2008–08/2009)

Grüter, Andreas (11/2005–04/2006)Keller, Mirco (10/2008–present)Keller, Stefanie (09/2007–05/2008)Ledergerber, Christian (04/2005–07/2005)Mavrodiev, Pavlin (10/2008–present)Muff, Stephanie (10/2007 -09/2008)Radosavljevic, Marko (04/2005- 07/2005)Storni, Remo (10/2006–07/2007)Thomas, Spyridon (02/2008–02/2009)Walther, Georg (10/2008–10/2009)Wirthmüller, Alexander (11/2005- 02/2006)Zhang, Lin (03/2009–present)Zanetti, Marcelo (07/2008–present)

Trainees

Graf, Nadine (08/2009–present)

1.2. Theses 7

1.2 Theses

Dissertations

1. J. Emeterio Navarro Barrientos: Adaptive Investment Strategies for Different Scenarios

• Dissertation Humboldt University Berlin, Defense: 10/2008

• Supervisor/Examiner: Prof. Hans-Dieter Burkhard, Supervisor/Co-Examiner: Prof.Frank Schweitzer, Co-Examiner: Prof. Kai Nagel

2. Michael D. König: Dynamic R&D Networks. The Efficiency and Evolution of InterfirmCollaboration Networks

• Dissertation ETH No. 18182, Defense: 01/2009

• Supervisor/Examiner: Prof. Frank Schweitzer, Co-Examiner: Prof. Francis Bloch

• The thesis was awarded the Zurich Dissertation Prize.

3. Frank E. Walter: Designing Mechanisms for Trust-based Interaction in Social Networks


• Supervisor/Examiner: Prof. Frank Schweitzer, Co-Examiner: Prof. Elgar Fleisch

4. Patrick Christian Groeber: An Interdisciplinary Approach to the Emergence and Enforce-ment of Norms of Coordination and Cooperation


• Supervisor/Examiner: Prof. Frank Schweitzer, Co-Examiner: Prof. Dirk Helbing

5. Markus Michael Geipel: Dynamics of Communities and Code in Open Source Software


• Supervisor/Examiner: Prof. Frank Schweitzer, Co-Examiner: Prof. Georg von Krogh

Diploma Theses

1. Victor Garcia Palencia: Brownian Agents and Bacterial Motility

• Department of Physics, ETH Zurich

• Supervisor: Prof. Frank Schweitzer, date of completion: 07/2008

2. Nils Brünggel: Web Crawler for Recommender Systems

• Lucerne University of Applied Sciences and Arts (Advisor: Roland Portman)


Master Theses

1. Mahir Yildirim: Towards a Unified Framework for Recommender Systems

• Department of Computer Sciences, University of Freiburg, Germany

• Supervisor: Prof. Frank Schweitzer, Frank E. Walter, Dr. Stefano Battiston

• date of completion: 02/2007

2. Stefan Frei: Vulnerability Management

8 1. Our Team

• Department of Management, Technology, and Economics


3. Remo Sandro Storni: A simple social vs economical capital trade-off model

• Department of Mathematics, ETH Zurich


Bachelor Theses

1. Krishna Murthy: Modeling viral marketing through the voter model and polya process

• Department of Mechanical and Process Engineering, ETH Zurich


Semester Theses

1. Nicolas Bürkler: Dynamics of Companies: The Evolution of its Research

• Department of Mechanical and Process Engineering, ETH Zurich


1.3. IT Infrastructure 9

1.3 IT Infrastructure

The Chair of System Design has established a sophisticated IT infrastructure which extends theservices offered by ETH. At a glance, this includes workstations for our staff, workstations forour student assistants and guests, different dedicated servers (including a database server, fileserver and web server) and a computer cluster. In addition to hardware equipment, we also havea well defined and configured software infrastructure, including custom made applications andscripts.

Standard clients All people working at the Chair of Systems Design have what we call a“standard workplace” – a set of common hard- and software equipment:

1. In terms of hardware, this consists mainly of a laptop and an external screen. The clientshave access to our local printers, among which there is a multi functional laser printer.

2. In terms of software, this consists of a standard Linux distribution (Ubuntu Linux)with a set of commonly used programs such as a web browser (Firefox), an email client(Thunderbird), or a text editor pre-installed and pre-configured.

3. We are using LATEX as document markup language and our editor of choice is emacs. OurChair uses a set of templates for our presentations, letters and papers.

4. The client workstations are pre-equipped with a set of scripts used during the daily activ-ities: backup, connection to server, VPN connection, etc., as well as scientific software(R, Matlab, Mathematica, etc.).

Groupware Our Chair is by definition a collaborative environment. In order to have a propercollaboration both internally and with external partners, SG implemented the following collab-orative management tools:

1. Our main server hosts a number of shared common directories, accessible by our staff.Along the common folders (like administrative, research, teaching, IT, etc.), each user hasa private one for storage and backup.

2. Another service needed for teamwork is a document versioning software. For this pur-pose, our Chair uses SVN for our internal as well as external collaboration.

3. The Ebibtex server is an internally developed application used for reference management,allowing us to collaboratively manage references for projects and papers.

4. The internal calendar is used for marking important events which take place in our Chair(such as meetings, visits, absences, important academic events, etc.).

5. We also use a Wiki which acts like an internal Knowledge Base and in which users con-tribute useful information, varying from tips and tricks to reviews of lectures and confer-ences.

6. In order to have a more efficient work-flow when it comes to solving IT related issues, wedeveloped a Trouble Ticket management system, a platform in which users can opentickets about their problem and track the progress.

10 1. Our Team

Figure 1.1: Groupware applications

Server infrastructure We are operating an all-purpose server (which runs a file server, a webserver, and a number of other services), a dedicated database server and several other servers usedfor individual projects or as a backup. Our server rack and all the cluster nodes are physicallylocated in an air-conditioned room in our building. All our servers are backed up every nightand the data is stored externally, on ETH infrastructure.

1. The all-purpose server (IBM which runs on openSUSE Linux) is a multi-processormachine which has redundant storage system (RAID) and generous storage capabilities.The server hosts our common and private folders, as well as a series of commonly usedservices: our internal calendar, Ebibtex, internal Wiki, our Trouble Ticket system, etc.

2. Our main database server is running MySQL and it stores data used mainly in simula-tions and data analysis. Currently, the MySQL server serves few billion records, distributedin project specific databases. Among those is “ORBIS”, a database that contains up to dateinformation about firms and their ownership relations.

3. SG owns and uses a document management system (DMS) called Archivista, a hardwareand software solution designed to create and manage digital documents. The Archivistasolution allows digitalizing printed documents (using the provided fast scanner), performsOCR and indexing and allows easy document location and retrieval.

Figure 1.2: Servers and cluster infrastructure

1.3. IT Infrastructure 11

Computer cluster Our chair implemented its own cluster to provide the necessary computationpower for extensive processes such as data analysis and simulations.

1. Our cluster consists of 13 computers: one is acting as a gateway and the other 12 areproviding a total of 40 cores available for computations. They boot from the network;their file system is located on our all-purpose server. While such a setup is more complicatedinitially, it makes maintenance much easier – when we would like to install additionalapplications, we only have to do this once and these applications immediately becomeavailable to all nodes. The gateway acts as a terminal to the cluster; all jobs have tobe launched from there. The cluster itself is, for security reasons, located in a privatesub-network only reachable through the gateway.

2. The jobs are managed by MOSIX, an on-line management system targeted for high per-formance computing on Linux clusters. The operating system is openSUSE Linux.

SG’s contribution Several developments by the Chair of System Design are also publicly avail-able for the benefit of the community. Most notable contributions are:

1. Cuttlefish – an open source graph visualizer (see 8.4.1);

2. ETH Letter – LATEX templates for formal letters adopted ETH-wide;

3. Nokia VPN – ETH specific tutorial for using the VPN on Nokia devices.

12 1. Our Team

2 Research Topics

Research Overview

What kind of research do we do at the Chairof Systems Design? ‘Systems Design’ has var-ious meanings, for example, in computer sci-ence, where it refers to the architecture of com-puter systems. Engineering sciences also havetheir own version of systems design with partic-ular emphasis on product development. Noneof these topics is really at the heart of our re-search agenda, although we cover some issuesrelated to systems engineering in our courses.

Being part of the Department of Manage-ment, Technology, and Economics (D-MTEC)of ETH Zurich, the main focus of our researchis on social and economic systems, for exam-ple, online communities with a large number ofusers, networks of firms and banks, or organi-zations in general. We are interested in a fun-damental understanding of their structure anddynamics, to mitigate important societal prob-lems, such as systemic risk or inefficient (sub-optimal) solutions resulting from the dilemmabetween individual and collective utility max-imization. Can we ‘design’ the structure anddynamics of socio-economic systems such thata desired outcome is obtained, for example, co-operation is enhanced, or knowledge is sharedin a more efficient way? This implies that weshould know more about the structure and thedynamics within these systems and about theway they are built up by their constituents,grow and adapt to changing outside conditions.

Our methodological framework to addressthese ‘big issues’ is given by the theory of com-plex systems, which has been developed overthe last 40 years in different scientific disci-plines, such as statistical physics, evolutionarybiology, micro economics, and computational

sciences, before it merged into a commonly ac-cepted approach to investigate systems at large.Complex systems are comprised of a large num-ber of strongly interacting subsystems – enti-ties, processes, or so-called ‘agents’. Their in-teraction results in the emergence of systemicfeatures which cannot be inferred from theproperties of the agents. Thus, the fundamen-tal challenge of complex systems theory is therelation between the properties of the agentsand their interaction on the ‘micro’ level as wellas the properties of the system as a whole onthe ‘macro’ level. A useful theoretical perspec-tive to formalize these questions is provided bythe ‘Complex Network’ approach, where agentsare represented by nodes, and their interactionsare represented by links. Both agents and linksmay change over time, often on different timescales. Hence, the dynamics of the system canbe seen as a coevolution of the agents and theirnetwork.

The complex network approach allows usto utilize a wealth of methods recently devel-oped in different areas, to investigate for ex-ample biological or social networks. However,it is important to note that our research startsfrom identifying the socio-economic questionsrather than proceeding from methods and tools.Empirical research plays a crucial role in ourresearch agenda, in particular the analysis oflarge data sets about firm ownership, patentsharing, user ratings, or animal societies. Onlythe insights we obtain about the structure andthe dynamics of these specific social or eco-nomic systems allow us to specify formal mod-els of interacting agents to be simulated, ormathematically analyzed.

14 2. Research Topics

2.1 Economic Networks

Economic networks indeed challenge the estab-lished concepts of investigating complex net-works. The common approach assumes anundirected, unweighted network for which thetopological structure is identified and aggre-gated measures, such as modularity or clus-tering coefficient, are calculated. In contrast,economic networks are directed and weightednetworks, changing over time. Links representspecific economic relations, such as ownership,lending or borrowing money, exchanging knowl-edge, which are usually directional, and theweight of the link results from the quantity ex-changed. Moreover, the nodes of the network,i.e. the economic agents, are not equal, theyrepresent for example firms or investors of dif-ferent size, capital stock, etc., which in turnmay affect their role in the network. Links be-tween such agents are not established randomly,but based on a strategic decision. Eventually,in large networks new systemic features emerge,most notably systemic risk which exceeds thelocally bounded view of an agent.

In order to address these challenges, our re-search on economic network encompasses twodifferent strands. On one hand, we start fromthe macroscopic perspective of large economicnetworks to identify their structure with respectto weighted nodes and weighted, directed links

– for example in ownership relations. On theother hand, we start from the microscopic per-spective of ‘individual’ agents which try to in-crease their private utility resulting e.g. fromknowledge exchange by forming or deletinglinks to other agents. The latter allows us tounderstand the resulting structure e.g. of R&D(research and development) networks bottomup, while the former tells us more about thereal structure of economic networks and the keyplayers therein. This way, our approaches com-bine both an in-depth analysis of large data setsand formal modeling of network dynamics in amathematical framework.

IFI

FRANKLIN RES.

STATE STREETUNICREDITO

CR.SUISSE

PRUDENTIAL FIN.

DEUTSCHE BANK

KEYCORP

INTESA-SANPAOLO

BARCLAYSSUMITOMO MITSUI

GENERALI

MERRILL LYNCH

CITIGROUP

SANTANDER

LLOYDS TSB

BNP PARIBAS

ABERDEEN

NOMURA

GOLDMAN SACHS

MEDIOBANCA

BANK OF AMERICA

UBS

MITSUBISHI UFJ

MORGAN STANLEY

CAPITAL GROUP

BEAR STEARNS

ING

GEN.ELECTRIC

SUMITOMO

FMR CORP

ROYAL BANK SCOTLAND

FIDELITY MNG

WELLINGTON MNG

HSBC

COMMERZBANK

JPMORGAN CHASE

BANK NOVA SCOTIA

FRIENDS PROVIDENT

SOC.GENERALE

HBOS

Figure 2.1: Subset of an international financial net-work. Nodes represent major financial institutions,links the strongest existing relations, node colorsdifferent geographic areas.

Selected Publications:

• Schweitzer, Frank, Fagiolo, Giorgio, Sornette, Didier, Vega-Redondo, Fernando, White, DouglasR.: Economic Networks: What do we know and what do we need to know?, ACS – Advances inComplex Systems, vol. 12, no. 4/5 (2009), pp. 407-422.

• Schweitzer, Frank, Fagiolo, Giorgio, Sornette, Didier, Vega-Redondo, Fernando, Vespignani, Alessan-dro, White, Douglas R.: Economic Networks: The New Challenges, Science, vol. 325 (2009), pp.422–425.

• König, Michael David, Battiston, Stefano: From Graph Theory to Models of Economic Networks.A Tutorial., in: Networks, Topology and Dynamics, Theory and Applications to Economics andSocial Systems, Series: Lecture Notes in Economics and Mathematical Systems, vol. 613, (Eds.Naimzada, Ahmad K.; Stefani, Silvana; Torriero, Anna), Springer, (2008), pp. 26–63.

2.1. Economic Networks 15

2.1.1 Systemic Risk

Systemic Risk denotes the risk that a whole sys-tem, consisting of many interacting elements,fails. This is not only a problem in financialsystems but also in social systems (e.g., spread-ing of an epidemic disease), in technical sys-tems (e.g., blackout of power grids) or in mate-rials (e.g., rupture of a bundle of fibers). Thesesystems may exhibit a switch to collective fail-ure which is reminiscent of phase transitions inphysical systems. The question is how such amacroscopic state may emerge from local in-teractions of the subunits, given some specificboundary conditions. The fraction X of failedelements can be regarded as a measure of sys-temic risk. How can one predict the value of Xover time or asymptotically?

µ

σ

0 0.2 0.4 0.6 0.8 1

0.2

0.4

0.6

0.8

1

Figure 2.2: Example of a phase diagram for sys-temic risk . X is coded in color (where white means0 and black means 1, i.e. total failure).

In order to solve this question we devel-oped a general framework for models of cascadeand contagion processes on networks. Nodes inthe network are characterized by a net fragility,which is the difference between its fragilityand the threshold that determines its failure.If their net fragility grows above zero nodesfail, which increases the fragility of neighbor-

ing nodes, thus possibly triggering a cascade.In our generalized framework, we identify threemodel classes which differ in their microscopicinteraction mechanism and cover most phenom-ena of collective breakdown. For each modelclass, a phase diagram could be derived to showthe critical conditions where small changes inthe initial conditions lead to global failure. Weare also able generalize our framework to en-compass stochastic contagion models. This in-dicates the potential for further applications.

In addition to this more general approach tosystemic risk, we focus on specific mechanismsthat increase systemic risk in financial systems.One such mechanism is financial distress prop-agation, which is captured in an abstract modelof trend reinforcing of stochastic shocks. Onemay argue that reducing the volatility of suchshocks may prevent nodes from going bankrupt,however, this also reduces their possibility tobreak negative trends. Therefore, an intermedi-ate volatility, which is related to a certain opti-mal diversification in the fragility of the nodes,is able to reduce systemic risk.

On the more applied side, we also developspecific models of systemic risk in firm-firm,firm-bank and bank-bank networks. Currently,we are working on models of networks of insti-tutions connected by over-the-counter deriva-tive contracts. The empirical analysis of suchnetworks started as an SNF project in October2009 and aims at validating the models by usingtime series data from balance-sheets and finan-cial statements. In particular, we investigateto what extent financial acceleration as wellas interdependence among financial fragility ofagents can be detected in real data.


• Lorenz, Jan, Battiston, Stefano, Schweitzer, Frank: Systemic Risk in a Unifying Framework forCascading Processes on Networks, European Physical Journal B vol. 71, no. 4 (2009), pp. 441-460.

• Lorenz, Jan, Battiston, Stefano: Systemic risk in a network fragility model analyzed with proba-bility density evolution of persistent random walks, Networks and Heterogeneous Media, vol. 3,no. 2, June (2008), pp. 185-200.

• Battiston, Stefano, Delli Gatti, Domenico, Gallegati, Mauro, Greenwald, Bruce, Stiglitz, Joseph E.:Credit chains and bankruptcy propagation in production networks, Journal of Economic Dynamicsand Control, vol. 31, no. 6 (2007), pp. 2061-2084.

• Battiston, Stefano, Delli Gatti, Domenico, Gallegati, Mauro, Greenwald, Bruce, Stiglitz, JosephE.: Liaisons Dangeureuses: Risk Diversification and Systemic Risk, (working paper 2009).


2.1.2 Ownership Networks

The empirical analysis of real-world complexnetworks has revealed unsuspected regularitiessuch as scaling laws which are robust acrossmany domains. Tools and concepts from statis-tical physics have been crucial for the achieve-ment of these findings, especially when the re-lationships (links) among the agents (nodes)create interdependency in the system. As anexample, firms cannot exist in isolation andthey interact with other firms through supply-costumers relations, but also partnership, own-ership and other interdependencies. Often how-ever, such interactions between economic actorsare not considered in standard economic anal-ysis.

Figure 2.3: Subset of some ownership relationsamong major international financial institutions. Afew links are highlighted to better depict the exis-tence of mutual cross-shareholdings and longer cy-cles of ownership relations.

The study of such relations can uncover im-portant patterns in the structural organizationof economic networks: the power held by eachsingle economic actor, the level of competitionin the markets, the diffusion of systemic cri-sis, as well as the emergence of statistical prop-erties observed at a macroscopic scale. Con-sidering, for example, the world-wide owner-ship network, it is possible to estimate the con-

trol held by each economic actor by taking allthe paths among the nodes into account, i.e.,all the direct and indirect ownership relations.On an international level control is found tobe highly concentrated in a small tightly-knitcore of mainly Anglo-Saxon financial institu-tions, which collectively also hold shares in eachother (see Fig. 2.3). This core can be seen as aneconomic “super-entity”, raising issues for eco-nomic policies in a global market. Indeed, manyresults only become observable by employingnetwork analysis tools. As an example, thewell known observation that local ownership iswidely dispersed among many shareholders inAnglo-Saxon markets, takes a counter-intuitiveturn if analyzed on a global level: from a bird’s-eye perspective, the ownership ends up lying inthe hands of very few important shareholders.

From a complex networks perspective, wedo not only consider the existence of relation-ships, but also their direction, weights and, ifpossible, a proxy for the value or importanceof the nodes. Furthermore, we embed theseeconomic networks in geographical space look-ing for regularities concerning the localizationof the economic agents. Our study shows thatthe agents prefer to establish a large numberof ownership relations with other companies lo-calized in the same geographical area, whilethe connections between different regions aremainly due to financial institutions. In an-other work, the inter-regional network of di-rect investment stocks was analyzed. Whileoutward investments and activity of firms wasfound to be power-law distributed with similarexponents, for regions these quantities are dis-tributed log-normally, implying that regions areless heterogeneous than firms and countries.


• Vitali, Stefania, Glattfelder, James B., Battiston, Stefano: The Network of Global CorporateControl, (submitted).

• Glattfelder, James B., Battiston, Stefano: Backbone of Complex Networks of Corporations: TheFlow of Control, Physical Review E 80 (2009).

• Battiston, Stefano, Rodrigues Joao Frederico, Zeytinoglu, Hamza: The Network of Inter-RegionalDirect Investment Stocks across Europe, ACS - Advances in Complex Systems, vol. 10, no. 1(2007), pp. 29–51.

2.1. Economic Networks 17

2.1.3 Knowledge Transfer

As a result of the growing complexity in thetechnologies used for production and innova-tion, firms have to rely on knowledge transferfrom other firms through R&D collaborations.However, the formation of R&D collaborationsand their impact on the economy are not suffi-ciently understood, which limits the possibilityto design policies to increase their efficiency andcompetitiveness in a rapidly changing globaleconomy.

To better understand the evolution of R&Dnetworks, we have developed a new modelingframework of firms exchanging knowledge inR&D partnerships. By means of mathemati-cal analysis, we examine the efficiency of givennetwork structures in terms of maximizing ag-gregate profits in the economy. We show thatthe efficient network structure depends on themarginal cost of collaboration and the volatil-ity of the technological environment. When themarginal cost is low, the complete graph is effi-cient. However, high marginal cost implies thatthe efficient network is sparser and has a core-periphery structure. An illustration for an in-dustry populated by 10 firms can be seen inFigure 2.4.

Further, by means of computer simulationswe examine the evolution of the network struc-ture under alternative scenarios, assuming thatthe decision on collaborating partners is decen-tralized. First, we assume that the formation oflinks follows a dynamic process in which firmsare stochastically selected to revise their col-laboration strategies. In these dynamics, firmsdecide to form a link if the link did not ex-ist before and the link is beneficial to both ofthem, and decide to delete a link if the linkexisted before and deletion is beneficial to at

least one of them. We show the existence ofmultiple equilibrium structures, most of thembeing inefficient. This is due to the path depen-dent character of the partner selection process,the presence of knowledge externalities and thepresence of severance costs involved in link dele-tion. We study the properties of the emergingequilibrium networks and we show that they arecoherent with the stylized facts of R&D net-works.

0.5 0.65 0.75 0.85c

F10,10 = K10 F10,9 F10,8 F10,7

Figure 2.4: Efficient networks in terms of maximiz-ing aggregate industry output for different values ofthe link formation cost in an industry comprised ofn = 10 firms.

Second, we analyze a dynamic link forma-tion process in which alliances are formed be-tween firms with high knowledge levels (tech-nological leaders) while the volatile environ-ment introduces interruptions in the connec-tions between firms leading to the breakdown ofsome collaborations. The volatile environmentcauses existing links to decay where those linksthat firms value less than others are more likelyto be affect by the decay. We show analyticallythat there exist stationary networks and thattheir topological properties match with thoseof empirical R&D networks. In particular, wefind a strong core-periphery structure observedin empirical studies. We show that the relativesize of the core compared to the periphery crit-ically depends on the linking opportunities offirms and the strength of the link decay.


• König, Michael D., Battiston, Stefano, Schweitzer, Frank: Modelling Evolving Innovation Net-works, Innovation Networks – New Approaches in Modelling and Analyzing (Eds. A. Pyka, A.Scharnhorst), Berlin: Springer (2009), pp. 187-267.

• König, Michael D., Battiston, Stefano, Napoletano, Mauro, Schweitzer, Frank: On Algebraic GraphTheory and the Dynamics of Innovation Networks, Networks and Heterogeneous Media, vol. 3, no.2 (2008), pp. 201-219.

• König, Michael D., Battiston, Stefano, Napoletano, Mauro, Schweitzer, Frank: RecombinantKnowledge and the Evolution of Innovation Networks, Journal of Economic Behavior and Or-ganization (2009, submitted).


2.2 Social Organizations

Research on social organizations encompassesa broad range of topics as shown in the follow-ing. Emphasis is again on the emergence ofsystemic properties out of the interaction of so-cial agents. For example, in opinion dynamicswe investigate the problem how a macroscopicordered state of the system, consensus, is ob-tained from the local communication of agentswith different opinions. Likewise, the emer-gence of social norms out of the heterogeneousbehavior of agents should be explained. A bet-ter understanding of such dynamics opens thepossibility to design interaction mechanismsthat, for example, favor a specific desired out-come, such as cooperation or trust.

While some of our projects related to socialorganizations deal with rather formal and ab-stract models, for example in opinion dynamics,others are more closely related to real applica-tions, e.g. the design of recommender systems,and even deal with large datasets, for exam-ple about user ratings or the social structureof animal societies. This requires to develop orto adapt a multitude of different methodologiesand tools, ranging from multi-agent computersimulations to database scripts on one hand,and from mathematical proofs to algorithm de-sign on the other hand.

Even if the research problems presentedseem to be quite diverse on the first glimpse,in most cases they not only share a commonmethodology, they also approach, from differentsides, some central questions about the trade-off between robustness and adaptivity in so-cial organizations. While robustness seems tobe a desired feature for social organizations, itmay limit the possibility of such an organizationto adapt quickly to a changing environment or

to internal challenges. Successful organizationsare both able to maintain a coherent structurethat ensures their functionality and to adapt toinnovations or threads instantaneously. In or-der to reveal the principles behind this, we haveto rely on data, but not to narrow our perspec-tive too much. That’s why we decided, for ex-ample, to work onOpen Source Software (OSS):we are able to obtain data not only about thestructure of various OSS projects – the ‘prod-ucts’, but also about the communication be-tween developers and users – the ‘producers’.We can thus trace how changes propagate inthe system, on both levels, how robustness ismaintained while allowing for gradual or radi-cal improvements and adoption of innovations,and how the success of a product eventuallyis connected to the dynamics of the communityof ‘producers’. In a similar manner, insight intothe structure of animal societies allows us to re-veal some ‘hidden’ principles of a problem solv-ing organization which maintains cooperationover generations.

Figure 2.5: Snapshot of a network of managers inSwiss companies.

2.2. Social Organizations 19

2.2.1 Opinion Dynamics and Emergence of Norms

Assuming that agents have different opinionsabout a given subject, or a different behavior,how does a common opinion, or a social normcoordinating their behavior, emerges? Withreference to our methodological framework out-lined in Sect. 2, we argue that these systemicproperties may emerge from the nonlinear in-teractions of the agents, bottom up. In orderto understand these relations, we focus on somerather abstract multi-agent models, which nev-ertheless capture essential features of a socialinteraction.

One of these is herding behavior, or imi-tation; another one, in particular in the pres-ence of social networks, is social homophily, i.e.agents which are more similar in their behavioralso tend to interact more often. The questionthen is under which conditions we observe col-lective states which are characterized by justone opinion, or norm, i.e. consensus, or statescharacterized by the, possibly non-stationary,coexistence of different opinions, or norms. Toanswer these question, we utilize two differentmodel classes: (i) models of binary opinions,such as the voter model, (ii) models of contin-uous opinions, such as the bounded confidencemodel.

Figure 2.6: Time to reach consensus Tκ for theVoter Model in presence of reluctance µ Symbolsshow fully connected systems with different systemsizes, lines represent an analytical prediction, filledsymbols indicate the values of Tκ in absence of re-luctance.

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Figure 2.7: Convergence toward consensus overtime. Trajectories of closed-minded agents are inblack, of open-minded in red.

The voter model focuses particularly on theimitation effect, i.e. agents adopt the opinion oftheir acquaintances, but nonlinear voter mod-els also consider that agents can vote againstthe trend or become reluctant to adopt a newopinion if they have the current one for a longtime. The investigation of these possible non-linearities revealed a number of counterintuitiveresults on the systemic level. For example, onewould assume that a ‘microscopic’ reluctanceon the agent level may considerably slow downthe time to reach consensus on the system’slevel. We find, however, that slowing-downthe microscopic dynamics can in fact acceleratethe macroscopic ordering dynamics in certainranges of the reluctance. This holds regardlessof the network topology of the interaction. Aswe could point out, the dynamics near equilib-rium states is governed by two competing pro-cesses, which stabilize either the majority orthe minority states. If the first one dominates,it accelerates the ordering of the system.

Individual reluctance is just one example ofthe heterogeneity of social agents. In anothermodel, we consider a diversity in the individualpreference to follow either the opinion of theirneighbors (herding) or an external opinion (‘ad-vertising’). The question then is under whatcondition the multi-agent system adopts the ad-vertised opinion best. Counter-intuitively, this


happens for some intermediate level of hetero-geneity, rather than for cases where the indi-viduals are identical or too diverse – a phe-nomenon called “diversity-induced resonance”.Consequently, heterogeneity may enhance theemergence of consensus.

In the model class of continuous opinions,we investigate the conditions for reaching con-sensus or polarization, depending on the open-mindedness of the agents, i.e. their ability to in-teract with agents having far different opinions(‘bounded confidence’). Here, the assumptionis that agents, if they interact, also adjust theiropinion to the mean. Dependent on the confi-dence interval, the model shows a sharp transi-tion between a consensus state and states char-acterized by the coexistence of different opin-ions shared by groups of agents. These resultsbecome more interesting, again, if heterogene-ity in the agents open mindedness is consid-ered. Then, consensus can be reached even incases where a ‘homogeneous’ society would failto reach consensus.

Such abstract model find an application inexplaining, for example, the emergence of a ‘lo-cal culture’, i.e. a common business practice,among firms. In addition to the opinion dy-namics, we consider an effect on the social net-

work, i.e. the agent’s in-group consisting of ac-quainted firms forces them to also stay suffi-ciently close to them. Since the in-group net-work structure changes over time, there exists afeedback mechanism of agent behavior and in-group structure. Studying its consequences bymeans of agent-based computer simulations, wefind the somehow counterintuitive result thatfor narrow-minded agents the feedback mecha-nism helps find consensus more often, whereasfor open-minded agents this does not necessar-ily hold.

Figure 2.8: In-group influence allows agents to in-teract even if their respective opinions are too dif-ferent. This enhances the emergence of consensus


• Lorenz, Jan: Heterogeneous bounds of confidence: Meet, Discuss and Find Consensus!, Complexity(2009, forthcoming).

• Groeber, Patrick, Schweitzer, Frank, Press, Kerstin: How Groups Can Foster Consensus: The Caseof Local Cultures, Journal of Artificial Societies and Social Simulation vol. 12, no. 2, (2009).

• Schweitzer, Frank, Behera, Laxmidhar: Nonlinear Voter Models: The Transition from Invasion toCoexistence, European Physical Journal B vol. 67, no. 3 (2009), pp. 301-318.

• Stark, Hans-Ulrich, Tessone, Claudio J., Schweitzer, Frank: Slower is faster: Fostering consensusformation by heterogeneous interia, ACS - Advances in Complex Systems, vol. 11 no. 4 (2008),pp. 87-116.

• Stark, Hans-Ulrich, Tessone, Claudio J., Schweitzer Frank: Decelerating microdynamics can ac-celerate macrodynamics in the voter model, Physical Review Letters, vol. 101, no. 1 (2008), pp.018701(4).

• Tessone, Claudio J., Toral, Raúl: Diversity-induced resonance in a model for opinion formation,European Physical Journal B, vol. 71, no. 4 (2009) pp. 549-556.

• Urbig, Diemo, Lorenz, Jan, Herzberg, Heiko: Opinion Dynamics: the Effect of the Number of PeersMet at Once, Journal of Artificial Societies and Social Simulation, vol. 11, no. 2 (2008).

• Lorenz, Jan: Continuous Opinion Dynamics under Bounded Confidence: A Survey, in: Interna-tional Journal of Modern Physics C, vol. 18, no. 12 (2007), pp. 1819-1838.

• Fent, Thomas, Groeber, Patrick, Schweitzer, Frank: Coexistence of Social Norms based on In- andOut-group Interactions, ACS - Advances in Complex Systems, vol. 10, no. 2 (2007), pp. 271-286.


2.2.2 Trust-based Networks

Over the last decade, the Internet has beenchanging fundamentally. Ten years ago, it wasa medium through which people searched for in-formation, bought products, or communicatedwith friends. Today, the Internet is a mediumon which people provide information, sell prod-ucts, and make friends. Social interaction liesat the core of all of these actions. The goal ofour research is to design mechanisms for trust-based social interactions among people in anon-line environment which achieve a trade-offbetween individual utility and social efficiency.

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Figure 2.9: Social network of agents and trust build-up in a trust-based network: there are two profiles,red and blue, indicated by the cores of the node.Only square nodes are cooperating; e.g., nodes 2,3, and 4 are not cooperating. In a few steps, thenodes learn which other nodes to trust.

The main theme in this project on trust-based networks is to look at the social networkand the trust relationships between the people

in communities on the Internet. Our hypoth-esis is that, by incorporating knowledge aboutwho trusts whom to which extent, it is possi-ble to better personalize a vast range of servicesprovided on the Internet.

In our work, we developed a model of atrust-based recommender system on a socialnetwork whose main idea is that users can usetheir social network to reach information andtheir trust relationships to filter the informa-tion to separate the relevant and the irrelevant.We then proposed a novel trust metric for socialnetworks – we refer to it as the TrustWebRankmetric. It is personalized and dynamic, and al-lows us to compute the indirect trust betweentwo users which are not neighbors based on thedirect trust between users that are neighbors.Figure 2.9 illustrates how the TrustWebRankmetric can be used to develop a trust-basednetwork. Linking the model of a trust-basedrecommender system and the metric to real-ity, we statistically analyzed a dataset on a realInternet community, Epinions, and empiricallyvalidated the claims made by testing the perfor-mance of a recommender system using our ideason this dataset. Finally, we showed that theseresults on webs of trust are universal and simi-lar principles can be applied to more than justtrust-based recommender systems: in our case,we applied our ideas to a scenario of coalitionformation among users in the setting of groupbuying in an electronic marketplace, e.g. on theInternet.


• Yildirim, Mahir, Walter, Frank Edward, Battiston, Stefano, Schweitzer, Frank: Moving Recom-mender Systems from On-line Commerce to Retail Stores (2009, submitted).

• Walter, Frank Edward, Battiston, Stefano, Schweitzer, Frank: Personalised and Dynamic Trust inSocial Networks, (2009, accepted at Recommender Systems).

• Walter, Frank Edward, Battiston, Stefano, Schweitzer, Frank: Coping with Information Overloadthrough Trust-based Networks, in: Managing Complexity: Insights, concepts, Applications (Eds.Helbing, D.) Springer, Berlin (2008), pp. 273–300.

• Walter, Frank Edward, Battiston, Stefano, Schweitzer, Frank: A Model of a Trust-Based Recom-mendation System on a Social Network, Journal of Autonomous Agents and Multi-Agent Systems,vol. 16, no. 1 (2008), pp. 57–74.


2.2.3 Structure and Dynamics of Open Source Software

Open Source Software breaks with many estab-lished assumptions on the nature of softwareproduction and management. In this project,the phenomenon of Open Source is analyzedfrom a multi-disciplinary perspective, includingsuch diverse fields as statistical physics, com-puter science and management science. Theaim is to analyze the social interaction in aproject, to understand the architectural changein the software, and to ultimately combine thisknowledge to paint a holistic picture of OpenSource development.

Our approach is highly data driven: Weuse data about the evolution of code depen-dencies in more than 10 projects. Figure 2.10,for instance, shows such an evolution for theJUNG project. Furthermore, we collected de-tailed information about forum communicationand code change of 100 SourceForge projects.

2003 2004 2007

Figure 2.10: Evolution of the dependency betweenJava classes in the Jung project in the years 2003to 2007.

This unprecedented database led to several

results: (i) Concerning the community dynam-ics, we found that, interaction between devel-opers and users is high throughout the 100projects; that communication concentrates onhighly active individuals, and that turnoverand segregation between users and developersis only slightly correlated, which may allow tobenefit from high turnover rates, while keepingnegative effects small. (ii) Analyzing the de-pendency structure and the change records of35 Java projects we found that in conflict withprevailing scholarly opinion, dependencies areunreliable as a predictor of the change dynam-ics in the architecture. The role of dependenciesis more complex and ambivalent than previousresearch suggests. (iii) Examining the evolu-tion of the dependencies we found that existingmathematical models of software evolution ig-nore several key characteristics clearly evidentin the data. For instance, newly added nodesin the dependency network are endowed withan initial degree drawn from a power-law dis-tribution. Furthermore, in many projects thedensity of the dependency network grows mono-tonically.

We argue that these results may eventuallyhelp developers to steer the software towards fa-vorable architectures and enable new manage-ment principles or tools for smoothening the in-terface between software, developers, and users.


• Geipel, Markus Michael, Press, Kerstin, Schweitzer, Frank: Communication in Innovation Com-munities: An Analysis of 100 Open Source Projects, (submitted to Research Policy 2009).

• Geipel, Markus Michael, Tessone, Claudio J., Schweitzer Frank: A complementary view on thegrowth of directory trees, European Physical Journal B vol. 71, no. 4 (2009), pp. 641–648.

• Geipel, Markus Michael, Schweitzer, Frank: (2009), Software Change Dynamics: Empirical Evi-dence from 35 Java Projects, Proceedings of the European Software Engineering Conference 2009,ESEC/FSE, pp. 269–272.

• Markus Michael Geipel, (2009) Dynamics of Communities and Code in Open Source Software,Doctoral thesis ETH #18480.


2.2.4 Empirics of User Ratings and Herding Behavior

Nowadays, almost everything on the world wideweb can be user-rated or user-reviewed. Whatis the impact of the published data on the ratingbehavior? What are optimal number of stars?Does rating-click-fraud happen? Can it be de-tected? The vast amount of on-line data givesthe opportunity to study the rating and review-ing process empirically.

In a first project, we studied the Internetplatform Epinions, where people can share re-views about products and services. Our datasetcontains over a million reviews. Interestingly,the number of reviews per user follows a veryskewed distribution: About 69% of the userscontributed at least one review (contributors),whereas 31% did not even contribute one review(free-riders). Almost one and a half thousandusers wrote 100 to 500 reviews and more thanone hundred even wrote over 500 reviews. Eachrating for a product is on a scale from 1 to 5,where 1 is the worst and 5 is the best. Theaverage product rating is almost 4 out of 5– abias which is commonly observed on such ratingplatforms and needs to be explained.

In a second project, we studied the ratinghistograms of popular movies from the InternetMovie Database. It turned out that the his-tograms have particular shapes which can bequantified by a one-parameter fit. If an ex-pressed opinion is an average of different factorsthe shape of the histogram should be Gaussiandue to the central limit theorem, which could beverified for the central bin. But there are peaksat the extremal bins which can be explained bya discretization effect.

These insights reveal some regularitieswhich exist in public opinion formation and canbe also observed in other social examples, suchas public polls. As we could verify for the case

of the Swiss national elections, these polls alsoshow a remarkable ‘universality’, i.e. a com-mon distribution of votes (i.e. sympathy) forcandidates within each political party.

Individual donations are another importantexample, where this kind of universality is em-pirically observed. Looking into the time se-ries of donations received by a Swiss and twoGerman donor organizations before and afterthe tsunami disaster of 2004, we find a power-law behavior for the distributions of donationswith similar exponents for different countriesas well as before and after the tsunami, whichaccounts for some kind of universal behaviorin donations independent of the actual event.The time-dependent change of both the num-ber and the total amount of donations afterthe tsunami follows a logistic growth equation,which is known to describe an epidemic-likeprocess. It captures the herding behavior indonations as a non-local interaction between in-dividuals via a time-dependent mean field rep-resenting the mass media. A more recent studyrelates these findings to universality classes inhuman response waiting time distributions.

Figure 2.11: Fraction of the total number of do-nations (inset: relative growth of amount of dona-tions) over time, after the disaster.


• Crane, Riley, Schweitzer, Frank, Sornette, Didier: New power law signature of media exposure inhuman response waiting time distributions (2009, submitted).

• Walter, Frank Edward: An investigation of Epinions.com: Statistical properties and empiricalvalidation of recommender system algorithms (2009, working paper).

• Lorenz, Jan: Universality of movie rating distributions, European Physical Journal B, vol. 71, no.2 (2009), pp. 251–258.

• Schweitzer, Frank, Mach, Robert: The Epidemics of Donations: Logistic Growth and Power Laws,PLoS ONE vol. 3, no. 1 (2008), e1458.


2.2.5 Collective Emotions

Different from opinions, emotions are short-living and hard to control. Nevertheless, if theycan be transmitted, for example involving massmedia or the internet, individual emotions canbe synchronized, amplified and eventually re-sult in a collective emotional state, which maytrigger irrational behavior.

We are interested in modeling the emer-gence of these collective emotions out of theinteraction between agents, for example inter-net users. Our research touches a number ofproblems on which little is known yet: How areemotions expressed in a non-face-to-face com-munication? Can they be detected from verbalcommunication? How are emotion transmittedin technical media? What are the conditions tosynchronize individual emotions? How can thespread of collective emotions be controlled?

In order to answer these questions, we col-laborate with experts in psychology, data min-ing and artificial intelligence. In particular,we need to know better about the commu-nication between users. At our chair, wehave analyzed BBC forum data and classifiedthree different kinds of conversation structures:dialogue-based threads between two users, nor-mal threads with heterogeneous user participa-tion and contributions of many different users.Examples of these can be observed in Figure2.12. We also found that an increase in thenumber of links in a community network con-sisting of different groups can hinder the syn-chronization in emotions and opinions betweenthese groups.

In a second step, we focus on the role of

emotions in on-line communities rating prod-ucts and firms. For example, when analyz-ing reviews from the internet movie databaseIMDB.com, we found support for the hypoth-esis that movie ratings follow a some generalbasic principles regarding their bias.

Insights from these empirical analysis mayallow us to develop an agent-based model thatexplains the dynamics of ratings, based on emo-tional feedback and the network of users. In thelong term, we aim at a multi-agent model of thetransmission of emotions applicable to blog de-bates, newsgroups, etc. which may reproducestylized facts on emotions in groups observedempirically.

Figure 2.12: Three types of threads: dialog-based,normal conversation and user attractor.


• Shin, Jae Kyun, Lorenz, Jan: Tipping Diffusivity in Information Accumulation Systems: MoreLinks, less Consensus. (Working paper)

• Lorenz, Jan: Universality of movie rating distributions, The European Physical Journal B, 71(2009), pp. 251–258


2.2.6 Social Structure of Animal Societies

In order to reveal the hidden complexity of so-cial interaction, it would be desirable to have asmuch data as possible about individual encoun-ters, such as time series about contacts betweenany two agents, their duration, the kinship orfriendship between agents, the spatial coordi-nates of their meetings, etc. Such data ex-ists, e.g. from customers of mobile phone com-panies or from conference participants taggedwith RFID chips, but usually they are not pub-licly available and provide only rather shorttime series.

Therefore, it is a good idea to use dataobtained from animal societies instead of hu-man societies. Again, there is the underlyingmethodological assumption that it is the in-teraction between individuals rather than theirspecific attributes that generates social com-plexity. Hence, knowing about the evolutionof the social network of mammals such as dol-phins, elephants, mice, or bats allows some in-sights into the common principles of group for-mation, adaptation to changing environmentalconditions, problem solving, or origins of coop-eration. It also allows to tackle the general re-search problem of how network structure influ-ences individual behavior and how this, in turn,influences the network dynamics, and eventu-ally the behavior at the population level.

In close collaboration with biologists fromthe University of Zurich, we investigate differ-ent large scale data sets about the evolution ofgroup structures in mice and in bats. In thecase of bats, the data encompasses about 20years, which allows us to study the role of kin-ship and generation changes on the long-termstructure of the community, but also the roleof memory of individuals in the formation ofgroups between seasons and in the formation ofwhole colonies. For these typical fission-fusionsocieties group formation is highly dynamicsand composition changes frequently as mem-bers of groups split (fission) or join (fusion).The data allows us to investigate the differentlevels of hierarchies in such social organizations,to address their robustness and their adaptiv-

ity. It also allows to identify key individuals insuch a society and to explore the role of ‘lead-ers’ and ‘followers’ in maintaining a coherentsocial structure.

In the case of mice, we have the possibil-ity to analyze data which records all encoun-ters of individuals entering or leaving differentnests. This allows to reconstruct a weighted,evolving network of social interactions, wheremice follow different ‘strategies’ of exploration,cooperation, or segregation. This data can bemerged with information about the pedigree be-tween individuals, and their ‘success’ measuredin terms of reproduction rates.

A first, but important step in both projectsis to reveal the structure of the social net-work, which means to calculate different net-work metrics changing over time. These in-sight may allow to quantify the robustness andadaptivity of such social organizations and toinfer the evolutionary advantages of their exis-tence. As a second step, we want to use theseinformation to set up a multi-agent simulationwhere agents form their social relations basedon species specific optimization criteria. A cal-ibration of these computer models eventuallymay allow us to reproduce the stylized facts ob-served in the different animal societies. On aneven more generalized level, we hope to iden-tify general principles in the formation of socialorganizations and to link these to their func-tionality and evolutionary success.

Figure 2.13: Community structure observed in thenetwork of a large bat colony.


2.3 Further Research Topics

2.3.1 Agent-based Models of Socio-economic Dynamics

In addition to specific models of social inter-actions, we also investigate classes of abstractmodels, e.g. multiplicative stochastic processes,where the dynamics is governed by randomforces rather than by specific strategies. Takefor example some agents which invest part oftheir budget in the stock market, with the hopeof some positive return on investment (RoI).If we do not know the market dynamics thatdetermines the RoI, we may describe it by arandom function with some given properties, tofind out how agents perform in such an uncer-tain environment. This defines a reference case,to compare the outcome of more refined strate-gic decisions with. In most cases, we are ableto derive some mathematical results, to whichagent-based computer simulations can refer. Ifthe market signal is not completely random,by gradually increasing the complexity of theagent’s strategy, we are able to determine howmuch information is optimal to reach a goodperformance, in the long run.

Other such generic models are related tocoalition formation, which is usually seen as theresult of negotiations and strategic decisions.In order to evaluate the outcome, it should becompared to the reference cases, where onlyrandom interactions together with very simpleutility maximization strategies are taken into

account.

For the latter example, we also have an ap-plication in mind: a phenomenon called “groupbuying” – buyers joining groups, or coalitions,to bundle their purchasing power towards sell-ers. In our model, agents may decide to buyproducts individually, join an existing coalition,or start creating a new one. Their decision de-pend on their product preferences, but also onthe critical size for a successful coalition andhence on the risk that such a coalition fails.Dependent on these conditions, we find scenar-ios where individual buyers dominate, scenarioswhere small groups form, and scenarios wherea giant coalition dominates. The performance,measured in terms of the utility of the agents,can be improved if agents use their trust re-lationships in order to determine who to formcoalitions with.

t = tbeginning t = tintermediate t = tequilibrium

Figure 2.14: Different stages of a coalition forma-tion process following a trust-based approach.


• Eguiluz, Victor, Tessone, Claudio Juan: Critical behavior in an evolutionary model of altruisticbehavior with social structure, Advances in Complex Systems vol. 12, no. 2 (2009), pp. 221-232.

• Geipel, Markus Michael, Weiss, Gerhard: A generic framework for argumentation-based negotia-tion, in: Cooperative Information Agents Xl, no. 11 in Lecture Notes in Computer Science (Eds.Klusch, M., Hindriks, K., Papazoglou, M. P., Sterling, L.) vol. 4676, Berlin/Heidelberg, Springer(2007), pp. 209-223

• Navarro, Emeterio, Walter, Frank Edward, Schweitzer, Frank: Risk-Seeking versus Risk-AvoidingInvestments in Noisy Periodic Environments, in: International Journal of Modern Physics C vol.19, no. 6 (2008), pp. 971-994.

• Navarro, Emeterio, Cantero, Ruben, Rodrigues, João, Schweitzer, Frank: Investments in RandomEnvironments, in: Physica A, vol. 387, no. 8-9 (2008), pp. 2035-2046.

• Walter, Frank Edward, Battiston, Stefano, Schweitzer, Frank: Emergence and Evolution of Coali-tions in Buyer-Seller Networks, in: Emergent Intelligence of Networked Agents, Studies in Compu-tational Intelligence (Eds. Namatame, A., Kurihara, S., Nakashima, H) vol. 56, Springer, Berlin(2007), pp. 245-258.

• Walter, Frank Edward: Trust as the Basis of Coalition Formation in Electronic Marketplaces,submitted (2009).

2.3. Further Research Topics 27

2.3.2 Structure and Dynamics of Complex Networks

Obviously, complex networks are pervasivestructures that arise also on biological systems.One example is the Tree of Life which resultsfrom diversification processes during biologicalevolution. A phylogenetic tree is a subset ofthe Tree of Life in which each node representsa diversification event, connected by branches(links). Every node in the tree can be consid-ered the initial point of another one. Allomet-ric scaling measures how the cumulative branchsize C for each node scales with respect to itsbranch size A (see Figure 2.15). We have exam-ined the topological properties of a large set ofinter-specific and intra-specific phylogenies andhave shown that the branching patterns followallometric rules conserved across the differentlevels in the Tree of Life, all departing fromthose expected from the standard null models.Moreover, we have found similar allometric ex-ponents characterizing the scaling properties ofintra- and inter-specific phylogenies.

Figure 2.15: (left) Example of calculation of thesub-tree size and cumulative sub-tree sizes for twodifferent trees. (right) Plot of sub-tree size A, andcumulative branch size C, for the inter-specific andintra-specific data sets. The line represents the bestfit to a power law C ∼ Aη, with η = 1.44. The insetillustrates the small dispersion found in the values.

In many cases, however, the network evolu-tion is inherently coupled with the dynamics ofthe agents. For example, we have studied a net-work of coupled excitable agents under externalperturbations. Studying the influence of repul-sive interactions on this system we found, sur-prisingly, that a moderate repulsion can triggera global synchronized excitation (firing) of theensemble. This is suppressed in large systemsif the network of repulsive interactions is fullyrandom, i.e. the degree distribution becomeshomogeneous.

Another generic model to address the co-evolution between the network dynamics andthe dynamics of the agents is used to describethe prebiotic evolution of chemical species thatcatalyze each others replication. On a fasttime scale, each of these species (agents) followsa dynamics based on catalytic support fromother agents, whereas on a much slower timescale the catalytic network evolves through se-lection and mutation of its nodes (agent). Itis known that the performance of such a sys-tem vastly improves if by chance some cy-cles (closed catalytic loops) appear, which havesome analogy to indirect reciprocity. However,the core formed by these cooperating agentsbreaks down if all agents belong to the core.Despite these crashes, the system recovers, sothat the average evolution can be describedby a growth regime followed by a saturationregime for which we obtain a logarithmic scal-ing. Noteworthy, these investigations were thestarting point for our recent work on R&D net-works, where agents also develop some coop-eration to boost their knowledge growth, albeitnot by chance, but based on strategic decisions,


• Schweitzer, Frank: Multi-Agent Approach to the Self-Organization of Networks, in: Understandingand Managing Complex Agent-Based Dynamical Networks (Eds. F. Reed-Tsochas, N. F. Johnson,J. Efstathiou), Singapore: World Scientific (2009, forthcoming).

• Tessone, Claudio J., Zanette, Damián H., Toral, Raúl: Global firing induced by network disorderin ensembles of active rotators. European Physical Journal B, vol. 62, no. 3 (2008), pp. 319-326.

• Herrada, E.A.; Tessone, Claudio J., Klemm, K., Eguíluz, V.M., Hernandez-García, E., Duarte,C.M. Universal Scaling in the Branching of the Tree of Life, PLoS ONE, vol. 3, no. 7 (2008),e2757.

• Seufert, Adrian, Schweitzer, Frank: Aggregate Dynamics in an Evolutionary Network Model, In-ternational Journal of Modern Physics C, vol. 18, no. 10 (2007), pp. 1659-1674.


2.3.3 Geographical Localization and Clustering

The analysis of firms’ location has attracted theattention of economists for a very long time.After the pioneering work of Alfred Marshalin the early 19th century, the existence of dis-tricts has been justified by advantages fromco-location due to agglomeration externalities.More recently, a relevant body of theoreticaland empirical research in the “New EconomicGeography” literature has aimed at explainingwhat might be considered the basic stylized factof economic geography, i.e. that firms lookmuch more clustered in space than any theoryof comparative advantage would predict. Be-side these theoretical contributions, a good dealof empirical research has investigated localiza-tion patterns in manufacturing industries.

0.3 0.35 0.4 0.45 0.5 0.55 0.6 0.650.3

0.35

0.4

0.45

0.5

0.55

0.6

Share of localized sectors, E&G Index (2−sigma rule)

S

hare

of l

ocal

ized

sec

tors

, D&

O In

dex

Belgium

France

ItalyUK

Spain

Germany

Figure 2.16: Share of localized sectors according tothe Ellison and Glaeser index (x-axis) vs. shareof localized sector according to the Duranton andOverman index (y-axis).

In this line of research, we have simulta-

neously investigated the industry localizationin several EU countries by exploiting a firmsdatabase homogeneous across European coun-tries and employing different measure of ag-glomeration. Conforming to the previous lit-erature, we find that localization is a perva-sive phenomenon in all countries studied, how-ever, in each country, the degree of localiza-tion is very unevenly distributed across sec-tors. Moreover, there are significant differencesin the strength of localization according to themethodology employed (see Fig. 2.16).

The existence and success of these non-random spatial concentrations of firms in oneor several related industries, also called districtsor clusters, is usually justified by the benefitsconveyed to co-located firms as compared toisolated firms. Indeed, such clusters developa local culture that acts as a form of gover-nance. Geographic proximity facilitates thisprocess, since actors in the same area alreadyshare a set of formal institutions such as legis-lation as well as general informal institutions.However, as technological and economic envi-ronment change, these clusters may be unableto adapt and become obsolete. We find thatcollective local cultures can be relatively stablefor a limited environment volatility. Moreover,flexible specialization clusters benefit in adjust-ment if product complexity is limited and firmsnumber are greater. Nevertheless, these bene-fits come at the expenses of greater failure riskfor individual companies.


• Vitali, Stefania, Napoletano, Mauro, Fagiolo, Giorgio: Spatial Localisation in Manufacturing: ACross-Country Analysis, LEM Working Paper, April 2009; OFCE Document de Travail, July 2009;Papers in Evolutionary Economic Geography, #09.06.

• Press, Kerstin: Divide to conquer? Limits to the adaptability of disintegrated, flexible specializa-tion clusters, Journal of Economic Geography, vol. 8, no. 4 (2008), pp. 565-580.

• Press, Kerstin: When does defection pay? The stability of institutional arrangements in clusters,Journal of Economic Interaction and Coordination, vol. 2, no. 1 (2007), pp. 67-84.

2.3. Further Research Topics 29

2.3.4 Agent-Based Models of Biological Motion

The modeling of biological motion is not at theheart of our research agenda, although FrankSchweitzer has published on this topic beforehe started to work at ETH. Emphasis was onthe development of a modeling framework usingBrownian Agents, which was already applied tothe motion of different biological species, e.g.ants, or bacteria. Thus, our recent engagementis only on singular topics from this vast fieldand builds on his previous experience by ap-plying a set of tools and methods already de-veloped.

Our research focuses on two different ar-eas: (i) the motion of individual entities (or‘agents’), such as cells, bacteria, or even intra-cellular organelles, and (ii) the collective mo-tion of many of these agents. One example isthe vortex swarming of daphnia (tiny water fliescycling in the same direction) which is seen asan emergent phenomenon resulting from theirmutual interaction, such as attraction or repul-sion. Our aim is to develop multi-agent mod-els which reproduce the individual or collectivepatterns of motion observed, but also allow forcalibration against experimental data. Conse-quently, this involves the collaboration with ex-perimental biologists.

Examples of such collective patters areshown in Figure 2.17. They result fromthe spatial organization and compartmental-ization of intracellular organelles and vesicles(small membrane-enclosed ‘bubbles’ that storeor transport substances within a cell). Theseentities are equipped with motor proteins whichallow them to actively move along intracellular

structures such as microtubules and actin fil-aments. An intracellular organelle can switchintermittently between at least four distincttransport states. In order to model these, vesi-cles are described by Brownian agents that arecharacterized by a set of internal degrees offreedom accounting for the different transportstates. The biological details of each cell, suchas cell outline, nucleus, microtubules and actinfilaments form the boundary conditions for thevesicles movement within the cell. Simulationsof vesicle transport enable us to investigate theresulting steady states of the multi-agent modeland compare them with the experimentally ob-served vesicle patterns.

Figure 2.17: Four distinct vesicle patterns resultingfrom different treatments of the cells.


• Victor Garcia Palencia: Brownian Agents and Bacterial Motility, Diploma Thesis, Department ofPhysics, ETH Zurich, Supervisor: Prof. Frank Schweitzer, date of completion: 07/2008.

• Mach, Robert, Schweitzer, Frank: Modeling Vortex Swarming In Daphnia, Bulletin of Mathemat-ical Biology, vol. 69 (2007), pp. 539-562.

3 Publications

Publications are probably the most importantindicator of successful research activities – al-though not the only one. They could be judgedin several ways, by quantity (sheer number ofpapers), by quality (rating of the journal), byimpact (number of citations obtained). None ofthese figures provides an objective insight in theachievements of the past five years. Because westarted with a completely new research agendarather than continuing previous activities, itis obvious that in early years the publicationswere quite sparse. It took some time before thefirst results were obtained, and even longer be-fore they were developed towards a level thatmatches the quality standards of top journals.

On another note, interdisciplinary research can-not be easily placed in major journals whichsometimes have a quite narrow focus. In thisrespect, we first had to adapt to the standardsof different scientific communities, ranging fromeconomics and the social sciences to computerscience and statistical physics.

As the list of publications below demon-strates, there is a broad spectrum of papers,mostly in reviewed journals and proceedingsof competitive conferences, which encompassesour diverse research topics. The increasingnumber of publication shows that we are ad-vancing in most of these areas.

Submitted/Forthcoming

62. Vitali, Stefania, Glattfelder, James B., Battiston, Stefano: The Network of Global Corpo-rate Control (2009, submitted)

61. Geipel, Markus Michael, Press, Kerstin, Schweitzer, Frank: Communication in InnovationCommunities: An Analysis of 100 Open Source Projects, Research Policy (2009, submitted)

60. Yildirim, Mahir, Walter, Frank Edward, Battiston, Stefano, Schweitzer, Frank: MovingRecommender Systems from On-line Commerce to Retail Stores (2009, submitted)

59. König, Michael D., Tessone, Claudio J., Zenou, Yves: A Dynamic Model of Network For-mation with Strategic Interactions, Econometrica (2009, submitted)

58. König, Michael D., Battiston, Stefano, Napoletano, Mauro, Schweitzer, Frank: Recombi-nant Knowledge and the Evolution of Innovation Networks, Journal of Economic Behaviorand Organization (2009, submitted)

57. Vitali, Stefania, Napoletano, Mauro, Fagiolo, Giorgio: Spatial localisation in manufactur-ing: A cross-country analysis (2009, submitted)

56. König, Michael D., Battiston, Stefano, Napoletano, Mauro, Schweitzer, Frank: The Effi-ciency and Stability of R&D Networks, Games and Economic Behavior (2009, resubmitted)

55. Glattfelder, James B., Dupuis, A., Olsen, R.B.: An extensive set of scaling laws and theFX coastline. (2008, forthcoming)

54. Lorenz, Jan: Heterogeneous bounds of confidence: Meet, Discuss and Find Consensus!, in:Complexity (2009, forthcoming)

53. Groeber, Patrick, Rauhut Heiko: Does the Confidence in a Moral World reinforce normativebehavior?, Computational and Mathematical Organization Theory (2009, forthcoming)

32 3. Publications

52. Schweitzer, Frank: Multi-Agent Approach to the Self-Organization of Networks, in: Un-derstanding and Managing Complex Agent-Based Dynamical Networks (Eds. F. Reed-Tsochas, N. F. Johnson, J. Efstathiou), Singapore: World Scientific (2009, forthcoming)

51. Walter, Frank Edward, Battiston, Stefano, Schweitzer, Frank: Personalised and DynamicTrust in Social Networks, (2009, accepted at Recommender Systems)

Published50. Glattfelder, James B., Battiston, Stefano: The backbone of complex networks of corpora-

tions: Who is controlling whom?, in: Physical Review E vol. 80, no. 3 (2009) 036104

49. Szolnoki, Attila, Perc, Matjaz, Szabó György, Stark, Hans-Ulrich: Impact of aging on theevolution of cooperation in the spatial prisoner‘s dilemma game, in: Physical Review Evol. 80, no. 2 (2009) 021901

48. Geipel, Markus Michael, Schweitzer, Frank: (2009), Software Change Dynamics: EmpiricalEvidence from 35 Java Projects, in: Proceedings of the European Software EngineeringConference 2009, ESEC/FSE, pp. 269–272.

47. Tessone, Claudio J., Toral, Raul: Diversity-induced resonance in a model for opinion for-mation, in: European Physical Journal B vol. 71, no. 4 (2009), pp. 549-555

46. Geipel, Markus Michael, Tessone, Claudio J., Schweitzer Frank: A complementary view onthe growth of directory trees, in: European Physical Journal B vol. 71, no. 4 (2009), pp.641–648

45. Lorenz, Jan, Battiston, Stefano, Schweitzer, Frank: Systemic Risk in a Unifying Frameworkfor Cascading Processes on Networks, in: European Physical Journal B, vol. 71, no. 4(2009), pp. 441–460

44. Schweitzer, Frank, Fagiolo, Giorgio, Sornette, Didier, Vega-Redondo, Fernando, White,Douglas R.: Economic Networks: What do we know and what do we need to know?, in:ACS – Advances in Complex Systems, vol. 12, nos. 4/5 (2009), pp. 407–422

43. Schweitzer, Frank, Fagiolo, Giorgio, Sornette, Didier, Vega-Redondo, Fernando, Vespig-nani, Alessandro, White, Douglas R.: Economic Networks: The New Challenges, in: Sci-ence, vol 325 (2009), pp. 422–425

42. Lorenz, Jan: Universality of movie rating distributions, in: European Physical Journal Bvol. 71, no. 2 (2009), pp. 251–258

41. König, Michael David, Battiston, Stefano, Schweitzer Frank: Modeling Evolving InnovationNetworks, in: Innovation Networks - New Approaches in Modeling and Analyzing (Eds.A. Pyka, A. Scharnhorst), Berlin: Springer (2009), pp. 187–267.

40. Eguíluz, Víctor, Tessone, Claudio Juan: Critical behavior in an evolutionary model ofaltruistic behavior with social structure, in: ACS - Advances in Complex Systems vol. 12,no. 2, (2009), pp. 221–232

39. Groeber, Patrick, Schweitzer, Frank, Press, Kerstin: How Groups Can Foster Consensus:The Case of Local Cultures, in: Journal of Artificial Societies and Social Simulation vol.12, no. 2 (2009) 4

38. Schweitzer, Frank, Behera, Laxmidhar: Nonlinear Voter Models: The Transition fromInvasion to Coexistence, in: European Physical Journal B vol. 67, no. 3 (2009), pp.301–318.

37. Stark, Hans-Ulrich, Tessone, Claudio J., Schweitzer, Frank: Slower is faster: Fosteringconsensus formation by heterogeneous interia, in: ACS – Advances in Complex Systems,vol. 11 no. 4 (2008), pp. 87–116.

33

36. Urbig, Diemo, Lorenz, Jan, Herzberg, Heiko: Opinion Dynamics: the Effect of the Numberof Peers Met at Once , in: Journal of Artificial Societies and Social Simulation, vol. 11,no. 2 (2008) 4.

35. Navarro, Emeterio, Walter, Frank Edward, Schweitzer, Frank: Risk-Seeking versus Risk-Avoiding Investments in Noisy Periodic Environments, in: International Journal of ModernPhysics C vol. 19, no. 6, June (2008), pp. 971–994.

34. König, Michael David, Battiston, Stefano: From Graph Theory to Models of EconomicNetworks. A Tutorial., in: Networks, Topology and Dynamics, Theory and Applicationsto Economics and Social Systems, Series: Lecture Notes in Economics and MathematicalSystems, vol. 613, (Eds. Naimzada, Ahmad K.; Stefani, Silvana; Torriero, Anna), Springer,(2008), pp. 23–63.

33. Cencini, Massimo, Tessone, Claudio J., Torcini, Alessandro, Chaotic synchronization ofspatially extended systems as non-equilibrium phase transitions, in: Chaos, vol. 18, no. 3,pp. 037125 (2008).

32. König, Michael David, Battiston, Stefano, Napoletano, Mauro, Schweitzer Frank: On Al-gebraic Graph Theory and the Dynamics of Innovation Networks , in: Networks and Het-erogeneous Media, vol. 3, no. 2 (2008), pp. 201–219.

31. Lorenz, Jan, Battiston, Stefano: Systemic risk in a network fragility model analyzed withprobability density evolution of persistent random walks, in: Networks and HeterogeneousMedia, vol. 3, no. 2 (2008), pp. 185–200.

30. Stark, Hans-Ulrich, Tessone, Claudio J., Schweitzer Frank: Decelerating microdynamicscan accelerate macrodynamics in the voter model, in: Physical Review Letters, vol. 101,no. 1 (2008), 018701(4).

29. Tessone, Claudio J., Zanette, Damián H., Toral, Raúl: Global firing induced by networkdisorder in ensembles of active rotators. in: European Physical Journal B, vol. 62, no. 3(2008), pp. 319–326.

28. Navarro, Emeterio, Cantero, Ruben, Rodrigues, Joao, Schweitzer, Frank: Investments inRandom Environments, in: Physica A, vol. 387, no. 8–9 (2008), pp. 2035–2046.

27. Stark, Hans-Ulrich, Helbing, Dirk, Schönhof, Martin, Holyst, Janusz A.: Alternating coop-eration strategies in a Route Choice Game: Theory, experiments and effects of a learningscenario, in: Games, Rationality and Behaviour (Eds. A. Innocenti, P. Sbriglia), NewYork: Palgrave Macmilllian, (2008), pp. 256–273.

26. Battiston, Stefano, Delli Gatti, Domenico, Gallegati: Trade Credit and Systemic Risk, in:Managing Complexity: Insights, Concepts, Applications (Ed. D. Helbing) Springer, Berlin(2008), pp. 213-239

25. Walter, Frank Edward, Battiston, Stefano, Schweitzer, Frank: Coping with InformationOverload through Trust-based Networks, in: Managing Complexity: Insights, Concepts,Applications (Ed. D. Helbing) Springer, Berlin (2008), pp. 273–300.

24. Schweitzer, Frank, Mach, Robert: The Epidemics of Donations: Logistic Growth and PowerLaws, in: PLoS ONE vol. 3, no. 1 (2008) e1458.

23. Walter, Frank Edward, Battiston, Stefano, Schweitzer, Frank: A Model of a Trust-BasedRecommendation System on a Social Network, in: Journal of Autonomous Agents andMulti-Agent Systems, vol. 16, no. 1 (2008), pp. 57–74.

22. Herrada, E.A.; Tessone, Claudio J., Klemm, K., Eguíluz, V.M., Hernandez-García, E.,Duarte, C.M. Universal Scaling in the Branching of the Tree of Life, in: PLoS ONE, vol.3, no. 7 (2008), e2757.

21. Fagiolo, Giorgio, Napoletano, Mauro, Roventini, Andrea: Are Output Growth-Rate Dis-tributions Fat-Tailed? Some Evidence from OECD Countries, in: Journal of AppliedEconometrics vol. 23, (2008), pp. 639–669.

34 3. Publications

20. Lorenz, Jan: Continuous Opinion Dynamics under Bounded Confidence: A Survey , in:International Journal of Modern Physics C, vol. 18, no. 12 (2007), pp. 1819–1838.

19. Seufert, Adrian, Schweitzer, Frank: Aggregate Dynamics in an Evolutionary NetworkModel, in: International Journal of Modern Physics C, vol. 18, no. 10 (2007), pp. 1659–1674.

18. Weisbuch, Gerard, Battiston, Stefano: From production networks to geographical eco-nomics, in: Journal of Economic Behaviour and Organization, vol. 64, no. 3–4 (2007), pp.448–469.

17. Geipel, Markus: Self-Organization applied to Dynamic Network Layout, in: InternationalJournal of Modern Physics C vol. 18, no. 10 (2007), pp. 1537–1549.

16. Geipel, Markus Michael, Weiss, Gerhard: A generic framework for argumentation-basednegotiation, in: Cooperative Information Agents Xl, no. 11 in Lecture Notes in Com-puter Science (Eds. Klusch, M., Hindriks, K., Papazoglou, M. P., Sterling, L.) vol. 4676,Berlin/Heidelberg, Springer (2007), pp. 209–223.

15. Russo, Alberto, Catalano, Michele, Gaffeo, Edoardo, Gallegati, Mauro, Napoletano, Mauro:Industrial dynamics, fiscal policy and R&D: Evidence from a computational experiment,in: Journal of Economic Behaviour and Organization, vol. 64, no. 3–4 (2007), pp. 426–447.

14. Walter, Frank Edward, Battiston, Stefano, Schweitzer, Frank: Emergence and Evolutionof Coalitions in Buyer-Seller Networks, in: Emergent Intelligence of Networked Agents,Studies in Computational Intelligence (Eds. Namatame, A., Kurihara, S., Nakashima, H)vol. 56, Springer, Berlin (2007), pp. 245–258.

13. Schweitzer, Frank: Collective Decisions in Multi-Agent Systems, in: Advancing SocialSimulation, The First World Congress (Eds. S. Takahashi, D. Sallach, J. Rouchier), Tokyo:Springer (2007), pp. 7–12.

12. Fagiolo, Giorgio, Napoletano, Mauro, Roventini, Andrea: How Do Output Growth RateDistributions Look Like? Some Time-Series Evidence on OECD Countries, in: EuropeanPhysical Journal B vol. 57, no. 57 (2007), pp. 205–211.

11. Press, Kerstin: Divide and conquer? The role of governance for the adaptability of In-dustrial Districts, in: ACS – Advances in Complex Systems, Special issue on ‘EmpiricalApplications of Complexity Theory in Economics and Geography’, vol. 10, no. 1 (2007),pp. 73–92.

10. Press, Kerstin: When does defection pay? On the stability of institutional arrangementsin clusters, in: Journal of Economic Interaction and Coordination, vol. 2, no. 1 (2007),pp. 67–84.

9. Battitson, Stefano, Delli Gatti, Domenico, Gallegati, Mauro, Greenwald, Bruce, Stiglitz,Joseph E.: Credit chains and bankruptcy propagation in production networks, in: Journalof Economic Dynamics and Control, vol. 31, no. 6 (2007), pp. 2061–2084.

8. Fent, Thomas, Groeber, Patrick, Schweitzer, Frank: Coexistence of Social Norms based onIn- and Out-group Interactions, in: ACS – Advances in Complex Systems, vol. 10, no. 2(2007), pp. 271–286.

7. Mach, Robert, Schweitzer, Frank: Modeling Vortex Swarming In Daphnia, in: Bulletin ofMathematical Biology, vol. 69 (2007), pp. 539–562.

6. Battiston, Stefano, Rodrigues Joao Frederico, Zeytinoglu, Hamza: The Network of Inter-Regional Direct Investment Stocks across Europe, in: ACS – Advances in Complex Systems,vol. 10, no. 1 (2007), pp. 29–51.

5. Napoletano, Mauro, Roventini, Andrea, Sapio, Sandro : Modelling smooth and unevencross-sectoral growth patterns: an identification problem, in: Economics Bulletin, vol. 15,no. 6 (2006) pp. 1–8.

35

4. Schweitzer, Frank, Silverberg, Gerald: Konkurrenz, Selektion und Innovation in ökonomis-chen Systemen, in: Irreversible Prozesse und Selbstorganisation (Hrsg. Th. Pöschel, H.Malchow, L. Schimansky-Geier), Berlin, Logos-Verlag (2006) pp. 361–373.

3. Aparicio Diaz, Belinda; Fent, Thomas: An Agent Based Simulation Model of Age-at-Marriage Norms. , in: Agent-Based Computational Modelling: Applications in Demog-raphy, Social, Economic and Environmental Sciences (Eds. F. C. Billari, T. Fent, A.Prskawetz, J. Scheffran), Berlin: Springer (2006) pp. 85–116.

2. Schweitzer, Frank, Mach, Robert, Mühlenbein, Heinz: Agents with heterogeneous strate-gies interacting in a spatial IPD, in: Nonlinear Dynamics and Heterogenous InteractingAgents (Eds. T. Lux, S. Reitz, and E. Samanidou), Lecture Notes in Economics andMathematical Systems, vol. 550, Berlin: Springer (2005) pp. 87–102.

1. Prskawetz, Alexia, Zagaglia, Barbara, Fent, Thomas, Skirbekk, Vegard: Decomposing thechange in labour force indicators over time, in: Demographic Research, vol. 13, no. 7(2005) pp. 163–188.

Working papers

• Battiston, Stefano, Delli Gatti, Domenico, Gallegati, Mauro, Greenwald, Bruce, Stiglitz,Joseph E.: Liaisons Dangeureuses: Risk Diversification and Systemic Risk

• Shin, Jae Kyun, Lorenz, Jan: Tipping Diffusivity in Information Accumulation Systems:More Links, less Consensus. (http://arxiv.org/abs/0909.1252)

• Groeber, Patrick, Schweitzer, Frank: A microfoundation of social influence in models ofopinion formation

• König, Michael David, Tessone, Claudio J.: The emergence of hierarchies in networks ofagents competing for high centrality

• Vitali, Stefania, Battiston, Stefano: Fragility spread in a network of regions• Lorenz, Jan, Lorenz, Dirk A.: On Conditions for convergence to consensus, (http://arxiv.org/abs/0803.2211)

36 3. Publications

4 Talks

In addition to publications, scientific presenta-tions are an important way of disseminating ourachievements. In fact, talks are a quite efficientway of promoting our research activities. In-vited talks, in particular, and talks selected forhighly competitive conferences reflect to whatextend our research is recognized by the scien-tific community.

Looking back on the previous years, we findthat the number of talks given has steadily in-creased. This indicates both, that we have moreresults to disseminate and that our contribu-

tions are increasingly recognized by the scien-tific community.

This list does not include presentationsgiven during internal research meeting. It isworth noticing that the range of talks encom-passes almost all of our research fields, and var-ious levels, from local seminar talks, to compet-itive conferences, to invited talks and keynotepresentations at major conferences. So, wecan see this as a good indicator that we havereached some public attention for our research.

2009166. Schweitzer, Frank: Predicting Systemic Risk: The role of contagion and cascades, Com-

plex’09 - The 9th Asia-Pacific Complex Systems Conference, Tokyo, Japan, November 4–7,2009 (invited talk)

165. von Ledebur, Sidonia: Patent collaboration of German inventors - did the distance increaseover time?, EAEPE Conference, Amsterdam, Netherlands, November 6–8, 2009

164. Walter, Frank E.: Personalised and Dynamic Trust in Social Networks, 3rd ACM Confer-ence on Recommender Systems, New York City, New York, USA, October 22–25, 2009

163. Vitali, Stefania: The Network of global corporate control. Research seinar. Colloquium ofCCSS Center of Competence, ETH Zurich, Zurich, Switzerland, October 20, 2009

162. Battiston, Stefano: Liaisons Dangeureuses: Individual Risk Diversification and SystemicRisk, International Workshop: Financial risk, market complexity and regulation, CollegiumBudapest, Budapest, Hungary, October 8–10, 2009

161. Schweitzer, Frank: Economic Networks: Mirco and Macro Perspectives, Symposium: Fron-tiers in Network Science, Berlin, Germany, September 28–30, 2009

160. Pich, Christian: More Flexible Radial Layout, Graph Drawing 2009, Chicago IL, USA,September 22–25, 2009

159. Pich, Christian: Drawing Directed Graphs Clockwise, Graph Drawing 2009, Chicago IL,USA, September 22–25, 2009

158. König, Michael: Games of Dynamic Network Formation, HSC Video Conference, UCSD,San Diego, CA, USA, September 21, 2009

157. Tessone, Claudio J.: Social network evolution based on agent centrality, European SocialSimulation Association Conference, University of Surrey, Guildford, UK, September 16,2009

156. Schweitzer, Frank: Mechanisms of systemic risk: Contagion, reinforcement, redistribution,Conference: Complexity, Mathematics and Socio-Economic Problems, Bielefeld, Germany,August 31 – September 12, 2009

38 4. Talks

155. Tessone, Claudio J.: From networks evolving by centrality to rank-based dynamics, Inter-national Centre For Theoretical Physics “Abdus Salam”, Trieste, Italy, August 21, 2009

154. Lorenz, Jan: A model of emotion dynamics in cyberspace, CyberEmotions Workshop,Jacobs University of Bremen, Germany, July 1, 2009

153. Battiston, Stefano: Risk Diversification and Systemic Risk, NetSci 2009 – InternationalWorkshop and Conference on Complex Networks and their Applications, Venice, Italy,June 29 – July 3, 2009

152. Vitali, Stefania: The Map of Global Corporate Control, NetSci 2009 - International Work-shop and Conference on Complex Networks and their Applications, Venice, Italy, June 29– July 3, 2009

151. Schweitzer, Frank: Economic Networks: Micro and Macro Perspectives, NetSci 2009 -International Workshop and Conference on Complex Networks and their Applications,Venice, Italy, June 29 – July 3, 2009

150. Lorenz, Jan: Continuous opinion dynamics and bounded confidence: A model to explainthe formation of parties, University of Oldenburg, Germany, June 30, 2009

149. Battiston, Stefano: Efficiency and Evolution of R&D Networks, DIME International Con-ference on the Formation of Social and Economic Networks. Paris, June 27, 2009

148. König, Michael: Games of Dynamic Network Formation, DIME International Conferenceon the Formation and the Evolution of Social and Economic Networks, Paris, France, June25, 2009

147. König, Michael: Dynamic Network Formation and Centrality Measures, Department ofComputer & Information Science, University of Konstanz, Konstanz, Germany, June 18,2009

146. Glattfelder, James B.: The Network of Global Corporate Control, International Workshop“Coping with Crises in Complex Socio-Economic Systems”, Zurich, Switzerland, June 8–12,2009

145. Battiston, Stefano: Risk Diversification and Systemic Risk, International Workshop “Cop-ing with Crises in Complex Socio-Economic Systems”, Zurich, Switzerland, June 8–12,2009

144. Schweitzer, Frank: Mechanisms of Systemic Risk: Contagion, Reinforcement, Redistribu-tion, NET 2009: Evolution of Complexity & COST MP0801 Meeting, Rome, Italy, May28–30, 2009

143. Lorenz, Jan: Universality in Continuous Opinion Dynamics: An Empirical Study of MovieRating Distributions, NET 2009: Evolution of Complexity & COST MP0801 Meeting,Rome, Italy, May 28 –30, 2009

142. Tasca, Paolo: Information Mirages and Systemic Risks in Financial Markets. ResearchSeminar. Colloquium of CCSS Center of Competence, ETH Zurich, Zurich, Switzerland,April 21, 2009

141. König, Michael: Games of Dynamic Network Formation, Research Institute of IndustrialEconomics (IFN), Stockholm, Sweden, March 24, 2009

140. Perony, Nicolas: From Animal Societies to Complex Systems: The case study of roost-ing associations in Bechstein’s bats, ETH Zurich Interaction Seminar for Ecology, Zurich,Switzerland, March 2, 2009

139. Schweitzer, Frank: Mechanisms of Systemic Risk: Contagion, Reinforcement, Redistribu-tion, Complex’2009 – The First International Conference on Complex Sciences: Theoryand Applications, Shanghai, China, February 23–25, 2009

138. Schweitzer, Frank: Modeling Collective Interactions in Social Systems, CyberemotionsKickoff Meeting, Warsaw, Poland, February 12-14, 2009

39

137. Tasca, Paolo: Exploring Information Mirages in a Simulated Multi-Agent Stock Market,X Intern. Workshop on Quantitative Finance, PoliMi, Milan, Italy, January 29–30, 2009

136. König, Michael: Efficiency and Stability of Dynamic Innovation Networks, North AmericanWinter Meeting of the Econometric Society, San Francisco, CA, USA, January 3–5, 2009

2008135. Lorenz, Jan: Mathematik und Schwarmintelligenz, pi x Deutschland - Die Abschlussver-

anstaltung im Jahr der Mathematik, EXPO XXI, Köln, Germany, December 11, 2008134. Tessone, Claudio J.: Network evolution induced by agents competing for high central-

ity, BCNet Workshop: Trends and perspectives in Complex Networks, Barcelona, Spain,December 10, 2008

133. Schweitzer, Frank: Nonlinear Voter Models: The Transition from Invasion to Coexistence,XVI Conference on Nonequilibrium Statistical Mechanics and Nonlinear Physics (ME-DYFINOL’08), Punta del Este, Uruguay, December 1–5, 2008

132. Schweitzer, Frank: Dynamics of Economic Networks, Conference: Physics Applied to Eco-nomics and Social Sciences (PAESS’08), Porto Alegre, Brazil, November 25–29, 2008

131. Battiston Stefano: Diversification of Risk and Systemic Risk. Research seminar. Collo-quium of CCSS Center of Competence, ETH Zurich, Zurich, Switzerland, November 11,2008

130. König, Michael: Dynamic R&D Networks, Research Seminar, Institute for Empirical Re-search in Economics, University of Zurich, Zurich, Switzerland, November 10, 2008

129. Battiston Stefano: Trust webs and social networks as a basis for the design of new forms ofcooperation in society and in the enterprise, Conference, University of Cagliari, Sardinia,Italy, November 6, 2008

128. Perony, Nicolas: Social Structures in Bechstein’s bats, Meeting of the Zoological Instituteof the University of Zurich, Wildhaus, Switzerland, October 31 – November 1, 2008

127. König, Michael: From Graph Theory to Innovation Networks, Lecture at the GREQAMSummer School 2008, The Economics of Knowledge: Innovation and Networks, Aix-enProvence, France, October 14-17, 2008

126. Schweitzer, Frank: Evolving social networks: The influence of costs benefits, and prefer-ences, 5th Conference on Applications of Social Network Analysis (ASNA 2008), Zurich,Switzerland, September 12–13, 2008

125. Schweitzer, Frank: Collective Dynamics of Companies - A Complex Systems Perspective,Part 1: Models of Company Growth, Part 2: Models of Company Interaction, SummerSchool: Non-Equilibrium Dynamics of Large-Scale Complex Systems, Ambleside, UK, Au-gust 30, – September 8, 2008

124. Vitali, Stefania: Localisation in Manufacturing: A Cross-Country Analysis, ERSA (TheEuropean Regional Science Association) Congress: Culture, Cohesion and Competitive-ness: Regional Perspectives, Liverpool, United Kingdom, August 27-31, 2008

123. Lorenz, Jan: Schwärmen für das Viertelfest - die Intelligenz der Knackinsekten, ein math-ematisches Massenselbstorganisationsexperiment auf dem Viertelfest, Bremen, Germany,August 22–24, 2008

122. Schweitzer, Frank: The costs of interaction - and how they shape the social network,International Workshop on Challenges and Visions in the Social Sciences, ETH Zurich,Zurich, Switzerland, August 18–23, 2008

121. Geipel, Markus: Community Cohesion and User Impact: The Case of Mid-Sized OpenSource Projects, Academy of Management Annual Meeting, Anaheim CA, USA, August12, 2008

40 4. Talks

120. Battiston Stefano: 2 lectures: Social Networks. Central European University (CEU)-Collegium Budapest (ColBud) Summer School on Complex Systems, Budapest, Hungary,July 10–14, 2008.

119. Battiston Stefano: 3 lectures: Financial Networks. IX Trento Summer School, CEELprogram in adaptive economic dynamics: Financial Instability and Crises. Trento. Italy,July 2–7, 2008.

118. Vitali, Stefania: Spatial Concentration in Manufacturing: A Cross-Country Analysis, 12thInternational Schumpeter Conference Society, Rio de Janeiro, Brazil, July 2–5, 2008

117. König, Michael: Efficiency and Evolution of R&D Networks, 14th International Conferenceon Computing in Economics and Finance, University of Sorbonne, Paris, France, June 29,2008

116. König, Michael: Agents Competing for High Centrality in a Volatile Environment, 14thInternational Conference on Computing in Economics and Finance, University of Sorbonne,Paris, France, June 28, 2008

115. Tessone, Claudio J.: Social Network Formation of Agents Competing for High Central-ity, International Conference on Economic Science with Heterogeneous Interacting Agents,Warsaw, Poland, June 21 2008

114. Geipel, Markus: Modularity, Dependence and Change, NK Modelling in Economics andManagement, Scuola Superiore S. Anna, Pisa, Italy, June 13, 2008

113. Tessone, Claudio J.: Chaotic synchronization in extended systems as out-of-equilibriumphase transitions, Network Synchronization: from dynamical systems to neuroscience, Lei-den, Netherlands, May 27, 2008

112. Lorenz, Jan: Continuous Opinion Dynamics, International Workshop “Sociophysics: Statusand Perspectives”, ISI Foundation, Torino, Italy, May 26, 2008

111. Schweitzer, Frank: Modellierung komplexer Systeme - Von der Physik zu den Sozialwis-senschaften, Senioren-Universität Zürich, University of Zurich, Zurich, Switzerland, May13, 2008

110. Walter, Frank Edward: Agent-based Models, Networks, and the Web 2.0, Seminar on theAdvanced Computational Modeling of Social Systems, ETH Zurich, Zurich, Switzerland,April 23, 2008

109. Battiston, Stefano: Connected or trapped? Firms and banks in credit networks. Publictalk. MMCOMNET Final Workshop, Said Business School, Oxford, UK, April 17, 2008

108. König, Michael: Efficiency and Stability of Dynamic Innovation Networks, Astute Mod-elling Seminar, CER-ETH - Center of Economic Research, Zurich, Switzerland, April 15,2008

107. König, Michael: Efficiency and Stability of Dynamic Innovation Networks, Research Sem-inar: Modeling Socio-Economic Systems and Crises, ETH Zurich, Zurich, Switzerland,March 18, 2008

106. Glattfelder, James: The Backbone of Control in G8 Countries, Annual Conference Physicsof Socio-Economic Systems (AKSOE), March meeting of the German Physical Society(DPG), Berlin, Germany, February 27, 2008

105. Schweitzer, Frank: Decelerating microdynamics accelerates macrodynamics in the votermodel, Section Dynamics and Statistical Physics (DY), February meeting of the GermanPhysical Society (DPG), Berlin, Germany, February 26, 2008

104. Glattfelder, James: Global Ownership: Unveiling the Structures of Real-World ComplexNetworks, Section Dynamics and Statistical Physics (DY), February meeting of the GermanPhysical Society (DPG), Berlin, Germany, February 25, 2008

41

103. Schweitzer, Frank: Efficiency and Stability of Dynamic Innovation Networks, Annual Con-ference Physics of Socio-Economic Systems (AKSOE), February meeting of the GermanPhysical Society (DPG), Berlin, Germany, February 25, 2008

102. Schweitzer, Frank: Enforcing consensus formation by heterogeneous inertia to change opin-ion, Annual Conference Physics of Socio-Economic Systems (AKSOE), February meetingof the German Physical Society (DPG), Berlin, Germany, February 25, 2008

101. Schweitzer, Frank: Evolution of economic networks, 401. WE-Heraeus-Seminar, Physikzen-trum Bad Honnef, Bad Honnef, Germany, January 21–23, 2008

100. Vitali, Stefania: Spatial Concentration in Manufacturing, A Cross-Country Analysis, DRUID-Dime Academy Winter 2008 PhD Conference, Aalborg, Denmark, January 17–19, 2008

99. Schweitzer, Frank: Enhancing social interaction: preferences, similarities, and trust, Work-shop on “Is there a Physics of Society?”, Santa Fe Institute, Santa Fe, USA, January 09–12,2008

200798. König, Michael David: On Algebraic Graph Theory and Dynamics of innovation Networks,

Seminar at Complex Systems Research Group, Medical university of Vienna, Vienna, Aus-tria, December 21, 2007

97. Schweitzer, Frank: Designing Social Interactions Based on Trust and Reputation, Collo-quium “Selected Challenges in the Social Sciences: Modeling and Simulation Approaches”,ETH Zurich, Switzerland, November 27, 2007

96. Lorenz, Jan: Continuous Opinion Dynamics under Bounded Confidence, Seminar “LargeGraphs and Networks”, UCL Université catholique de Louvain, Louvain-la-neuve, Belgium,November 15, 2007 (invited talk)

95. Schweitzer, Frank: Gibt es eine Physik sozio-ökonomischer Systeme?, Physikalisches Kol-loquium, Julius-Maximilians-Universität Würzburg, Germany, November 12, 2007

94. Schweitzer, Frank: Models of Firm Networks, Satellite Workshop: “Dynamics On andOf Complex Networks”, European Conference on Complex Systems (ECCS’07), Dresden,Germany, October 04, 2007

93. Battiston, Stefano: A Model of a trust-based Recommender System on a Social Network,Satellite Workshop: “Enhancing Social Interaction: Recommendation Systems, ReputationP2P and Trust”, European Conference on Complex Systems (ECCS’07), Dresden, Germany,October 04, 2007

92. Battiston, Stefano: Emergence of Systemic Risk in Trade Credit Networks, EuropeanConference on Complex Systems (ECCS’07), Dresden, Germany, October 03, 2007

91. Schweitzer, Frank: Efficiency and Stability of Evolving Innovation Networks, EuropeanConference on Complex Systems (ECCS’07), Dresden, Germany, October 01–03, 2007

90. Stark, Hans-Ulrich: Slower is Faster: Reduction of consensus times through social iner-tia in the voter model, European Conference on Complex Systems (ECCS’07), Dresden,Germany, October 01–03, 2007

89. Schweitzer, Frank: Empirics and Models of Firm Networks, Econophysics Colloquium andBeyond, Ancona, Italy, September 27–29, 2007 (invited talk)

88. Napoletano, Mauro: How tied is too tight? Links among financial institutions and thestability of financial systems, Econophysics Colloquium and Beyond, Polytechnic Universityof Marche, Ancona, Italy, November 28, 2007

87. Lorenz, Jan: Stochastic Fragility Dynamics with Trend Reinforcing, 4-th Annual MeetingCOST Action P10 “Physics of Risk”, Palermo, Italy, September 21–23, 2007

42 4. Talks

86. Tessone, Claudio Juan: Reluctance to opinion change leads to faster consensus, 4-th AnnualMeeting COST Action P10 “Physics of Risk”, Palermo, Italy, September 21–23, 2007

85. Geipel, Markus Michael: A generic framework for argumentation-based negotiation, Work-shop on Cooperative Information Agents XI, TU-Delft, Netherlands, September 19–21,2007

84. König, Michael David: Workshop on Modeling Dynamic Social & Economic Networks,Economic Science with Heterogeneous Interacting Agents (ESHIA), Agelonde, La LondeLes Maures, France, September 17–22, 2007

83. Walter, Frank Edward: A Model of a trust-based Recommender System on a Social Net-work. Fourth conference on Applications of Social Network Analysis (ASNA 2007), Zurich,Switzerland, September 13–15, 2007

82. Schweitzer, Frank: Emergence of social norms from collective agent interaction, Interna-tional Conference on Rational Choice and Social Institutions, Zurich, Switzerland, Septem-ber 06–08, 2007

81. König, Michael David: Efficiency and Stability of Dynamic Innovation Networks, ESSID2007 European Summer School on Industrial Dynamics, Workshop: Collaboration Net-works / Knowledge Diffusion, Dubrovnik, Croatia, September 03, 2007

80. Press, Kerstin: Do clusters promote growth? The case of Germany, UDE-CEDR JointConference Challenges to Long-run Economic Growth in China and Europe, Duisburg,Germany, August 26–29, 2007

79. Schweitzer, Frank: Physics in social sciences, 6th PSI Summer School on Condensed MatterResearch, Zuoz, Switzerland, August 24, 2007

78. Press, Kerstin: The nature of production and organisation, An N/K model of modularity,(dis)integration and performance, DRUID Summer Conference Appropriability, Proximity,Routines and Innovation, Copenhagen, Denmark, June 18–20, 2007

77. Lorenz, Jan: Threshold models of Continuous Opinion Dynamics – Clustering, Drifting andConvergence, Seminar “Interdisciplinary Physics”, Institute for Cross-Disciplinary Physicsand Complex Systems IFISC, Palma de Mallorca, Spain, June 18, 2007 (invited talk)

76. Schweitzer, Frank: Gibt es Gesetzmässigkeiten für komplexe Systeme? - Ein Blick aufsozio-ökonomische Beispiele, öffentliche Vortragsreihe: Komplexe technische Systeme, ETHZurich, Switzerland, June 06, 2007

75. Press, Kerstin: Divide to conquer? The Silicon Valley - Boston 128 case revisited, 5thInternational EMAEE Conference Globalisation, Services and Innovation: The ChangingDynamics of the Knowledge Economy, Manchester, UK, May 17–19, 2007

74. König, Michael David: Efficiency and Stability of Dynamic Innovation Networks, 5th In-ternational EMAEE Conference on Globalisation, Services and Innovation: The ChangingDynamics of the Knowledge Economy Session Networks ll, Manchester, UK, May 18, 2007

73. Battiston, Stefano: Emergence of Systemic Risk in Trade Credit Networks, Net 2007:Topology and Dynamics, Urbino, Italy, May 17, 2007

72. Battiston, Stefano: 4 Lectures Tutorial: From Graph Theory to Networks of Firms, Net2007: Topology and Dynamics, Urbino, Italy, May 17, 2007

71. Schweitzer, Frank: Modeling Economic Networks Theoretical Physics Seminar, Universityof Fribourg, Switzerland, April 30, 2007

70. Battiston, Stefano: Introduction to Networks, Workshop: “Reti: teoria ed applicazioni”,Milan, Italy, April 12, 2007

69. Napoletano Mauro: Can networks be too dense?, Workshop: “Reti: teoria ed applicazioni”,Milan, Italy, April 12, 2007

43

68. Battiston, Stefano: Emergence of Systemic Risk in Trade Credit Networks, MMCOMNETWorkshop, Warsaw, Poland, April 11, 2007

67. Geipel, Markus Michael: Value Chain Organization and Efficiency, MMCOMNET Work-shop, Warsaw, Poland, April 11, 2007

66. Schweitzer, Frank: A Trust-based recommendation system on a social network, AnnualConference Physics of Socio-Economic Systems (AKSOE), March meeting of the GermanPhysical Society (DPG), Regensburg, Germany, March 26–30, 2007

65. Schweitzer, Frank: Applying directed weighted networks to recommendation systems, Sec-tion Dynamics and Statistical Physics (DY), March meeting of the German Physical Society(DPG), Regensburg, Germany, March 26–30, 2007

64. König. Michael David: Evolving R&D Networks, Annual Meeting of the German PhysicalSociety, Working Group Physics of Socio-Economic Systems (AKSOE): Social, Information-and Production Networks lll, Regensburg, Germany, March 29, 2007

63. König, Michael David: Emergence of Networks from Optimizing Local Interaction, An-nual Meeting of the German Physical Society, Section Dynamics and Statistical Physics:Statistical physics of complex networks ll, Regensburg, Germany, March 26, 2007

62. Groeber, Patrick: Spontaneous Social Order by Informal Network Interaction, Frühjahrsta-gung 2007 der Sektion Modellbildung und Simulation der Deutschen Gesellschaft für Sozi-ologie (DGS), Kiel, Germany, March 22–23, 2007

61. Schweitzer, Frank: Sociophysics – Science or Fiction?, 18. Edgar-Lüscher-Seminar, Klosters,Switzerland, February 03–08, 2007 (invited talk)

60. Schweitzer, Frank: Empirics and Models of Firm Networks, CER-ETH Economics ResearchSeminar, ETH Zurich, January 29, 2007

59. Geipel, Markus Michael: Industry Organization and Efficiency, DRUID Winter Conference2007, Aalborg, Denmark, January 25, 2007

200658. Feiler, Matthias: Simultaneous Identification and Control of Time-Varying Systems, 45th

IEEE Conference on Decision and Control, San Diego, U. S. A. , December 13, 200657. Schweitzer, Frank: Modeling of socio-economic systems: A new branch of physics?, Lise-

Meitner Colloquium, Hahn-Meitner Institute, Berlin, December 11, 2006 (invited talk)56. Walter, Frank Edward: A Model of a Trust-Based Recommendation System on a Social

Network. COE Programme Seminar, Tokyo Institute of Technology, O-Okayama, Tokyo,Japan, November 30, 2006

55. Walter, Frank Edward: Impact of Trust on the Performance of a Recommendation Systemin a Social Network. GEAR Laboratory, Tokyo Institute of Technology, Tamachi, Tokyo,Japan, November 25, 2006

54. Schweitzer, Frank: The Role of Local Effects in Collective Decision Processes, ToyotaCRDLWorkshop “Decision Making and Uncertainty in Nonlinear Complex Systems”, Copen-hagen, Denmark, November 20–23, 2006 (invited talk)

53. Walter, Frank Edward: Impact of Trust on the Performance of a Recommendation Systemin a Social Network. Third conference on Applications of Social Network Analysis (ASNA2006), Zurich, Switzerland, October 05, 2006

52. Schweitzer, Frank: The Role of Heterogeneity in Collective Decision Processes, SatelliteWorkshop: Complex Adaptive Systems and Interacting Agents (CASIA), European Con-ference on Complex Systems (ECCS’06), Oxford, UK, September 28–29, 2006

51. Schweitzer, Frank: Impact of Trust on the Performance of a Recommendation System ina Social Network, European Conference on Complex Systems (ECCS’06), Oxford, UK,September 25–27, 2006

44 4. Talks

50. Stark, Hans-Ulrich: Alternating cooperation in social dilemmas: Announcement of a novelcomputer tournament. Goldrain workshop 2006, Goldrain Castle, Italy, September 20,2006

49. Press, Kerstin: Divide to conquer? The Silicon Valley - Boston 128 case revisited, Collo-quium, Section Economic Geography - Faculty of Geoscience, Utrecht University, Nether-lands, September 19, 2006.

48. Schweitzer, Frank: Übergang zur Kooperation, Ehrenkolloquium “Physik der Selbstorgan-isation”, Institut für Physik, Humboldt-Universität Berlin, September 15, 2006 (invitedtalk)

47. Schweitzer, Frank: Dynamik und Steuerbarkeit komplexer Systeme, Plenum “Konvergenzund Komplexität in Wissenschaft und Technologi”, Alpbacher Technologiegespräche, Alp-bach, Austria, August 24–26, 2006 (invited talk)

46. Schweitzer, Frank: Collective Decisions in Mulit-Agent Systems, First World Congress onSocial Simulation, Kyoto, Japan, August 21–25, 2006 (invited talk).

45. Schweitzer, Frank: Network Models of Cooperation, 21st-Century Center of ExcellenceProgram “Creation of agent-based social systems sciences”, Tokyo Institute of Technology,Tokyo, Japan, August 19, 2006.

44. Battiston, Stefano: Avalanches and Self-Organized Robustness in Evolving Networks, Work-shop on “Potential of Complexity Science for Business, Governments and the Media”, Bu-dapest, Hungary, August 05, 2006

43. Schweitzer, Frank: Modeling Complex Systems: On the Uses and Disadvantages of Selfor-ganization, Colloquium “What is Design? What is Modeling?”, ETH Competence Centerfor Digital Design & Modeling, Zurich, June 28, 2006

42. Napoletano, Mauro: Growth cycles and firms dynamics in an agent based model withfinancial market imperfections, 11th Conference of the International Schumpeter Society,Sophia-Antipolis, France, June 22, 2006

41. Press, Kerstin: When does defection pay? The stability of institutional arrangementsin clusters. DRUID Summer Conference “Knowledge, Innovation and Competitiveness:Dynamics of Firms, Networks, Regions and Institutions”, Copenhagen, Denmark, June18–20, 2006

40. Battiston, Stefano: Effects of Globalization in a Model of Production Networks embeddedin Geographical Space, 1st International Conference on Economic Sciences with Heteroge-neous Interacting Agents, University of Bologna, Italy, June 17, 2006

39. Stark, Hans-Ulrich: “Heterogeneity reduces time to reach consensus in collective decisions”.1st International Conference on Economic Sciences with Heterogeneous Interacting Agents,University of Bologna, Italy, June 15–17, 2006

38. Koenig, Michael David: Microeconomic Agent Model of Innovation Networks, 1st Interna-tional Conference on Economic Sciences with Heterogeneous Interacting Agents, Universityof Bolognia, Italy, June 15, 2006

37. Napoletano, Mauro: How do output growth rates distributions look like? Some time-seriesevidence on OECD countries, 1st International Conference on Economic Sciences withHeterogeneous Interacting Agents, University of Bologna, Italy, June 15–17, 2006

36. Stark, Hans-Ulrich: Experimental evidence of coherent, alternating cooperation in a routechoice game. Colloquium of the Institute for Economic Theory and Operations Research,University of Karlsruhe, Karsruhe, Germany, June 08, 2006

35. Schweitzer, Frank: Coalition Formation in Buyer-Seller Networks. Conference ComplexBehavior in Economics: Modeling,Computing and Mastering Complexity, Aix-en-Provence,France, May 18–20, 2006

45

34. Battiston, Stefano: Credit Chains and Bankruptcy Avalanches in Production Networks,Conference Complex Behavior in Economics: Modeling,Computing and Mastering Com-plexity, Aix-en-Provence, France, May 18–20, 2006

33. Stark, Hans-Ulrich: Alternating cooperation strategies in a Route Choice Game: Theory,experiments, and effects of learning scenarios. International Workshop on BehaviouralGame Theory and Experiments, Capua, Italy, May 12–13, 2006

32. Schweitzer, Frank: Distribution of Strategies in a Spatial Multi-Agent Game. 8th Interna-tional Workshop on Game Theoretic and Decision Theoretic Agents (GTDT) at the FifthInternational Joint Conference on Autonomous Agents and Multi-Agent Systems (AAMAS2006), Hakodate, Japan, May 09, 2006 (Proceedings GTDT, pp. 46-51)

31. Walter, Frank Edward: Impact of Trust on the Performance of a Recommendation Systemin a Social Network. Workshop “Trust in Agent Societies” at the Fifth International JointConference on Autonomous Agents and Multi-Agent Systems (AAMAS 2006), Hakodate,Japan, May 09, 2006

30. Schweitzer, Frank: Emergence and Evolution of Coalitions in Buyer-Seller Networks. Work-shop “Emergent Intelligence of Networked Agents” (WEIN’06) at the Fifth InternationalJoint Conference on Autonomous Agents and Multi-Agent Systems (AAMAS 2006), Hako-date, Japan, May 08, 2006 (invited talk)

29. Walter, Frank Edward: Emergence and Evolution of Coalitions in Buyer-Seller Networks.Tagung Soziale Netzwerke der Sektion Modellbildung und Simulation der Deutschen Gesellschaftfür Soziologie, Max-Planck-Institut für Gesellschaftsforschung, Köln, March 31–April 01,2006

28. Schweitzer, Frank: Self-Organization and Collective Decision Making in Animal and Hu-man Societies, Symposium Structure Formation and Self-Organization in non-equilibriumSystems, March meeting of the German Physical Society (DPG), Dresden, Germany, March31, 2006 (invited talk)

27. Koenig, Michael David: Emergence of Hierarchical Structures in a Stochastic NetworkModel, Section Dynamics and Statistical Physics (DY), March meeting of the GermanPhysical Society (DPG), Dresden, March 30, 2006

26. Mach, Robert: Non-stationary spatial pattern formation in a game-theoretical model, Sym-posium Structure Formation and Self-Organization in non-equilibrium Systems, Marchmeeting of the German Physical Society (DPG), Dresden, March 30, 2006

25. Rodrigues, João: The Network of Inter-Regional Direct Investment Stocks across Europe.Annual Conference Physics of Socio-Economic Systems (AKSOE), March meeting of theGerman Physical Society (DPG), Dresden, March 30, 2006

24. Battiston, Stefano: Credit Chains and Bankruptcy Avalanches in Production Networks,March meeting of the German Physical Society (DPG), Dresden, March 27, 2006

23. Koenig, Michael David: A Network Model of Company Growth, Annual Conference Physicsof Socio-Economic Systems (AKSOE), March meeting of the German Physical Society(DPG), Dresden, March 27, 2006

22. Schweitzer, Frank: Modeling of socio-economic systems: A new branch of physics?, Col-loquium, Max-Planck-Institut für Dynamik komplexer technischer Systeme, Magdeburg,Germany, March 23, 2006 (invited talk)

21. Walter, Frank Edward: Emergence and Evolution of Coalitions in Buyer-Seller Networks.EXYSTENCE Topical Workshop “Trust-Based Networks and Robustness in Organisa-tions”, ETH Zurich, March 13–March 17, 2006

20. Battiston, Stefano: Scaling Laws in Inter-regional Investment Networks across Europe,Institut d’Histoire et de Philosophie des Sciences et des Téchniques, Université de Paris I,February 9, 2006

46 4. Talks

19. Schweitzer, Frank: Swarming of Brownian Agents, Cognitive Science Brown Bag Lectures,Department of Informatics, University of Zurich, January 10, 2006 (invited talk)

200518. Schweitzer, Frank: Emergent Properties, Workshop “Emergent Properties in Social Pro-

cesses”, Vienna, November 28–29, 200517. Schweitzer, Frank: Einführungsvorlesung: Einfach zu komplex? Die Eigendynamik von

Systemen verstehen, ETH Zürich, October 31, 200516. Battiston, Stefano: Scaling Laws in the Network of Inter-Regional Direct Investment Stocks

across Europe, MMCOMNET Meeting, Oxford, UK, October 20, 200515. Battiston, Stefano: Innovation and decision making in firms networks, Topical Workshop

Innovation Networks - New Approaches in Modelling and Analyzing, Department of Eco-nomics, Augsburg, Germany, October 10–14, 2005

14. König, Michael: A catalytic growth model prototype for innovation networks?, TopicalWorkshop Innovation Networks - New Approaches in Modelling and Analyzing, Depart-ment of Economics, Augsburg, Germany, October 12, 2005

13. Schweitzer, Frank: Dynamics of Companies, DPG-School on Physics “Dynamics of Socio-Economic Systems: A Physics Perspective”, Bad Honnef, September 18, 2005

12. Schweitzer, Frank: Urban Structure Formation, DPG-School on Physics “Dynamics ofSocio-Economic Systems: A Physics Perspective”, Bad Honnef, September 18–24, 2005

11. Schweitzer, Frank: Von der Brownschen Bewegung zur Dynamik biologischer und sozialerGruppen, “Einstein Lectures” der Österreichischen Akademie der Wissenschaften, Wien,September 28, 2005

10. Schweitzer, Frank: Swarming of Brownian Agents, Symposium “Brownian Motion, an In-terdisciplinary Phenomenon”, Annual Meeting of the Austrian Physical Society, Wien,September 29, 2005

9. Fent, Thomas: Collective Social Dynamics and Social Norms, European Social SimulationAssociation (ESSA), Koblenz, Germany, September 5, 2005

8. Schweitzer, Frank: Multiplicative Models of Company Dynamics, 2nd Sino-German Evo-lutionary Workshop, Max-Planck-Institut zur Erforschung von Wirtschaftsystemen, Jena,August 29–31, 2005

7. Mach, Robert: Food-Web Presentation of Agent Interaction, Thematic Institute: Informa-tion and Material Flow in Complex Networks, Coldrano, Italy, June 28, 2005

6. Schweitzer, Frank: Local Interactions do Matter: Sometimes the Minority wins, Trans-Atlantic Initiative on Complex Organizations and Networks (TAICON), ETH Zurich, June6, 2005

5. Mach, Robert: Adaption of Cooperation: Random versus Local Interaction in a SpatialAgent Model, Recent Developments in Strategic Network Formation, Mannheim, May 20,2005

4. Schweitzer, Frank: Multiplicative Models for Company Dynamics, DPG-Jahrestagung“Physik seit Einstein”, Berlin, March 4–9, 2005

3. Mach, Robert: Effects of network topology on strategy distributions in a IPD, DPG-Jahrestagung “Physik seit Einstein”, Berlin, March 4–9, 2005

2. Schweitzer, Frank: Selbstorganisation und Emergenz aus physikalischer Sicht, WorkshopOrganic Computing, Kloster Irsee, February 13–15, 2005

1. Schweitzer, Frank: Selforganization in Multi-Agent Systems: Local Interaction versusGlobal Dynamics, Forschungsseminar Quantitative Methoden in der Ökonomie, Univer-sität Zürich, January 24, 2005

5 Funded Projects

The endowment of our chair certainly allowsus to fulfill our research and teaching commit-ments. However, given the broad range of re-search topics, it needs additional funding toreach a sufficient level of activity in all of theseareas. Applying for external funding has sev-eral positive aspects. First of all, it is a verycompetitive process. Hence, if a proposal isgranted after rounds of external evaluation, thisshould be seen as an additional confirmationof the quality of the research proposed (anddone so far). Secondly, as most funded researchprojects are based on collaborations, they al-low us to develop our scientific network in-side Switzerland, but also in Europe and world-wide. Thirdly, being partners in multinational

research projects, we are able to efficiently dis-seminate our competencies, while contributingto the frontiers in scientific research, interna-tionally.

Consequently, grant proposals played animportant role in our scientific activities overthe last five years. Certainly, not all of our ap-plications turned out to be successful, at theend. However, we are proud having receivedexternal funds of about 2.7 million CHF forour chair, up to today, some of which can bespent until the end of 2012. Noteworthy, exceptfor a rather small project, none of the fundsis from joint projects with the industry – theycome mostly from Swiss governmental institu-tions and from the European commission.

48 5. Funded Projects

5.1 Measuring and Modeling Complex Networks Across Domains

Duration 40 months (February 2005 – May 2008)

Funding program NEST PATHFINDER “Tackling complexity in science”. Contract No 12999(NEST)

Research partners University of Oxford (UK), Technische Universität Dresden (Germany),PolitechnikaWarszawska (Poland), INSEAD Business School, (France), ETH Zurich (Switzer-land), Stockholm University (Sweden).

Total budget 1,500,000 EUR

Benefit for the Chair 372,100 CHF (contribution to 1 Post-doc and 1 PhD student position)

The project MMCOMNET: Measuring andModeling Complex Networks Across Domainsaimed at understanding the structure, functionand behavior of networks from different areas,e.e. biology, economy, infrastructure. One ofthe challenges was the integration of macro-scopic, or top-down, approaches and agent-based, or bottom-up, approaches utilizing re-cent findings from the science of complexity.

The research focused on data and models ofparadigmatic examples from the domains men-tioned, such as fungal networks, textile supplynetworks, credit networks, venture capital net-works, road and transportation networks. Theoverall objective was to generate modeling ap-proaches and formulate universal principles toaid in the management of complex networksin real-world situations. The desirable prop-erties observed in model networks can poten-tially be transferred to networks involving com-puters, information, business and enterprise,power grids, and railway or other transport sys-tems. Our chair contributed to working pack-ages on supply networks, innovation networksand network visualization. Further informationis available at http://sbs-xnet.sbs.ox.ac.uk/complexity/complexity_mmcomnet.asp

Figure 5.1: Propagation of production failures ina simplified network of supply. Failures propagatedownwards to the initial shock (the node in red) orupwards or both, depending on the cost involved.

Deliverables

• Analysis and modeling of supply networks (4 publications from the Chair)

• Graph Layout: open source software, 1 publication from the Chair

• Models of Innovation Networks (3 publications from the Chair)

5.2. Physics of Risk 49

5.2 Physics of Risk

Duration 20 months (April 2007 – November 2008)

Funding program COST-P10 action, Swiss State Secretariat for Education and Research SER(Project C05.0148)

Research partners ETH Zürich, Chair of Systems Design, ETH Zürich, Institute for Opera-tions Research, Zürich University of Applied Sciences Winterthur, Institute for Data Anal-ysis and Process Design, University of Fribourg, Department of Physics, University ofGeneva, Department of Theoretical Physics

Total budget 750,000 CHF

Benefit for the Chair 327,780 CHF (covering two post-doctoral positions, plus funds to orga-nize a workshop and several meetings)

The COST-P10 action fostered the collabora-tion of researchers of leading European researchgroups, to achieve a new quantitative under-standing of the assessment of risk. Differentfrom various other activities in the field ofrisk management, this project involved physi-cists. In fact, in recent years statistical physicshas successfully contributed to many interdis-ciplinary fields – ranging from regulatory net-works in biology to financial market models.Therefore, a transfer of methods and tools fromstatistical physics into the area of risk assess-ment is be a promising endeavor. An addi-tional objective was to increase the understand-ing of the fundamental principles underlyingthe problems of uncertainty and complexity inthe socio-economic realms.

Frank Schweitzer was the Swiss national co-ordinator of COST-P10 and also lead the jointSwiss project, which encompassed five differentsub-projects: (i) Network structure, robustness

and adaptivity of organizations, (ii) Failure riskpropagation in economic and supply networks,(iii) Robust fitting technique in financial andnon-financial time series, (iv) Collaborative in-formation filtering for the internet, (v) Impactof severe environment changes on populationdynamics. Projects (i) and (ii) were carried outat ETH Zurich.

Figure 5.2: Dependent on the network topology andthe distribution mechanism (inwards/outwards),the failure of nodes may results in cascades of dif-ferent size.

Deliverables

• Topical issue “Risk, Markets, Games, and Networks” published in European Physical Jour-nal B (Guest Editors: F. Schweitzer, S. Battiston, C. J. Tessone) – see the attached tableof contents

• International Workshop “The Physics Approach to Risk: Agent-based Models and Net-works” – see under events

• 7 journal publications from the Chair of Systems Design


EPJ-B Topical Issue: RISK, MARKETS, GAMES, AND NETWORKS

• Guest Editors: F. Schweitzer, S. Battiston, C. J. Tessone - ETH Zurich

• Systemic Risk

– Systemic Risk in a Unifying Framework for Cascading Processes on NetworksJ. Lorenz, S. Battiston, F. Schweitzer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 441

– Collective firm bankruptcies and phase transition in rating dynamicsP. Sieczka, J.A. Holyst . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .461

– Eroding market stability by proliferation of financial instrumentsF. Caccioli, M. Marsili and P. Vivo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 467

– Computational Complexity of Impact Size Estimation for Spreading Processes on NetworksM. Laumanns, R. Zenklusen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 481

• Financial Markets

– The role of communication and imitation in limit order marketsG. Tedeschi, G. Iori, M. Gallegati . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 489

– Studies of the limit order book around large price changesB. Tóth, J. Kertész, J.D. Farmer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 499

– Empirical Behavior of a World Stock Index from Intrato Monthly Time ScalesW. Breymann, D. Lüthi, E. Platen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 511

– Network and eigenvalue analysis of financial transaction networksF. Kyriakopoulos, S. Thurner, C. Puhr, S.W. Schmitz . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 523

• Games and Decisions

– Physics of risk and uncertainty in quantum decision makingV.I. Yukalov and D. Sornette . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 533

– Diversity-induced resonance in a model for opinion formationC.J. Tessone, R. Toral . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 549

– Consensus and ordering in language dynamicsX. Castelló, A. Baronchelli, V. Loreto . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 557

– The role of a matchmaker in buyer-vendor interactionsL. Lu, M. Medo, Y.-C. Zhang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 565

– Structure-preserving desynchronization of minority gamesG. Mosetti, D. Challet, S. Solomon . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 573

• Cooperation

– Motion of influential players can support cooperation in Prisoner’s DilemmaM. Droz, J. Szwabinski, G. Szabo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 579

– Promotion of cooperation on networks? The myopic best response caseCarlos P. Roca, José A. Cuesta, Angel Sánchez . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 587

– Cycles of cooperation and free-riding in social systemsY. Ma, S. Gonçalves, S. Mignot, J-P. Nadal, M. B. Gordon . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 597

• Complex Networks

– Calculating Statistics of Complex Networks through Random Walks with an application tothe on-line social network BeboS.J. Hardiman, P. Richmond and S. Hutzler . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 611

– Predicting Missing Links via Local InformationTao Zhou, Linyuan Lu, Yi-Cheng Zhang. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 623

– Jamming and Correlation Patterns in Traffic of Information on Sparse Modular NetworksB. Tadić, M. Mitrović . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 631

– A complementary view on the growth of directory treesM. M. Geipel, C. J. Tessone, F. Schweitzer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 641

5.3. Physics of Competition, Cooperation and Conflicts 51

5.3 Physics of Competition, Cooperation and Conflicts

Duration 24 months (December 2009 – November 2011)

Funding program COST-MP0801 action, Swiss State Secretariat for Education and ResearchSER (Project C09.0055)


Benefit for the Chair 170,000 CHF (contribution to one post-doctoral position for two years)

The COST-MP0801 action ‘Physics of Compe-tition, Cooperation and Conflicts’ succeeds theprevious COST-P10 action. It takes the ‘biggerchallenge’ by aiming at social systems at large,to accomplish a new quantitative understand-ing of competition and conflicts. Because of theinherent complexity of socio-economic interac-tion, the outcome of such a global dynamics is(i) hard to predict, and (ii) difficult to controlor to design. New theoretical approaches arethus needed to achieve this goal.

Frank Schweitzer is the Swiss national coor-dinator of COST-MP0801 and also leads a spe-cific research project at ETH Zurich, where wedevelop a general theoretical model of agentscompeting in social and economic networks.This allows us to analyze and mitigate conflictsin networked societies, by combining methodsfrom statistical physics with those from socialnetwork analysis and game theory.

In particular, we will identify potentialsources of conflict when agents do not internal-ize in their decisions the externalities they in-duce on other agents through their networkingstrategies (meaning that they do not take intoaccount the effects of their actions onto others).We test the networks generated by the modelagainst the general empirical patterns found inreal-world networks, such as R&D networks,ownership networks or networks of open-sourceprojects. Moreover, we evaluate the efficiencyof certain network structures (see Figure 5.3)and thereby derive incentive mechanisms to op-

timize the interaction outcome in order to mit-igate the potential conflicts in presence of non-aligned utility functions. The figure shows com-puter simulations that assume that agents pre-fer to connect to the neighbors of their neigh-bors that have a higher centrality, which cre-ates local shortcuts. Network efficiency is mea-sured on the basis of the aggregate centrality ofagents. Environmental volatility measures therisk that if any single agent is exposed to an ex-ogenous shock, it will force the deletion of onelink. If the loss of links pushes the network ef-ficiency down and environmental volatility uppast some critical level, the strongly homoge-neous network structure will break down into asparse, hierarchical structure, similar to a core-periphery structure and is accompanied with abreakdown in network efficiency.

Figure 5.3: The structure and efficiency of an equi-librium network strongly depend on the conditionsunder which myopic agents can form links.


5.4 Ownership Networks and Corporate Control

Duration 42 months (November 2006 – April 2010)

Funding source Olsen Ltd., 8008 Zürich


Benefit for the Chair 150,773 CHF (covering a 50 % doctoral student position)

Who holds the most control in our glob-alized world? And how is control distributedacross economic actors and geographical re-gions? Interestingly, these issues which haveprofound implications for market competitionand systemic risk are not yet well understood.The analysis requires to be carried on at var-ious levels. On the one hand, the topologicalstructure of the ownership network, in whichthe control flows, has to be uncovered. On theother hand, a methodology has to be developedin order to compute control.

In this project two separate empirical stud-ies are performed, yielding insights into thestructural organization of ownership networks.Firstly, a cross-country analysis of 48 nationalstock markets has uncovered the local andglobal distribution of control. Secondly, theexamination of the whole worldwide ownershipnetwork (constructed around all transnationalcorporations) has revealed the global concen-tration of control and the organization and in-terdependence of the top power-holders.

The methodology is developed from existingmodels that suffer from various drawbacks. Inorder to track the flow of control that emergesfrom ownership relations in a network, a newcentrality measure is introduced. It is related tothe notion of eigenvector centrality and is spe-

cially adapted to cope with the specific topol-ogy found in real-world ownership networks. Italso overcomes the aforementioned limitations.

Finally, the emerging stylized facts allowthe development of an agent-based model whichcan shed light on the possible dynamics behindthe network formation process.

Figure 5.4: Strongly connected component of anownership network of firms. Node size correspondsto the logarithm of operating revenue, color indi-cates the degree of control.

Deliverables

• Project 1: (published 09/2009) J. B. Glattfelder and S. Battiston, Backbone of ComplexNetworks of Corporations: The Flow of Control, Physical Review E 80, 036104 (2009)

• Project 2: (submission being reviewed) S. Vitali, J. B. Glattfelder and S. Battiston, TheNetwork of Global Corporate Control

• Project 3: (deadline 02/2010) agent-based model reproducing some observed stylized facts

• PhD: (deadline 04/2010) Ownership Networks and Corporate Control: Mapping EconomicControl in a Globalized World

5.5. Proof-of-Concept of a Trust-based Recommender System 53

5.5 Proof-of-Concept of a Trust-based Recommender System

Duration 18 months (May 2008 – November 2009)

Funding source Gesellschaft zur Förderung der Forschung und Ausbildung in Unternehmenswis-senschaften an der ETH Zurich, Grant no 41-3682.4


Benefit for the Chair 70,000 CHF (funding the position of one software architect for 12months).

Figure 5.5: Screen capture of Knowledge Sharing Playground

Recommender Systems (RS) are applicationsthat enable users of a particular on-line plat-form, e.g. Amazon, Last.FM, etc., to retrieveinformation on products and services offered.This information can be provided at differentlevels of personalization and filtering. Thus,RS can be seen as tools to support the decision-making of consumers; because of this, they havebecome more and more widespread in all eco-nomic sectors. This project extends a noveltype of electronic RS, developed at our chair,towards a real-world application. Differentlyfrom existing RS, the proposed system lever-ages the fact that users are part of a real socialnetwork and that they trust each other to dif-ferent extents depending on the context. Themain benefit of this approach is that it offers

personalization i.e. the recommendations is tai-lored to each individual user.

The main objective of the project is to provethe feasibility of a trust-based recommendersystem using social networks. We achieved thisgoal by developing a Knowledge Sharing Play-ground (KSP) which is, at a glance, a webapplication where users can share knowledgewith other users. The framework implementsthe trust algorithms presented in Walter etal. (2006) and extended in Walter et al. (2009).

Currently, our group is negotiating a part-nership with development companies in orderto continue the development of the frameworkand to migrate it towards a commercial appli-cation. This will allow us to collect real dataand validate empirically our models of RS.

Deliverables

• The web application (framework) which implements the theoretical research results;

• The technical report of the implementation.


5.6 CCSS: Coping with Crises in Complex Socio-Economic Sys-tems

Duration 36 months (September 2008 – August 2011)

Funding source ETH Research Grant (CHIRP I)

Research partners Six chairs from three different departments of ETH Zurich: Prof. Kay Ax-hausen (D-BAUG), Prof. Lars-Erik Cederman (D-GESS), Prof. Dirk Helbing (D-GESS),Prof. Hans J. Herrmann (D-BAUG), Prof. Frank Schweitzer (D-MTEC), Prof. DidierSornette (D-MTEC).


Benefit for the Chair 108,000 CHF (contribution to one post-doc position)

The research grant further supports the ac-tivities of our ETH Competence Center ‘Cop-ing with Crises in Complex Socio-EconomicSystems’ (CCSS), which started in Septem-ber 2008 (see also http://www.ccss.ethz.ch).By means of theoretical and empirical analy-sis, CCSS aims at understanding the causesof and cures to crises in selected problem ar-eas, for example in financial markets, in soci-etal infrastructure, or crises involving politicalviolence. While these different crises may havetheir own time scale and evolution, they canbe seen as the unintentional result of the inter-action among millions of social actors. In thisproject, we look at these crises as emergent phe-nomena in complex systems and we investigatethe feedback mechanisms that generate themas well as the possible strategies to prevent ormitigate them.

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Overall, the project consists of 3 workingpackages and our contribution is mainly in thefirst package: “Crises in Markets”. Our scien-tific goal is to investigate possible mechanismsfor the control of systemic risk and for the mit-igation of crises. We have started developinga dynamic model of financial fragility that isbased on the literature on liability networks infinancial economics. We are able to analyticallyinvestigate the relation between individual riskdiversification and emerging systemic risk, inthe presence of financial acceleration. Under-standing this relation is a first important steptowards the possible control of systemic risk.On-going work focuses on extending the modelto more complex situations and network struc-tures.

5.7. Cyberemotions 55

5.7 Cyberemotions

Duration 48 months (February 2009 – January 2013)

Funding program EU 7th Framework Programme. Theme 3: Science of complex systems forsocially intelligent ICT.

Research partners Warsaw University of Technology (Poland), Ecole Polytechnique Fédéralede Lausanne (Switzerland), University of Wolverhampton (UK), Österreichissche Studi-engesellschaft für Kybernetik (Austria), ETH Zürich (Switzerland), Jozef Stefan InstituteLjubljana (Slovenia), Jacobs University Bremen (Germany), Technical University Berlin(Germany), Gemius SA (Poland).

Total budget 3,600,000 EUR

Benefit for the Chair 543,000 CHF (contribution to fund one post-doc and one doctoral stu-dent position for the duration of the project)

The project focuses on the role of collectiveemotions in creating, forming and breaking-upof e-communities. Understanding these phe-nomena is important in view of the growingrole of ICT-mediated social interactions andspecific features of e-communities. The chal-lenge of this interdisciplinary project is to com-bine both: psychological models of emotionalinteractions and algorithmic methods for de-tection and classification of human emotionsin the Internet. The latter uses probabilisticmodels of complex systems and data drivensimulations based on heterogeneous emotion-ally reacting agents. Our theoretical founda-tions will mainly apply statistical physics ap-

proaches of emergent properties in multi-agentsystems and methods developed to model self-organizing networks. On the empirical side,we concentrate on how to support and main-tain emotional climates of security, trust, hope,and freedom in future techno-social communi-ties and how to prevent and resolve conflicts inthem.

At the Chair of Systems Design, we focuson agent-based models of collective emotions.Our partner from Bremen will provide dataand empirical results on psychological emotiondynamics when people read or interact in theweb. Our partner from Wolverhamptom re-trieves datasets from the web and developstools for emotional text classification. Theseresults and data will help us to develop theoreti-cal models for the emergence of collective emo-tions in cyberspace in collaboration with thegroups in Warsaw and Ljubljana. The partnersin Vienna, Lausanne, and Berlin build on thesemodels with the development of concepts aboutnew information and communication services.

Figure 5.8: Impact of lasting and temporal contexts on the formation of collective emotions.


5.8 On The Interplay between Social Interactions and SoftwareArchitecture in Open Source Software

Duration 24 months (October 2009 – September 2011)

Funding source Swiss National Science Foundation (Grant CR12I1_125298)


Benefit for the Chair 202,050 CHF (one post-doctoral position for the duration of the project)

Open Source Software denotes computer soft-ware developed by voluntary contributions anddistributed under specific licensing terms, en-abling the user to study the code and alter it atwill. The popularity of Open Source is reflectedby the fact that it led to several category-killers: products that quickly took over signif-icant market-share. For example, the Apacheweb server holds around 50% of the world-wide server market and Mozilla’s Firefox holdsaround 30% of the browser market.

Consequently, several scientific disciplinestook up research on Open Source. Physicistsuse the new developments in network science tostudy both the architecture of Open Source so-lutions as well as the social networks in the de-veloper communities. In Computer Science, re-search enables and fosters Open Source by pro-viding new approaches to software engineeringand collaboration. Management Science cen-ters on the motivation of developers, the com-petitive dynamics between Open Source andproprietary software solutions as well as the de-terminants of success.

The aim of this project is to bring togetherthese scientific disciplines to reap synergies be-tween them, and to advance the understand-ing of the complex socio-technological dynamicsunderlying Open Source Software beyond thescope of one particular discipline. We focus onthe statistical laws governing the evolution of

the software architecture, its link to project or-ganization, and the resulting social dynamics.

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Figure 5.9: In the PyDev project, development(gray) and communication (black) run in sync.

The project contributes both to science andpractice. With its explicit multi-disciplinarysetup, it establishes a holistic picture of thephenomenon of Open Source and fosters crossfertilization between physics, computer scienceand management. We suppose that this insightwill yield results which will be also relevantto practitioners. Understanding the statisticallaws of software evolution may help developersto steer development towards favorable archi-tectures. Understanding the link between ar-chitecture and project organization may enablenew management principles or provide toolsfor smoothening the interface between software,developers, and users.

5.9. R&D Network Life Cycles 57

5.9 R&D Network Life Cycles

Duration 24 months (January 2010 – December 2011)

Funding source Swiss National Science Foundation (Grant 100014_126865)


Benefit for the Chair 192,890 CHF (one post-doctoral position for the duration of the project)

It is widely agreed that research and develop-ment (R&D), with its subsequent technolog-ical innovations, is the driving force in eco-nomic growth. R&D activities are distributedacross different economic actors, notably firmsor inventors, which interact in many ways, e.g.through collaboration or knowledge transfer.Using the conceptual network approach, thesefirms can be seen as nodes and their interac-tions as links of a network that may evolve overtime, both with respect to the actors and theirinteractions. As an illustration, Figure 5.10shows such a network of inventors in Boston128. In this project we have complete patentdata available that allows us to construct net-works of inventors that have appears togetheron a patent.

Our aim is to explain the formation ofR&D networks as a purely endogenous phe-nomenon, emerging from the behavior of indi-vidual firms. Many studies have been devotedto the investigation of the consequences of acertain R&D network structure on a firm’s per-formance. However, only little is known aboutthe time evolution of this structure, or the an-tecedents of this evolution. Emphasizing tem-poral aspects, this proposal focuses both on therelationship between the structure of an R&Dnetwork and its performance, on an individualfirm and aggregate industry level, and on thedynamics of network creation and evolution.

In the project we study, first, how the be-havior of firms affects the types of networksthat emerge at the aggregate level and, second,

how a firm’s position in the network affects itsknowledge productivity and performance.

A distinctive feature of this proposal, theanalysis of the evolution of R&D networks iscarried out both on an empirical and on a the-oretical level. The empirical analysis will allowus to identify specific firm and network charac-teristics that can serve as the foundation andbenchmark for realistic models of R&D net-works. The theoretical approach aims at de-veloping such a realistic model to explain thearchitecture and formation of the network aswell as the productivity of the firms embeddedin the network. The joint analysis shall point atpractical implications for policies to foster theinnovativeness of an R&D intensive industry inwhich firms exchange knowledge through R&Dcollaborations.

Figure 5.10: Inventor network of Boston 128 inven-tors (as evidenced by joint patent applications) in1986–1990. The figure shows the largest connectedcomponent only. Boxed areas indicate highly clus-tered inventors (source: Fleming et al. 2007)


5.10 OTC Derivatives and Systemic Risk in Financial Networks

Duration 36 months (October 2009 - September 2012)

Funding source Swiss National Science Foundation (Grant CR12I1-127000/1)


Benefit for the Chair 476,000 CHF (contribution to fund one PostDoc and one PhD studentposition, plus travel)

Different from regulated markets, OTC (over-the-counter) markets are non-regulated mar-kets, in which derivatives on different under-lying assets are traded without the presence ofa clearing house. The volume of OTC mar-kets represents an important part of the totalvalue of financial markets. In OTC markets,the counterpart default risk generates a net-work of interdependences among actors. In thisproject, we investigate how the structure of fi-nancial networks affects systemic risk, and inparticular to what extent OTC derivatives con-tribute to systemic risk.

With our insights, we want to contribute tothe recent debate about the impact of deriva-tives products on financial instability. Sincederivative products make market more com-plete and by this reinforce financial integration,they are claimed to improve stability. Other au-thors have instead argued that derivatives mayincrease the probability of financial instabilitydue to a number of reasons: the complexityand lack of transparency of the contracts; dis-proportionate leverage exposure that actors cantake; strict interconnection with other markets;issues of monetary policy such as difficulty incompiling information on banks’ reserves.

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Figure 5.11: Gross market value and Credit expo-sure of OTC derivatives strongly increased over thelast few years, while OTC values dropped duringthe crisis.

Secondly, we want to achieve a better un-derstanding of the relation between integration

of financial markets and financial instability.Indeed, completeness and integration of mar-kets allow for full diversification of risks in allstates of nature. On the other hand, integra-tion exposes agents to the possible propagationof financial distress from other agents. It is notclear a priory which of the two factors domi-nate. To gain more insight, we start from theidentification of two fundamental mechanismsat work in the propagation of financial distressamong agents, namely the interdependence oftheir financial robustness and the trend rein-forcement, known as financial acceleration.

The project consists of a theoretical partand an empirical one. The first one aims atbridging two existing strands of literature: one,in finance, on default correlation and anotherone, in economics, on systemic risk in creditnetworks. To merge these, we build on ourrecent and on-going work on systemic risk incredit networks. We further develop a modelthat dynamically describes the evolution of fi-nancial robustness at a firm-level when agentsare connected in a network of credit and OTCderivative contracts. The empirical part of theproject analyzes cross-sectional time series offinancial statements data, including off-balanceexposures mainly represented by OTC deriva-tives. For this, we use the classical horizon-tal and vertical analysis of balance sheets andprofit-loss accounts, the ratios and cash-flowsanalysis. Additionally, we are interested indetecting mechanisms of interdependence andtrend reinforcement to possibly validate themodels developed in the theoretical part.

Since our previous work has shown thatcredit networks can become unstable if agentsare too much interconnected, a crucial point(related to important monetary policy issues) isto introduce OTC derivative contracts to bet-ter fit the realm of financial markets and thusinvestigate their impact on stability.

6 Teaching

In 2004, when Frank Schweitzer started at ETHZurich, he became part of the new Depart-ment of Management, Technology, and Eco-nomics (D-MTEC) and has been involved fromthe beginning in developing the curriculum forthe new Master of Science program in Man-agement, Technology, and Economics (MScMTEC). This program is targeted at Swiss andinternational students with a technical back-ground, who want to obtain additional qual-ifications in management and economics – tomeet the requirements of leading technologyoriented companies. At the same time, the sci-entific frontiers in management and economicshave to be an integral part of this educationalprogram, with ETH Zurich aiming to be aplayer in the top league of scientific researchinstitutions.

For us, this meant developing completelynew courses on subjects we had not not dealtwith before. The challenge is not only to pay at-tention to the requirements of practitioners, but

also to provide a strong quantitative method-ological background to advance scientific re-search on these topics. In all these courses em-phasis is put on the quantitative understandingof socio-economic systems, to model their struc-ture and dynamics and to optimize and designtheir behavior.

Currently, we offer three different coursesat the master level, which are all accompaniedby extensive exercises. Two of these courses,Systems Dynamics and Complexity and Collec-tive Dynamics of Firms, are core courses of theMTEC curriculum, the third one on ComplexAdaptive Systems is hooked to the Departmentof Physics of ETH, with Frank Schweitzer be-ing an associated member. Additionally, since2008 we have been co-organizing the Inter-disciplinary Seminar Modeling Complex Socio-Economic Systems and Crises for students anddoctoral students, where internal and externalspeakers give talks every Tuesday during thesemester.

60 6. Teaching

6.1 Systems Dynamics and Complexity

The course encompasses a large range of ap-proaches towards systems and their dynamics,with particular emphasis on problem solving.It starts from basic insights on why real prob-lems are not simple but complex, and whysystems do not always do what we expect.Based on that, we introduce tools and methodscommonly used in Systems Engineering (sys-tems oriented management) and Project Man-agement to analyse problems at large, to searchfor solutions and to optimize project schedul-ing. The part of Systems Dynamics focuseson models of systems with various positive andnegative feedback loops, which exhibit, for ex-ample, unwanted oscillations. Using examplesof Economic Dynamics, such as the adoptionof new products or the competition betweentechnologies, we also develop an understand-ing of the specific nonlinear dynamics in mar-kets. Eventually, we investigate the effects ofNonlinear Dynamics on the instability of sys-tems and their long-term predictiveness, withapplications in supply chains and manufactur-ing systems.

In addition to the theoretical framework

developed, emphasis of the course is put onquantitative tools for systems modeling and onreal world examples from industries and mar-kets. Weekly self-study tasks provide opportu-nities to deepen the theoretical understandingof the topics, to develop own solutions in smallgroups, and to learn about the software VEN-SIM used for systems dynamics modeling.

Participants of the course should have anengineering background and be interested inlearning about systems dynamics at large, bothfrom a practical and a modeling perspective.

Syllabus

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Lecture 01 – Overview, Basic Introduction

1. Systems: Basic Concepts

• about this course: administrative issues, self-study tasks, seminars

• complex systems versus systems dynamics perspectives, systems design

Self-Study Task 01: Systems thinking (discussion exercise). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Lecture 02 – Systems Engineering I

2. Systems Engineering: Problem Solving Cycle

• systems engineering/ systems oriented management: basic approach

• problem solving cycle (PSC) I: situation analysis, definition of objectives

Self-Study Task 02: PSC – Airport example I (discussion exercise). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Lecture 03 – Systems Engineering II / Project Management I

• problem solving cycle II: search for solutions, validation and decision

6.1. Systems Dynamics and Complexity 61

3. Project Management: Scheduling Techniques

• project phases, milestone-trend diagram

• bar chart scheduling

Self-Study Task 03: PSC – Airport example II (discussion exercise). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Lecture 04 – Project Management II: 08.10.2009

• critical path method: precedence network, forward and backward pass, float

Self-Study Task 04: PEST and SWOT analysis (discussion exercise). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Lecture 05 – Project Management III: 15.10.2009

• milestone-trend diagram, feedback control loop, stock and flow diagrams

• case study: critical changes in project management

Self-Study Task 05: Critical chain project management (discussion exercise) (due: 22.10.09). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Lecture 06 – Systems Dynamics I

4. Systems Dynamics: Oscillatory Behavior

• What is modeling? software program overview (Vensim)

• feedback processes, causal loops

• example: predator-prey population dynamics

Self-Study Task 06: Population dynamics (Vensim exercise). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Lecture 07 – Systems Dynamics II

• Workforce-inventory model

• case study: high velocity industry

Self-Study Task 07: Workforce-inventory dynamics (Vensim exercise). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Lecture 08 – Systems Dynamics III

• oscillatory behavior in supply chains, bullwhip effect

• innovation adoptation and product life cycle

Self-Study Task 08: Adoptation of innovation (Vensim exercise). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Lecture 09 – Economic Dynamics I

5. Economic Dynamics: Technology Adoptation

• models of technology diffusion

• sources of innovations, ’word of mouth’ effect, fads

Self-Study Task 09: Network effects (discussion exercise)

62 6. Teaching

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Lecture 10 – Economic Dynamics II

• linear and nonlinear Polya processes

• path dependence, lock-in effects (example: Qwerty)

Self-Study Task 10: Competing products (Vensim exercise). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Lecture 11 – Nonlinear Dynamics I

6. Nonlinear Dynamics: Instability and Chaos

• cobweb dynamics: elasticities of supply, demand

• bifurcations, logistic map

Self-Study Task 11: Cobweb dynamics (Vensim exercise). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Lecture 12 – Nonlinear Dynamics II

• chaos in manufacturing systems

• case study: wetbench example

Self-Study Task 12: Questions/Answers. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Lecture 13 – Nonlinear Dynamics III

• stability analysis, supply networks

• complexity in enterprises (and beyond)

Self-Study Task 13: Exam preparation

6.2. Collective Dynamics of Firms 63

6.2 Collective Dynamics of Firms

In this course, we are interested in the collec-tive dynamics observed in a large number offirms, rather than in the dynamics of individ-ual firms. The latter is hard to predict anddepends on many firm specific factors, rangingfrom location and taxes to managerial talent.The collective dynamics observed on the aggre-gate level of a system of firms, however, showsome remarkable regularities which can be cov-ered by rather simple models. Examples arethe distributions of firm sizes and growth rates,but also specific patterns resulting from profitmaximizing behavior.

One aim of this course is to obtain these reg-ularities from analyzing data sets of real firmsand to explain them using different stochasticmodels. To provide the skills for such an anal-ysis, weekly exercises complement the lectures.A second focus of the course is on modelingthe interaction patterns between firms, in par-ticular competition and cooperation, but alsotheir adoption of a common behavior, or theirresponse to innovations. Eventually, the thirdpart of the course aims at embedding these find-ings into the economic theory, by using con-

cepts of production functions, competitive equi-librium models and models from evolutionaryeconomy.

Participants of the course should have somebackground in mathematics and statistics, a de-dicated interest in formal modeling and com-puter simulations, and a motivation to learnabout industrial organization from a quantita-tive perspective.

Figure 6.1: The size distribution of US manufactur-ing firms clearly follows a log-normal distributionover 20 years

Syllabus

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1. Data and Empirics

Lecture 01 – Introduction, Data and Empirics I: Software

• motivation: industrial dynamics, administrative issues

• data sources, computer programs (Emacs, R)

Exercise 01: R – Learning about R, Installation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Lecture 02 – Data and Empirics II: Distributions

• continuous/discrete distributions, normal/skewed distributions

• binning data, testing distributions (density estimation)

Exercise 02: R – Binning data, testing distributions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Lecture 03 – Data and Empirics III: Size and Growth of Firms

• stylized facts about firm size and growth rate distributions

• Zipf plot, ranked distributions

64 6. Teaching

Exercise 03: R – Calculating/testing firm size and growth rate distributions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Lecture 04 – Data and Empirics IV: Parameter Dependencies

• estimating parameters of distributions, Maximum-Likelihood method

• stylized facts about size dependence of volatility, scaling

Exercise 04: R – Estimating parameters from firm distributions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Lecture 05 – Data and Empirics V: Entry and Exit dynamics

• stylized facts about age, time correlations

• stylized facts about entry and exit of firms

Exercise 05: R – Verifying growth hypotheses. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

2. Stochastic Growth ModelsLecture 06 – Stochastic Growth Models I: Random processes

• agent based modeling vs systems

• random processes, Gibrat dynamics

Exercise 06: R – Simulating additive stochastic processes. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Lecture 07 – Stochastic Growth Models II: Modifying Gibrat’s Dynamics

• entry/exit dynamics

• correlations in growth rates

Exercise 07: R – Simulating multiplicative stochastic processes. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

3. Competition and CooperationLecture 08 – Competition and Cooperation I: Free Market Capitalism

• Marx on competition

• competition scenarios under limited resources

Exercise 08: R – Modeling indirect competition. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Lecture 09 – Competition and Cooperation II: Inequality Distribution

• distribution of market shares, Lorenz curve

• models of indirect competition, selection vs. hyperselection,

Exercise 09: R – Calculating the Lorenz curve. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Lecture 10 – Competition and Cooperation III: Strategic Network Formation

• growth through network effects, life cycles

• role of direct and indirect reciprocity

Exercise 10: R – Simple models of direct and indirect growth

6.2. Collective Dynamics of Firms 65

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Lecture 11 – Competition and Cooperation IV: Adoption of Behavior

• herding effects, firms adopting a common business practice

• Polya processes, path dependence, lock-in effects

Exercise 11: R – Simulating nonlinear Polya processes. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Lecture 12 – Competition and Cooperation V: Agent-based Models of Innovation

• innovation, search and growth

• stochastic models of innovation processes

Exercise 12: R – Simulating the survival of innovations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

4. Production FunctionsLecture 13 – Production Functions I: Neoclassical Growth Model

• Cobb-Douglas production functions

• Solow model, empirical implications

Exercise 13: R – Calculating the Solow model. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Lecture 14 – Production Functions II: Competitive Equilibrium

• supply and demand, market clearing

• Lucas model of size distribution of firms

Exercise 14: R – Simulating the worker/manager distribution. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Lecture 15 – Production Functions III: Productivity and Technical Change

• evolutionary models with boundedly rational firms

• perform ace of innovation/imitation strategies

Exercise 15: R – Modeling imitation vs innovation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Lecture 16 – Production Functions IV: Microproductivity

• stochastic models of productivity dynamics

• empirical analysis: determinants of productivity

Exercise 16: R – Simulating microproductivity growth

66 6. Teaching

6.3 Complex Adaptive Systems

What do gregarious insects, neurons, or humanvoters have in common? They form complexsystems with multiple interacting components(agents). In complex systems, the macroscopicbehavior of the system as a whole cannot besimply inferred from the behavior of the agents.Nonlinear feedback processes together with spe-cific conditions for the supply of energy, matter,or information may lead to the emergence ofnew system qualities on the macroscopic scale.In complex adaptive systems, the agents areable to adapt to changing external or inter-nal conditions. This may lead to even morecomplex dynamics and, together with conceptsof evolutionary optimization, provides a frame-work of evolutionary processes.

The course presents this cutting-edge re-search area from different perspectives. In thefirst part, the basic concepts of nonlinear dy-namical systems are introduced and applied tospatio-temporal structure formation. In par-ticular, the emergence of patterns in physico-chemical and biological systems is discussed.In the second part, two different agent-basedframeworks for modeling complex systems areintroduced: Brownian agents and cellular au-tomata. Discussed examples range from opin-ion dynamics and evolutionary game theory to

population dynamics. In the third part, thefocus is on the structural and dynamical fea-tures of networks, with an emphasis on randomBoolean networks and catalytic networks. Ap-plications cover prebiotic evolution as well associal and economic networks where the inter-action between nodes evolves over time.

Students get a broad overview of conceptsand methods used in the field of complex adap-tive systems. They learn to use methods of dy-namical systems and statistical physics to for-mally describe and analyze these systems. Thecollective dynamics are illustrated by meansof computer simulations, mainly using multi-agent approaches.

, , ,- - --,, --,Micro Level , , ,- - --,, -

-,Macro Level

Figure 6.2: The key question in complex systemstheory: How are the properties of the elements andtheir interactions (“microscopic” level) related tothe dynamics and the properties of the whole sys-tem (“macroscopic” level)?

Syllabus

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Lecture 01 – Introduction, Non-linear Dynamics I

1. Non-linear Dynamics

• administrative issues, examples

• (non)autonomous systems, deterministic vs. stochastic, stability analysis

Exercise: A linear version of Romeo and Juliet. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Lecture 02 – Non-linear Dynamics II

• Liapunov functions, attractors in 2d systems, classification of fixpoints

• population dynamics, bifurcations

Exercise: Predators and preys: Lotka-Volterra model. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Lecture 03 – Bifurcations and Chaos

• gradient systems, catastrophe theory

6.3. Complex Adaptive Systems 67

• instability and chaos, logistic map, Liapunov exponent

Exercise: Bifurcations, A simple adaptive system with Liapunov function. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Lecture 04 – Structure Formation I

2. Structure Formation

• diffusion, population growth and spread, Turing patterns

• example of pattern formation: Schnakenberg model

Exercise: Logistic map and its Liapunov exponents. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Lecture 05 – Structure Formation II

• activator-inhibitor systems, activator-substrate reactions

• growth of hydra, spatial vs. temporal structures

Exercise: Diffusion driving (Turing) instabilities. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Lecture 06 – Langevin / Fokker-Planck Equations

3. Brownian Agent Models

• micro vs. macro perspective, Langevin / Fokker-Planck equations

• active Brownian particles, interacting Brownian particles (agents)

Exercise: Brownian motion with absorbing and reflecting boundaries. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Lecture 07 – Brownian agents

• Brownian agents, self-organization of networks

• examples: neural structures, foreaging route of ants, human crowds

Exercise: Brownian agents in an adaptive landscape. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Lecture 08 – Graphs and Networks

4. Complex Network Models

• graph theory, network types

• network characteristics, social networks, small-world phenomenon

Exercise: Growths of small-world and scale-free networks. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Lecture 09 – Boolean Networks

• (Random) Boolean Networks, Kauffmann network

• robustness of Boolean Networks, mean field theory

Exercise: Boolean Networks, grand ensemble. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Lecture 10 – Cellular Autamata I

5. Cellular Automata Models

• cellular automata, Wolfram classification

68 6. Teaching

• Game of Life, stochastic cellular automata, sand pile model

Exercise: Game of Life, Reversibility. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Lecture 11 – Cellular Autamata II

• social simulations, voter models, social impact

• artificial societies

Exercise: Random voter model. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Lecture 12 – Game theory and Evolution I

6. Evolutionary Models

• game theory, prisoner’s dilemma, evolutionary game theory

• conditions for cooperation, iterated prisoner’s dilemma

Exercise: Game theory: pure and mixed strategies. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Lecture 13 – Game theory and Evolution II

• Fisher-Eigen equation, mutation, error catastrophe, hypercycles

• evolutionary dynamics, coevolution

7 Events

Events organized by our team are an importantmeasure to increase our visibility. This was ofparticular importance five years ago, when westarted and had not established our reputation,yet. During the first years, SG colloquia withdistinguished external speakers were organizedon a regular basis – whereas today many of ourguest speakers are invited to the CCSS Semi-nar, which is jointly organized with the Chairsinvolved in the Competence Center ‘Copingwith Crises in Complex Socio-Economic Sys-tems’ (CCSS).

In addition to these activities, we also or-ganized six international workshops and oneschool for young scientists on research topics weare involved in. Four of these workshops werehosted in Zurich, which allowed us to attractinvited speakers and participants from all overthe world to ETH Zurich. In many cases, theseworkshops became the starting point of fruitfulcollaborations with researchers from Switzer-land and abroad. They were a good oppor-tunity for us to disseminate the results of ourresearch.

7.1 Workshops and Schools

7.1.1 The Fifth International Workshop on Emergent Intelligence on NetworkedAgents (WEIN 2010)

Date and Location May 10–11, 2010, Toronto, Canada

Website http://web.sg.ethz.ch/workshops/wein2010/

Scope TheWEINWorkshop series started as a Japanese initiative and has been part of AAMAS,the International Conference on Autonomous Agents and Multiagent Systems, regularlysince 2006. The aim of this workshop is to investigate the role of networked agents in theemergence of systemic properties, notably emergent intelligence. The focus is on topics suchas network formation among agents, feedback of network structures on agents dynamics,network-based collective phenomena, and emergent problem solving of networked agents.

Traditionally, these topics have been addressed by researchers in the field of network science.Currently, however, network science has much more affinity to the community of complexsystems research than to the one of multi-agent systems. The intention of this workshopis to change this state of art by bridging the gap between the two research communities incomplex networks and multi-agent systems.

Thus, the workshop shall contribute to the development of an active multi-disciplinarycommunity by (i) increasing the mutual awareness of researchers in the MAS and complexnetwork communities, (ii) building a foundation for combining agent-based modeling andcomplex networks, (iii) developing predictive methodologies that can be used to study theemergent intelligence of networked agents.

70 7. Events

7.1.2 International Workshop: ‘Coping with Crises in Complex Socio-EconomicSystems’

Date and Location June 8–12, 2009, ETH Zurich.

Website http://www.soms.ethz.ch/workshop2009/index

Scope The interdisciplinary workshop aims at a quantitative understanding of crises in socialand economic systems. These crises are unstable and dangerous situations characterized byabrupt and large-scale changes, which are very hard to predict with conventional methodsand even harder to control. The workshop was co-organized with the Chairs of the CCSSCompetence Center. It was accompanied by a summer school for young scientists and dailythink tank discussions.

Monday, June 8

08:30 Registration and Coffee Break09:40 Opening by Dirk Helbing09:55 Neil F. Johnson: Brains not Bullets? From Terrorism, Insurgencies and Drug

Wars, to Street Gangs and World of Warcraft10:35 Lars-Erik Cederman: Growing Sovereignty: Modeling the Shift from Indirect

to Direct Rule11:15 Coffee Break11:30 Aaron Clauset: Dynamics of Terrorist Groups12:10 Stephen Eubank: Policy Informatics for Complex Systems12:50 Lunch Break14:15 Parallel Sessions15:05 Coffee Break15:20 Parallel Sessions16:10 Coffee Break16:25 Dirk Helbing: Cooperation and Conflict in the Prisoner’s Dilemma and the

Emergence of Norms17:05 Think Tank on Conflicts19:00 Welcome Aperitif

Tuesday, June 9

09:00 Thomas Lux: Explaining and Forecasting the Psychological Component ofEconomic Activity

09:40 Coffee Break09:55 Mauro Gallegati: Financially Constrained Business Fluctuations in an Evolv-

ing Network Economy10:35 Imre Kondor: Financial Regulation: An Attempt to Regulate Complexity11:15 Coffee Break11:30 Luciano Pietronero: Self-Organization and Finite Size Effects in Agent Mod-

els for Financial Markets12:10 Stefan Thurner: No Risk – No Fun: Modeling Financial Crashes12:50 Lunch Break14:15 Parallel Sessions15:05 Coffee Break15:20 Parallel Sessions16:10 Coffee Break16:25 Didier Sornette: Financial Bubbles, Real Estate Bubbles,

Derivative Bubbles, and the Financial and Economic Crisis17:05 Think Tank on Financial Crises

7.1. Workshops and Schools 71

Wednesday, June 10

09:00 Shlomo Havlin: Efficient Immunization Approaches to Avoid EpidemicSpreading

09:40 Coffee Break09:55 Alessandro Vespignani: Planning for Pandemic Outbreaks with Large Scale

Computational Models10:35 H. Eugene Stanley: Economic Fluctuations and Statistical Physics: Quanti-

fying Extremely Rare and Much Less Rare Events11:15 Coffee Break11:30 Guido Caldarelli: Statistical Properties of Credit Networks12:10 Frank Schweitzer: Mechanisms of Systemic Risk: Contagion, Reinforcement,

Redistribution12:50 Lunch Break14:15 Parallel Sessions15:05 Coffee Break15:20 Parallel Sessions17:10 Social Event: Boat Trip on Lake Zurich to Rapperswil

Thursday, June 11

09:00 Janusz Holyst: Two Models of Collective Firm Bankruptcies09:40 Coffee Break09:55 Mark Buchanan: Prediction and its Limits in Socio-Economic Systems10:35 Mike Bell: Managing Uncertainty in Transport Networks: Hyperpaths and

Games11:15 Coffee Break11:30 Kai Nagel: Microscopic Simulation of Tsunami-Related Evacuation of the

City of Padang12:10 Nicolas Geroliminis: Macroscopic Modeling of Traffic in Congested Cities:

Empirical Evidence and Analytical Derivations12:50 Lunch Break14:15 Parallel Sessions15:05 Coffee Break15:20 Parallel Sessions16:10 Coffee Break16:25 Kay Axhausen: Travel and social capital: Some empirical evidence17:05 Think Tank on Cascade Spreading

Friday, June 12

09:00 Matteo Marsili: Spiraling Toward Complete Markets and Financial Instability09:40 Coffee Break09:55 János Kertész: The Role of tie Strength in the Cohesion of the Society: A

Tribute to Mark Granovetter10:35 Ernst Fehr: The Weave of Social Life: How Social Interactions Shape the

Individual11:15 Coffee Break11:30 Sorin Solomon: How Do Economies Grow, How Do They Interact, How Do

They Fall, and How Do They Recover?12:10 Hans J. Herrmann: Robustness of Social Networks12:50 Lunch Break14:15 Parallel Sessions14:40 Coffee Break14:55 Plenary Presentation of Best Posters and Award

72 7. Events

7.1.3 International Workshop: ‘The Physics Approach To Risk: Agent-BasedModels and Networks’

Date and Location October 27–29, 2008, ETH Zurich

Website http://web.sg.ethz.ch/workshops/cost-p10/

Scope This workshop marked the transition between two COST actions: the COST-P10 ‘Physicsof Risk’ and the new COST-MP0801 ‘Physics of Competition, Cooperation and Conflicts’.The aim of the workshop was to join the European “activists” of COST-P10, to discussthe scientific outcome of this action in an open-minded atmosphere, to think about thechallenges of COST-MP0801, and to outline possible collaborations with Swiss colleagues.There were presentations from the three working groups of COST-P10: Physics of Risk,Agent-based Models, and Networks.

Monday, October 27: Financial Risk

08:30 Registration09:00 Frank Schweitzer, Zurich: Opening09:05 Peter Richmond, Dublin: COST actions and some efforts to try and avoid

costs aka the application of statistical mechanics to financial data09:50 Giulia Iori, London: Herding effects in order driven markets: the rise and fall

of gurusCoffee break

10:45 Michel Droz, Geneve: Motion of influential players can support cooperationin Prisoner’s Dilemma

11:15 Vyacheslav Yukalov, Zurich: Physics of risk and uncertainty in quantum de-cision theoryShort break

12:00 Georges Harras, Zurich: Endogenous versus exogenous origins of financialrallies and crashes in an agent-based model with Bayesian learning and imi-tation

12:30 Andrzej Nowak, Warsaw: Detecting financial bubbles via decoupling in agentbased modelsLunch

14:30 Stefano Battiston, Zurich: Can risk diversification be bad for systemic crises?15:00 Jan Lorenz, Zurich: Growth in coupled multiplicative stochastic processes

Coffee break16:00 Wolfgang Breymann, Winterthur: Quantifying financial return risk from

short time series16:30 Bence Toth, Torino: Limit order book dynamics around large events

Discussion

Tuesday, October 28: Agent-based Models

09:00 Christophe Deissenberg, Aix-Marseille: Learning benevolent leadership in aheterogeneous agents economy

09:45 Matteo Marsili, Trieste: Proliferation of financial instruments and systemicinstabilityCoffee break

10:45 Matus Medo, Fribourg: Diversification and limited information in the Kellygame

11:15 Bosiljka Tadic, Ljubljana: Why network’s mesoscopic inhomogeneity mat-ters?


Short break12:00 Tao Zhou, Fribourg: Extracting hidden information via heat conduction and

mass diffusion12:30 Damien Challet, Torino: Fat tails and long memory in programmer and trader

behaviorLunch

14:30 Anxo Sanchez, Madrid: Promotion of cooperation on networks? The bestresponse case

15:00 Dirk Helbing, Zurich: Spontaneous outbreak and breakdown of cooperationCoffee break

16:00 Sorin Solomon, Jerusalem: In very risky environments, altruists are favoredby natural selection

16:30 Rosario Nunzio Mantegna, Palermo: Specialization, strategic and herdingbehavior of market members in a financial market

17:00 Discussion17:44 Departure for the Social Event

Wednesday, October 29: Networks

09:00 Kimmo Kaski, Espoo: Complex social networks: analysis and modeling09:45 Albert Diaz-Guilera, Barcelona: Synchronization in complex networks

Coffee break10:45 Claudio Castellano, Roma: Contact process dynamics on annealed scale-free

networks11:15 Claudio J. Tessone, Zurich: Agents competing for high centrality in a volatile

environmentShort break

12:00 Hans Juergen Herrmann, Zurich: Modelling social networks12:30 Marco Laumanns, Zurich: Computational complexity of impact size estima-

tion for spreading processes on networksLunch

14:00 Internal Project Meetings: reserved for internal discussions of the Swiss par-ticipantsDeparture

74 7. Events

7.1.4 International Workshop: ‘Challenges and Visions in the Social Sciences’

Date and Location August 18–23, 2008, ETH Zurich

Website http://www.soms.ethz.ch/workshop2008

Scope The interdisciplinary workshop aimed at identifying future trends in the social sciences.Emphasis was given to visionary ideas of what will be the important topics over the next10 or 20 years. An aim was to formulate a list of hard, ambitious, and important problemsto be addressed by collaborative and international research projects. The workshop wasaccompanied by daily think tank discussions and a summer school for young scientists.

Monday, August 18

08:30 Registration10:00 Opening by Dirk Helbing10:15 Bernardo A. Huberman: Social attention11:15 Coffee Break11:30 Michael Macy: Agents and avatars: A new era of computational social science12:30 Dirk Helbing: Emergence of cooperation by social interactions in space and

time.13:00 Lunch Break14:30 Thilo Gross: Adaptive networks – The intriguing interplay of dynamics ON

and OF networksTutorial by Douglas Heckathorn: Respondent-driven sampling (part 1/3)

15:05 Imre Kondor: Correlations in complex systems16:00 Coffee Break15:40 Jeremy Hackney: Validation results from a multi-agent simulation of coupled

travel and social behaviorTutorial by Douglas Heckathorn: Respondent-driven sampling (part 2/3)

16:30 Elpida Tzafestas: Reactive components of (proto-)social behavior17:00 Coffee Break17:30 Frank Schweitzer: The costs of interaction and how they shape the social

network18:00 Short Break18:15 Think Tank / Discussion on Empirical and Experimental Challenges

Tuesday, August 19

09:00 Barkley Rosser: Econophysics and economic complexity09:55 Short Break10:00 Douglas Heckathorn: Respondent-driven sampling: Drawing statistically

valid samples of hard-to-reach populations10:55 Coffee Break11:10 Robert Axtell: Agentization: Paradigm for a computational foundation for

the social sciences Cash Machine12:05 Herbert Gintis: The bounds of reason Main building below the terrace13:00 Lunch Break14:30 Jacob Foster: The promise and peril of high-Taking the avatar approximation:

throughput social-science in virtual worldsTutorial by Douglas Heckathorn Respondent-driven sampling (part 3/3)

15:05 Peter de Haan: Agent-based simulation of emergence through monetary in-centives and social pressure

15:40 Coffee Break


16:00 Floriana Gargiulo: The saturation threshold of public opinion: Are aggressivemedia campaigns always effective?Tutorial by Anders Johansson Simulating the dynamics of human crowds(part 1/2)

16:30 Gabriel Istrate: Adressing the validation problem for social simulations17:00 Coffee Break17:30 Didier Sornette: Illusory and genuine control in optimizing games and finan-

cial markets18:00 Short Break18:15 Think Tank / Discussion on Modeling and Simulation Challenges

Wednesday, August 20

09:00 Michael Hechter: Alien rule and its discontents10:00 Coffee Break10:15 Neil F. Johnson: The dangers of time-aggregation – A little knowledge is a

dangerous thing: and sampling in social systems11:15 Coffee Break11:30 Andrzej Nowak: t.b.a.12:30 Maxi San Miguel: Group formation: Fragmentation transitions in network

coevolution dynamics13:00 Lunch Break14:30 Jose J. Ramasco: Human dynamics revealed through Web analytics

Tutorial by Anders Johansson: Simulating the dynamics of human crowds(part 2/2)

15:05 Samer Hassan Collado: Injecting Data into Simulation: Can Agent-BasedModelling Learn from Microsimulation?

15:40 Jörg Reichardt: Data driven vs. hypothesis-driven research in the socialsciences

16:15 Coffee Break17:30 Boat Trip incl. Social Dinner

Thursday, August 21

09:00 Luis A.N. Amaral: Measuring science: Fears, challenges and opportunities10:00 Coffee Break10:15 Nigel Gilbert: Are social simulations ready for the real world?11:15 Coffee Break11:30 Andreas Flache: Modelling integration in a diverse society: theoretical and

methodological challenges12:30 Sandro Rambaldi: Models and tools for the analysis of human behavior in

third generation metropolis13:00 Lunch Break14:30 Christophe Deissenberg: Constructing a very large agent-based macroeco-

nomic model: Challenge and solutionsTutorial by Pietro Speroni di Fenizio: Observing society through tags: Usingtags to help society (part 1/3)

15:05 Pierpaolo Andriani: Extremes & scale-free dynamics in organization science15:40 Coffee Break16:00 Armando Geller: Idiography and formalism – Incompatible or complementary

concepts?Tutorial by Pietro Speroni di Fenizio: Observing society through tags: Usingtags to help society (part 2/3)

76 7. Events

16:30 Carlos P. Roca: Understanding the effect of population structure on the evo-lution of cooperation

17:00 Short Break17:15 Lars-Eric Cederman: Nationalism and its geopolitical consequences: A dis-

tributional perspective.17:45 Coffee Break18:15 Think Tank / Discussion on Practical Challenges

Friday, August 22

09:00 Chris Snijders: Crunching numbers, crunching brains10:00 Coffee Break10:15 Peter S. Dodds: The emotional content of large-scale texts: The happiness of

bloggers, song lyrics, and presidents11:15 Coffee Break11:30 Carter Butts: Modeling reality without sacrificing data: Inferentially

tractable models for complex social systems12:30 Pietro Speroni di Fenizio: How should the 21st century democracy be orga-

nized? Studying online communities to answer it13:00 Lunch Break14:30 Ali Rana Atilgan: Document life cycle systems identify decision dynamics in

organizationsTutorial by Jörg Reichardt: Network analysis: A physics perspective (part1/2)

15:05 Tai-Yu Ma: A multiagent approach for dynamic daily activity pattern formu-lation

15:40 Coffee Break16:00 Talks of 3 best posters

Tutorial by Jörg Reichardt: Network analysis: A physics perspective (part2/2)

17:00 Coffee Break17:15 Andreas Diekmann &Wojtek Przepiorka: Signalling models and experiments.

A research perspective18:15 Workshop Closing by Dirk Helbing18:30 Think Tank / Discussion on Challenges of Interdisciplinary Research

Saturday, August 23

09:00 Tutorial by Pietro Speroni di Fenizio: Observing society through tags: Usingtags to help society (part 3/3)

10:0 Coffee Break10:15 Tutorial by Dirk Helbing: Mathematical modeling of behavioral changes by

pair interactions (part 1/2)11:15 Coffee Break11:30 Tutorial by Dirk Helbing: Mathematical modeling of behavioral changes by

pair interactions (part 2/2)13:15 Lunch


7.1.5 Satellite Workshop: ‘Enhancing Social Interaction: Recommendation Sys-tems, Reputation, P2P, Trust and Social Networks’

Date and Location October 5, 2007, Dresden, Germany

Website http://www.sg.ethz.ch/events/workshops/ECCS_satellite

Scope The one-day workshop was organized as a Satellite Workshop of the European Conferenceon Complex Systems (ECCS). Scientific Organization: Stefano Battiston (ETH Zurich),Nigel Gilbert (Univ. of Surrey), Stefano Leonardi (La Sapienza, Rome).

The workshop aimed at the challenges resulting from emerging technologies to enhancesocial interaction on the Internet. What can complex system theory offer to design socialinteractions in the Web 2.0, using concepts of trust, reputation, or P2P? Contributionsfocused on the design of mechanisms and incentives that can preserve the functionality ofsystems, or enhance robustness against non-cooperative behavior of free riding or maliciousagents.

October 5, 2007

08:30 Bernardo Huberman, HP, Palo Alto, Opening Lecture: Beyond Web 2.0Section 1: Reputation and P2P Networks

09:30 Filippo Menczer, Indiana University, 6S: Collaborative Web search via anadaptive peer network

10:00 Vinay Aggarwal, TU München, Utilizing ISP-P2P collaboration to enhancetrust in P2P systems

10:30 Coffee Break11:00 Geoffrey Canright, Telenor Research and Development, Self-mapping Net-

works11:30 David Hales, Univ. of Bologna, Emergent Networks as Distributed Reputa-

tion System12:00 Debora Donato, Yahoo Research, Barcelona, Efficient and Decentralized

PageRank Approximation in P2P Networks with Malicious Agents12:30 Lunch Break14:00 Stephan Schosser, Magdeburg Univ., How Competition Enforces Efficiency in

Structured P2P Systems14:20 Stefano Leonardi, Univ. La Sapienza, Roma, Metrics for Reputation Man-

agement in P2P networksSection 2: Collaborative Systems

14:50 Nigel Gilbert, Univ. of Surrey, Wikipedia as a site for the study of socialnorms

15:20 Guido Caldarelli, Univ. La Sapienza, Roma, Preferential attachment in thegrowth of social networks: the case of Wikipedia

15:50 Coffee Break16:20 Ciro Cattuto, Univ. La Sapienza, Roma, Stylized facts in collaborative tag-

ging systems16:50 Stefano Battiston, ETH Zurich, Trust Based Networks and Recommender

Systems17:20 Ben Markines, Indiana Univ, GiveALink: A socially constructed semantic

network for Web search and recommendation17:40 Ben Jann, ETH Zurich, Evolution of Cooperation on Anonymous Markets.

Empirical Findings From Internet Auctions18:00 Matus Medo, Fribourg Univ, Diffusion: a new approach to recommender

systems18:20 Open Discussion and Dinner

78 7. Events

7.1.6 International Workshop: ‘Trust-Based Networks and Robustness in Organ-isations’

Date and Location March 13–17, 2006, ETH Zurich

Website http://web.sg.ethz.ch/workshops/TW_Trust/

Scope This was the last thematic workshop of the European Network of Excellence (NoE)“Complex Systems” (EXYSTENCE). The aim of the workshop was to explore the possi-ble integration of existing models of trust-based networks and their further developmenttowards real-world oriented implementations. The workshop brought together a pool ofexperts in trust-based networks from social science, computer science, and physics. It wasstructured into two parts: a first part with presentations and general discussions and a sec-ond part with working groups focusing on specific topics. The results of the discussion werecompiled in a final report and submitted to the IST Program of the European Commissionto contribute to the design of future grant calls.

Monday, March 13

10:00 Frank Schweitzer, ETH Zurich, CH, Workshop Opening10:15 Berninghaus, Siegfried, University of Karlsruhe, D, Fairness and trust in net-

work formation11:00 Schosser, Stephan, University Karlsruhe, D, Incentives Engineering for Struc-

tured P2P Systems a Feasibility Demonstration Using Economic Experiments11:30 Coffee break12:00 Pitt, Jeremy, Imperial College London, UK, Aspects of Trust and Robustness

in Socio-Cognitive Grids12:30 Jurca, Radu, Ecole Polytechnique Fédérale de Lausanne, CH, Obtaining Hon-

est Feedback from Self-Interested Agents13:00 Lunch14:30 Haenni, Rolf, University of Bern, CH, Two-Layer Models for Managing Dis-

tributed Trust and Authenticity15:15 Despotovic, Zoran, DoCoMo Communications Laboratories Europe, D, On

reputation based computational models of trust in large decentralized envi-ronments

16:00 Coffee Break16:30 Paolucci, Mario, ISTC/CNR, I, Repage: REPutation and ImAGE Among

Limited Autonomous Partners17:00 Przepiorka, Wojtek and Wehrli, Stefan, ETH Zurich, CH, Trust and Reputa-

tion in Online Auctions

Tuesday, March 14

09:00 Helbing, Dirk, Dresden University of Technology, D, Information Flows inHierarchical Networks and the Capability of Organizations to SuccessfullyRespond to Failures, Crises, and Disasters

09:45 De la Rosa i Esteva, Josep Lluis, University of Girona, ES, Opinion-BasedFiltering Through Trust

10:30 Coffee Break11:00 Urbig, Diemo, Humboldt University Berlin, D, Split and bounded memory in

a statistical approach to trust and reputation11:30 Battiston, Stefano, ETH Zurich, CH, Impact of Trust on the Performance of

a Recommendation System in a Social Network


12:00 Laureti, Paolo, University of Fribourg, CH, Finding Ratings and Reputationswith Iterative Refinement

12:30 Lunch14:00 Plenary Working Group: Main Challenges in Trust-Based Networks and Con-

verging Roadmaps15:30 Coffee Break16:00 Ule, Aljaz, University of Amsterdam, NL, Trust and cooperation in endoge-

nous networks: evidence from human subject experiments16:30 Zeytinoglu, Hamza, Clarifix Ltd., UK, Building trust towards a vision in

evolution; continuous checks of utility and balancing input in a health-care/clinical informatics setting

17:00 Gulyas, Laszlo, AITIA International Inc and Lorand Eotvos University, HU,Cooperation’s Sensitivity to Network Structure

Wednesday, March 15

09:00 Usunier, Jean-Claude, University of Lausanne, CH, A Survey of Trust inPeer-to-Peer Networks

09:45 Zhang, Yi-Cheng, University of Fribourg, CH, Collective Evaluation, Practiceand Theory

10:30 Coffee Break11:00 Gordijn, Jaap, Vrije Universiteit, NL, Using value modeling for understanding

inter-organizational controls11:45 Triantafillou, Peter, RACTI and University of Patras, GR, Data Management

in Trust-based Networks12:30 Lunch14:00 Walter, Frank E, ETH Zurich, CH, Emergence and Evolution of Coalitions

in Buyer-Seller Networks14:30 Saggau, Volker, Institute of Agricultural Economics, D, Consumers and food

scares – an agent-based approach15:00 Schmitz, Dominik, RWTH Aachen, D, The TCD/SNet approach: A Modeling

and Simulation Environment for Trust-Based Inter-Organizational Networks15:30 Coffee Break16:00 Sornette, Didier, ETH Zurich, CH, Why do we have a big brain? Discrete

Hierarchical Organization of Social Group Sizes16:30 End of Part I, Remarks16:45 Working Groups

Thursday, March 16 and Friday, March 17

Working Groups

80 7. Events

7.1.7 DPG Summer School: ‘Dynamics Of Socio-Economic Systems: A PhysicsPerspective’

Date and Location September 18–24, 2005, Physikzentrum Bad Honnef, Germany

Website http://web.sg.ethz.ch/workshops/Summerschool05/

Scope The summer school reviewed the current development in modelling social and economicsystems by using methods originally developed in statistical physics and complex systemstheory. A broad spectrum of topics on economic, social, urban and cultural problemswas presented by leading experts. The school was organized as an official school of theGerman Physical Society (DPG) as the follow-up event of a Winter School held in Konstanz,Germany, in February 2004.

Sunday, September 18

14:00 Arrival18:00 Dinner (repeated daily at the same time)19:30 D. Stauffer, Microscopic models for financial markets

Monday, September 19

09:00 W. Breymann, Quantifying risk11:00 B. Rosenow, Random-matrix theory for financial markets14:30 W. Breymann, Multifractality in financial markets16:30 D. Stauffer, Cellular automata models for social interaction19:30 Welcome Evening

Tuesday, September 20

09:00 F. Slanina, Physics models of wealth distributions11:00 F. Schweitzer, Dynamics of Companies14:30 T. Brenner, Phase locking in economics: business cycles, fashions16:30 B. Rosenow, Liquidity (Talk)19:30 3 contributed talks (20 min + 10 min discussion)

M. Ludwig, Analyzing information propagation in social networksC. J. Tessone, System size stochastic resonance in a model for opinion forma-tionHang-Hyun Jo, The effect of mob psychology on the bystanders’ interventionin an emergency situation

Wednesday, September 21

09:00 F. Slanina, The minority game: theory and applications11:00 K. Nagel, Traffic models and regional simulations14:30 Excursion19:30 S. Bornholdt, Analyzing social networks

Thursday, September 22

09:00 M. San-Miguel, Dissemination of culture. More talks can be found here.11:00 G. Szabo, Cooperation and social dilemmas14:30 D. Helbing, Physical models of production networks16:30 3 contributed talks (20 min + 10 min discussion)


A. De Martino, Dynamics of adaptive agents with asymmetric informationC. Biely, Statistical Mechanics of the Ising Model on Evolving NetworksWoo-Sung Jung, Globalization of the Korean Stock Market

Friday, September 23

09:00 G. Szabo, Public Good Games11:00 D. Helbing, Simulating pedestrians14:30 M. San-Miguel, Models of opinion spreading16:30 F. Schweitzer, Urban growth models19:30 Farewell Dinner

Saturday, September 24

Departure

82 7. Events

7.1.8 DPG Annual Meeting “Physik seit Einstein”

Date and Location March 4–9, 2005, Berlin, Germany

Website https://www.dpg-physik.de/dpg/gliederung/fv/soe/veranstaltungen/Berlin2005/Berlin2005.html

Scope The conference was the last of a series of conferences organized by Frank Schweitzerbetween 2002 and 2005, when he was the chairman of the AKSOE, the devision for thephysics of socio-economic systems of the German Physical Society (DPG). The AKSOEconference of 2005 covered 5 invited talks and about 60 contributed talks and posters fromGerman and international scientists. The conference was part of the DPG annual meetingon condensed matter physics, which attracted more than 5.000 participants in total. Aparticular highlight of the AKSOE conference, as in previous years, was the ceremony ofthe ‘Young-Scientist Award for Socio- and Econophysics’ with the President of the RoyalSociety, Lord Robert May, giving the keynote talk.

7.2. Colloquia 83

7.2 Colloquia

SG Colloquia are held for a larger scientific audience. The guest speakers are leading experts inthe field so they invited for an overview talk about their recent major developments.

23.07.2008 Thomas Brenner, Philipps University Marburg, Germany

Measuring Regional Innovation Efficiency and the Impact of R&DEmployment and Networks

Regional innovation systems and the innovativeness of regions have becomefrequently discussed topics in recent years in science and policy making. Nev-ertheless, it remains unclear what innovativeness really means. We argue thatall that can be measured is an innovation efficiency in relation to one or a fewgiven innovation input factors. Different basic input factors, such as R&Demployment are discussed and used in this paper. It is shown how the resultsof measuring innovativeness depend on the choice of input factors. Finally, itis studied how networks impact on the innovativeness of regions.

07.05.2008 Geoffrey B. West, Santa Fe Institute, USA

Universal Scaling Laws in Biology From Genomes to Ecosystems

Despite its extraordinary diversity and complexity, many of life’s most fun-damental and complex phenomena scale with size in a surprisingly simplefashion. For example, basal metabolic rate scales approximately as the 3/4-power of mass over 27 orders of magnitude from molecular and intra-cellularlevels up to the largest multicellular organisms. Similarly, time-scales (suchas lifespans and growth-rates) and sizes (such as bacterial genome lengths,RNA densities, and tree heights) scale as simple power laws with exponentswhich are typically simple multiples of 1/4.The universality and simplicity of these relationships suggest that fundamen-tal constraints underly much of the coarse-grained generic structure and or-ganisation of living systems. It will be shown how these scaling relationshipsfollow from underlying principles embedded in the dynamical and geomet-rical structure of space-filling, fractal-like, hierarchical branching networks,presumed optimised by natural selection.These ideas lead to a general quantitative, predictive theory that potentiallycaptures the essential features of many diverse biological systems. Exampleswill include animal and plant vascular systems, growth, cancer, aging andmortality, sleep, cell size, genome lengths, DNA nucleotide substitution ratesand the concept of a universal molecular clock. Considerations of temperatureand the role of invariants will be discussed.

84 7. Events

23.07.2008 Geoffrey B. West, Santa Fe Institute, USA

Size matters! Growth, Innovation and the Pace of Life: is it Sus-tainable?

A general framework that has been successfully developed for understanding“universal” scaling laws across an extraordinarily wide spectrum of biologicalphenomena from cells to ecosystems will be extended to social organisationsin an attempt to reveal general principles of organization, structure and dy-namics. Almost all physiological traits, including fundamental quantities suchas metabolic rates, lifespans, growth and evolutionary rates, scale as simplepower laws whose exponents are typically simple multiples of 1/4. These havetheir origin in generic physical and geometric properties of the networks thatsustain life, leading to a general quantitative, predictive theory that capturesthe essential features of many diverse biological systems.We shall show that analogous scaling laws are manifested in social organisa-tions, suggesting that there are general principles of organization, structureand dynamics common, for example, to all cities reflecting a “universal” un-derlying social network structure.We shall address questions such as: are social organisations “just” an extensionof biology, and to what extent is a city, for example, a very large organism?These scaling laws have dramatic implications for growth, the pace of life,development and sustainability: innovation and wealth creation that fuel so-cial systems, if left unchecked, potentially sow the seeds for their inevitablecollapse.

20.12.2007 Luigi Marengo, Scuola Superiore S. Anna, Pisa, Italy

How much should society fuel the greed of innovators?

This lecture proposes a critical assessment of both the theory and the empir-ical evidence on the role of appropriability and in particular of IntellectualProperty Rights (IPRs) as incentives for technological innovation. I will startwith a critical discussion of the standard justification of the attribution of IPRin terms of “market failures” in knowledge generation, arguing that it is basedupon an incomplete or wrong picture of the dynamics of both technology andmarkets. Next, I examine the recent changes in the IPR regimes and theirinfluence upon the rates of patenting and the underlying rates of innovation.The evidence broadly suggests that, first, IPRs are not the most importantdevice apt to profit from innovation, and, second, their impact heavily de-pends upon industry- and firm-specific factors. Finally, some directions forfuture research on the consequences of appropriability on industrial dynamicswill be briefly outlined.

7.2. Colloquia 85

30.11.2007 György Szábo, Research Institute for Technical Physics and Materials ScienceBudapest, Hungary

Mechanisms supporting cooperation in evolutionary Prisoner’sDilemma games

The evolutionary Prisoner’s Dilemma games provide a general mathematicalbackground to study the maintenance of cooperative behavior among self-ish individuals. First the basic concepts and features of spatial evolutionarygames are surveyed. Our attention will be focused on the mechanism sup-porting cooperation when the payoff parameters, the connectivity structure,and the noise are varied. Finally we consider several ways how influentialplayers can enhance the level of cooperation.

19.09.2007 Raúl Toral, Spain Institute for Cross-Disciplinary Physics and Complex Sys-tems, Palma de Mallorca, Spain

Constructive effects induced by heterogeneity: an application to amodel for opinion formation

Heterogeneity amongst the members of a society or group is usually inter-preted to be a source of disorder, and this effect has to overcome by a couplingof sufficiently large magnitude if a collective behavior is to emerge from theensemble. For example, the rhythmic applause in a concert occurs despitethe intrinsic tendency of each individual to clap at a different pace than theothers. A similar example is that of fireflies that manage to flash in perfectsynchrony despite the unavoidable diversity in the individuals. In this talk, Iwill present conclusive evidence showing that different sources of heterogene-ity can induce, paradoxically, a resonant collective behavior in an ensembleof coupled bistable or excitable model systems. An application will be shownin the field of opinion spreading, where a simple majority-rule model is ableto follow optimally an external stimulus (advertising) under the presence ofthe right amount of heterogeneity amongst the constituents.

24.05.2007 Jörg Reichardt, University of Würzburg, Institute for Theoretical Physics,Germany

Analyses of economic networks using the toolbox of statisticalphysics

Over the last ten years, advances in information technology have allowed torecord and make available an avalanche of relational data, aka networks, thatdescribe the interactions of thousands of human agents in various contexts.These span from communications in large scale telephone connection data,email exchange, internet dating or social networking sites to various mar-kets, both online and offline, such as the eBay auction platform or the UNcommodity trade database.

86 7. Events

These data sets allow for quantitative studies of unprecedented detail andscope. At the same time, a number of new data analysis methods has beendeveloped that allow to gain insight into the structure of these data, someof which are inspired by methods from statistical physics. In my talk, I willpresent a framework for the detection of functional roles based on connectivitypatterns in (market) networks. The formalism merges the sociological con-cepts of structural and regular equivalence into a density based generalizationof block modeling. Using a first principles approach, I derive a measure forthe fit of a network to any given block model allowing objective hypothesistesting. From the generic properties of an optimal fit, I derive how to findthe best fitting block model directly from the data and present a criterionto avoid over-fitting. The method can handle both two-mode and one-modedata, directed and undirected networks as well as weighted networks andallows for different types of links to be dealt with simultaneously. It is non-parametric and computationally efficient. Example applications to marketresearch problems are given which show the accuracy and predictive power ofthis approach. Finally, I will show how methods from statistical physics canbe used to calculate the detection accuracy and establish universal bounds onwhat kinds of patterns can be detected independent of the algorithm used.

25.06.2007 Wander Jager, University of Groningen, Faculty of Economics and BusinessMarketing Institute, Netherlands

Multi agent simulation of human behaviour using psychological the-ory

Multi agent simulation is becoming a more standard methodological toolin exploring and experimenting with micro-macro dynamics of human be-haviour. Typically they focus on the dynamics that emerge when large groupsof people are interacting. Applications relate to market dynamics and in-novation diffusion, opinion dynamics, crowding, traffic control, evacuation,team composition and performance and financial markets, to name a few.Whereas in social psychological and sociological literature an abundance ex-ist of theories on social interaction processes, the formalised rules in thesesimulation usually range from very simple to very basic formalisations of the-ories. Whereas simplification is usually a necessary process in formalisingagent rules, generally a conceptual model that forms the theoretical startingpoint for a simplification process is lacking. In the presentation examples onconsumer behaviour and opinion dynamics will be presented to demonstratehow behavioral theory can be used in formalizing agent rules. Following thata perspective will be given concerning the challenges in formalizing socialinteraction and choice problems.

7.2. Colloquia 87

14.03.2007 Koen Frenken and Giorgio Fagiolo

Issues in Economic Geography

We propose an evolutionary framework based on stochastic growth that issufficiently general to investigate systematically a number of stylized facts ineconomic geography, while at the same time sufficiently flexible to be appliedin more specific industry and territorial contexts. We view economic develop-ment as a branching process generating an ever-increasing variety of productsin the economy. Assuming that firms grow by diversification through prod-uct innovations, firm size and city size distributions can be derived as twoaggregates resulting from a single evolutionary process. Gains from varietyat the firm level and the urban level provide the central feedback mechanismsin economic development generating strong path dependencies in the spatialconcentration of industries and the industrial specialization of cities.

21.04.2006 John Casti, The Wissenschaftzentrum Wien and The Kenos Circle Vienna,Austria

On the Limits to Scientific Knowledge

The story of mathematics in the latter half of this century has to a greatextent been the story of “nonexistence” theorems. Goedel’s results on theexistence of undecidable propositions in number theory and Turing’s proof ofthe undecidability of the Halting Problem in computer science are probablythe best-known results of this sort. They demonstrate the existence of math-ematical questions that are forever beyond the powers of the human mind toanswer by carrying out a computation.Do the same kind of nonexistence results apply to the seemingly more com-plex world of natural phenomena? In particular, are there important andinteresting scientific questions that defy rational analysis? Given the vastlymore complicated types of interactions in areas like physics, biology, and eco-nomics than what are found in the vastly simpler domain of arithmetic, itis not unreasonable to suspect that such unanswerable questions do indeedexist. If so, what are they like?This talk will explore the thesis that the method by which questions areanswered in science is to carry out a computation–either explicitly by meansof a computer program or implicitly by constructing a mathematical modelembodying an algorithmic relationship between the components of a givensystem. So when it comes to unanswerable scientific questions, we can sharpenthe issue considerably by asking whether there are questions that cannot beanswered by performing a calculation.Examples of candidate questions from biology, physics and economics willbe presented, along with a consideration of how the three worlds of observa-tions, mathematics and computation interrelate in addressing the question of“limits”.

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11.11.2005 Dirk Helbing, Institute for Transport and Economics, TU Dresden, Germany

A unifying approach to the dynamics of production, supply andtraffic networks

Production systems are complex multi-component systems which may sufferfrom instabilities and nonlinear dynamics, including chaos. This is caused bynon-linear interactions, delays, and fluctuations, which can trigger phenomenasuch as bullwhip or slower-is-faster effects.Network theory is recently changing our understanding of such systems. Smallchanges in the network structure can have major consequences for the dy-namic behavior of production systems.Based on a simple model of supply networks, we investigate instabilities andoscillations observed in economic and engineering systems. It turns out thatthe network structure of material flows itself is a source of instability, andcyclical variations are an inherent feature of decentralized adjustments. Thisalso suggests a new interpretation of business cycles.We further point out analogies to traffic flows on road networks, which canalso be treated as dynamic queuing systems. Methods recently developed fora decentralized, adaptive traffic light control are promising for a more efficientand flexible on-line production scheduling as well.

16.06.2005 Didier Sornette, Dept. of Earth and Space Sciences, University of California

Mechanism for and Detection of Pockets of Predictability in Com-plex Adaptive Systems

We first document a mechanism operating in complex adaptive systems lead-ing to dynamical pockets of predictability (“prediction days”), in which agentscollectively take predetermined courses of action, transiently decoupled frompast history.We demonstrate and test it out-of-sample on synthetic minority and majoritygames as well as on real financial time series. The surprising large frequencyof these prediction days implies a collective organization of agents and of theirstrategies which condense into transitional herding regimes.We then present a systematic algorithm testing for the existence of collec-tive self-organization in the behavior of agents in social systems, with a con-crete empirical implementation on the Dow Jones Industrial Average index(DJIA) over the 20th century and on Hong Kong Hang Seng composite index(HSI) since 1969. The algorithm combines ideas from critical phenomena,the impact of agents’ expectation, multi-scale analysis and the mathematicalmethod of pattern recognition of sparse data.Trained on the three major crashes in DJIA of the century, our algorithmexhibits a remarkable ability for generalization and detects in advance 8 othersignificant drops or changes of regimes. An application to HSI gives promisingresults as well. The results are robust with respect to the variations of therecognition algorithm.

7.2. Colloquia 89

22.04.2005 Bernardo A. Huberman, Hewlett Packard Laboratories, Palo Alto, CA, USA

Encouraging risk taking in organizations

While most organizations consider taking risks as an essential part of theirsuccess, few have actual policies to encourage risk taking on the part of theirmanagers. The reasons are manifold, ranging from the often stated fact thatwhat organizations reward is outcomes, not decisions, to more subtle assump-tions about risk taking that give it a negative connotation.I will present a new mechanism for encouraging risk taking within organiza-tions that relies on the provision of a form of insurance to managers. Sinceinsurance also increases the temptation to free ride on the efforts of others, wealso introduce a privacy preserving monitoring technique that mitigates thismoral hazard while relying on the social network of the insured individual.We show that under general conditions, three possible regimes exist. In thefirst one, managers contribute to production but avoid risky projects. In thesecond, members take on risky projects without free riding. In the third, theyfree ride. We establish the conditions for the appearance of each of theseregimes and show how one can adjust the payoffs so as to get the highestexpected payoff for the firm in spite of its risk-adverse individuals.

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7.3 Seminars

The SG Seminars are mostly given by external guests and cover all topics that are of interest toour team. They tend to focus on new research areas and results.

14.07.2009 Shin Jae Kyun, Yeungnam University, Gyeongsan, South KoreaInformation Accumulation System by Inheritance and Diffusion

22.09.2008 Julien Hendrickx, Université Catholique de Louvain, BelgiumLocal consensus in Hegselmann-Krause opinion dynamics model

06.08.2008 Thanasis Papaioannou, Athens University of Economics and Business,GreeceProviding Incentives for Honest Feedback in Electronic Environments

23.05.2008 Christian Müller, Institute for Computational Science, ETH ZurichParameter optimization and sensitivity analysis of an agent-based model insocial psychology

15.03.2008 Koen Frenken, Faculty of Geosciences, Utrecht University, NetherlandsGlobal optimisation of modular systems

21.02.2007 Mayuko Nakamaru, Tokyo Institute of Technology, JapanSpread of Two Linked Social Norms on Complex Interaction Networks

12.09.2006 Reinhard König, University of Karlsruhe (TH), GermanySimulation methods for spatial processes

06.09.2006 Claudio J. Tessone, Institut Mediterrani d’Estudis Avançats, Palma de Mal-lorca, SpainOpinion Spreading and Neighbourhood Models

11.04.2006 Werner Ebeling, Humboldt-Universität BerlinProbleme der Dynamik und stochastischen Theorie dissipativer Hamilton-scher Systeme

11.04.2006 Werner Ebeling, Humboldt-Universität BerlinDynamik von Innovationen in stochastischen Modellen

26.01.2006 Gabrielle WanzenriedAnsätze der Ökonometrie und ihre Anwendung auf Schweizer Firmendaten

16.01.2006 Nicole Saam, Universität Erfurt, GermanyModellierung intergouvernmentaler Verhandlungen – Gedankenexperimentoder empirische Sozialforschung?

7.4. CCSS Seminar 91

7.4 CCSS Seminar

The CCSS Seminar series ‘Modeling Complex Socio-Economic Systems and Crises’ is jointlyorganized by the six chairs which founded the Competence Center ‘Coping with Crises in Com-plex Socio-Economic Systems’ (CCSS). It focuses on an interdisciplinary scientific audience andfeatures presentations and subsequent discussions on the of modeling of socio-economic systems.

The courses take place every semester since 2008, as a mixture between a seminar primarilyfor postdocs and PhD students and a colloquium involving invited speakers. Participants getan overview of the state of the art in the field, in a well understandable way. From the presen-tations, they learn how novel mathematical models for open problems are developed, how theyare analyzed by means of computers, and how the results are defended in response to criticalquestions.

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Fall Semester 2009

22.09.2009 Moritz Hetzer, MTEC ETH (invited by: Didier Sornette)Altruistic punishment for human cooperation: A Darwinian perspective

29.09.2009 Michael Faber, IBK ETH (Kay W. Axhausen)HazNeth and risk research

06.10.2009 David Charypar, IVT ETH (Kay W. Axhausen)Continuous demand generation and simulation

13.10.2009 Sebastian Schutte GESS ETH (Lars-Erik Cederman)Estimating conflict zones in civil wars based on population, infrastructure,and terrain

20.10.2009 Stefania Vitali, MTEC ETH (Frank Schweitzer)The network of global corporate control

27.10.2009 Theo Arentze, TU Eindhoven (Kay W. Axhausen)Social networks and influences on activity-travel behavior

Christine Horne, Washington State University (Dirk Helbing)The Rewards of Punishment

03.11.2009 Michael Ghil, ENS Paris (Didier Sornette)Coupling climate and macroeconomic dynamics

10.11.2009 Nils Weidmann, Princeton (Lars-Erik Cederman)Promises and Pitfalls in the Spatial Prediction of Ethnic Violence

17.11.2009 Stefan Bornholdt, University of Bremen (Hans Jürgen Herrmann)In an economy of lemmings: Why the next crisis is just around the corner

24.11.2009 Jürgen Kurths, University of Potsdam (Frank Schweitzer)Synchronization and complex networks: Are such theories useful for earthand life sciences?

01.12.2009 Konstantin Klemm, University of Leipzig (Hans Jürgen Herrmann)Predator-prey dynamics with altruistic agents

08.12.2009 Tobias Galla, SOMS ETH (Dirk Helbing)Chaos and noise in game dynamical learning

Amin Mazloumian, SOMS ETH (Dirk Helbing)Congestion Spreading in urban Road Networks

15.12.2009 Cars H. Hommes, University of Amsterdam (Didier Sornette)More hedging instruments may destabilize markets

7.4. CCSS Seminar 93

Spring Semester 2009

17.02.2009 Fritz Busch, TU Munich (Kay Axhausen)Data capture, Information collation and traffic management

24.02.2009 Dirk Helbing, ETH ZurichOn the Emergence of Cooperation and the Collapse of Societies

03.03.2009 Carlo Jaeger, Potsdam Institute for Climate Impact Research (Dirk Helbing)Social Systems and Complexity

10.03.2009 Lucilla de Arcangelis, University of Napoli (Hans Hermann)Spam flooding of your mailbox

17.03.2009 Harald C. Gall, Giacomo Ghezzi, University of Zurich (Thomas Maillart)Collaborative software engineering

24.03.2009 Guido Caldarelli, CNR-INFM, Italy (Frank Schweitzer)A Schroedinger-like equation for the PageRank

31.03.2009 Alex Saïchev, ETH Zurich (Didier Sornette)Theory of Zipf’s Law

07.04.2009 Francesco Ciari, Andreas Horni, ETH Zurich (Kay Axhausen)Current Work on Matsim

21.04.2009 Paolo Tasca, ETH Zurich (Frank Schweitzer)Information mirages and systemic risk in financial markets

05.05.2009 Manfred Milinski, MPI for Evolutionary Biology (Frank Schweitzer)Evolutionary economy and the climate crisis game

12.05.2009 Lubos Buzna, Univ. of Zurich, Wenjian Yu, ETH Zurich (Dirk Helbing)Modeling and simulation of conflicts in migration games

19.05.2009 Georges Harras, ETH Zurich (Didier Sornette)Stochastic resonance and the excess volatility puzzle in financial markets

Wanfeng Yan, ETH Zurich (Didier Sornette)Systematic tests of the LPPL model for financial bubbles and crashes

26.05.2009 Kamil Mizgier, ETH Zurich (Dirk Helbing)Modeling defaults of companies in multi-stage supply chain networks

Thomas Maillart, ETH Zurich (Dirk Helbing)Beyond Shannon: Characterizing Internet Traffic with Generalized EntropyMetrics

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Fall Semester 2008

16.09.2008 Adilson Motter, Northwestern UniversityCollective dynamics in complex networks of dynamical systems

30.09.2008 Michael Maes, Rijksuniversitet GroningenHomophily, persuasion and opinion polarization. laboratory experiments onpolarization in small groups

07.10.2008 Carlos Perez Roca, CSIC, SpainUnderstanding the effect of population structure on the evolution of cooper-ation. A systematic study of binary social dilemmas

14.10.2008 David Charypar, ETH ZurichParallel event-driven queue-based traffic flow microsimulation

21.10.2008 Marc Barthelemy, CEA ParisModeling and measuring city formation and growth

28.10.2008 Slava Yukalov, Dubna, RussiaQuantum theory of decision making: A solution for 9 different paradoxes

04.11.2008 Domenico Delli Gatti, Cath. University of MilanoEmergent macroeconomics: An agent-based perspective

11.11.2008 Stefano Battiston, ETH ZurichSystemic Risk

Albert Diaz-Guilera, University of BarcelonaSystemic risk – Dynamics on complex networks: information flow andsynchronization

18.11.2008 Julian Wucherpfennig, ETH ZurichPath Dependence in Civil Wars

25.11.2008 Aaron Clauset, Santa Fe InstituteStatistical Patterns in Global Terrorism

02.12.2008 Gabriele Tedeschi, Polytechn. University of MarcheHerding effects in order driven markets: The rise and fall of gurus

Christian Schneider, ETH ZurichScale-free network robustness to attack

09.12.2008 Thomas Maillart, ETH ZurichFitting power laws probability distributions and social dynamics

Heiko Rauhut, ETH ZurichA research perspective on social norms and conflict

8 Services

8.1 Journals

The nomination as an editor for a scientificjournal is certainly a public recognition of thescientific reputation of a person. But in returnfor the honour, one has to make a strong com-mitment to serve for the journal in various re-spects, such as handling manuscripts, choosingreferees, evaluating the papers and the refereereports, communicating with authors, settlingthe various cases of disagreement and discon-sent between authors and referees. This takestime, but requires also a profound knowledgeabout the recent achievements in the area, up-coming trends, etc.

The situation becomes even more demand-ing for the editor-in-chief who in addition alsotakes responsibility for the journal’s scientificprofile and attractiveness to authors, its furtherdevelopment and standing in the competitive

market of scientific publications.

On the other hand, it is great a opportu-nity to shape the future of such a journal, byaddressing the right trends at the right time, byattracting the right authors, and by making thejournal known for its distinct profile and qual-ity. We are very happy to have such differentopportunities as listed below.

The particular emphasis is on journals witha strong interdisciplinary profile, which allowresearchers with different scientific backgroundto publish in the same journal for the mutualbenefit of the contributing areas. The impactof such a ‘bridge’ cannot be simply measured innumbers such as the impact factor, rather thevalue is seen in fostering the collaboration of amultidisciplinary community.

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8.1.1 European Physical Journal B: Condensed Matter and Complex Systems

Publisher EDP Sciences, Societa Italiana di Fisica, Springer

Website http://epjb.edpsciences.org/

The journal EPJ-B is published twice a month,i.e. with 24 issues per year it is a major jour-nal in the field. It consists of two sections:Condensed Matter (Experimental/Theoretical)and Complex Systems. Frank Schweitzer is theEditor-in-Chief for the latter one since January2007, and his current contract ends by the endof 2011.

The Complex Systems Section was estab-lished with the nomination of Frank Schweitzer,who has defined the profile and has chosen the15 editors currently working for this section.At present, EPJ-B receives about 1000 submis-sions per year, roughly half of them for theComplex Systems Section. Frank Schweitzerlooks into every single paper for the ComplexSystem Section, decides about desk rejections,and assigns the papers to suitable handling ed-itors. He further handles personally most ap-peals of authors. The acceptance rate of EPJ-Bwas about 31 percent in 2008.

Overview of Sections and Topics

I. Condensed Matter (Luciano Colombo, Alois Loidl)

A: Solid State and Materials∗ Lattice structure and dynamics∗ Inhomogeneous and disordered systems∗ Glasses and amorphous materials∗ Phase transitions∗ Quantum solids and liquids∗ Electronic structure and dynamics∗ Low-dimensional systems∗ Strongly correlated electron systems∗ Surfaces and interfaces∗ Thin films, multilayers and superlattices∗ Semiconductors∗ Magnetic structure and dynamics∗ Random magnetism and frustrated magnets∗ Quantum spin systems∗ Spin-dependent transport∗ Spin electronics∗ Superconductivity

B: Mesoscopic and Nanoscale Systems

8.1. Journals 97

∗ Clusters and nanotubes∗ Quantum dots and wires∗ Quantum coherence∗ Nanoelectronics∗ Optoelectronics∗ Nanomechanics

C: Computational Methods

∗ Ab initio electronic structure calculations or similar∗ Quantum Monte Carlo∗ Atomistic simulations∗ Multiscale modelling

II. Complex Systems (Frank Schweitzer)

D: Statistical and Nonlinear Physics

∗ Fluctuation phenomena and stochastic processes∗ Phase transitions and critical phenomena∗ Discrete dynamics, chaos and adaptive control∗ Time series analysis∗ Non-equilibrium dynamics, pattern formation∗ Physics of networks∗ Hydrodynamic stability and transition∗ Turbulence

E: Interdisciplinary Physics

∗ Information theory, combinatorial optimization∗ Multi-agent systems, selforganization and emergence∗ Collective phenomena in biological systems∗ Ecological and population dynamics∗ Environmental systems, hazards and risks∗ Traffic, infrastructures and urban dynamics∗ Dynamics of groups and organizations∗ Opinion dynamics∗ Economic models, evolutionary game theory∗ Financial markets, econophysics∗ Information, social and economic networks

Editorial board

• Editors-in-ChiefLuciano Colombo, University of Cagliari, ItalyAlois Loidl, Universität Augsburg, GermanyFrank Schweitzer, ETH Zürich, Switzerland

• Editorial BoardLuís Amaral, Northwestern University, Evanston, IL, USALuca Biferale, Università Di Roma Tor Vergata, ItalyHélène Bouchiat, Université Paris-Sud, Orsay, FranceBernd Büchner, Leibniz-Institut für Festkörper- und Werkstoffforschung Dresden, Germany

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Silke Bühler-Paschen, Technische Universität Wien, AustriaMaria Gabriella Castellano, Instituto di Fotonica e Nanotecnologie, CNR, Rome, ItalyDebashish Chowdhury, Indian Institute of Technology, Kanpur, IndiaFabrizio Cleri, Université des Sciences et Technologies de Lille, FranceLeticia Cugliandolo, École Normale Supérieure, Paris, FranceJean Daillant, CEA, Saclay, FranceRosa Di Felice, Natl. Center S3 of INFM-CNR, Modena, ItalyTiziana Di Matteo, King’s College London, United KingdomHelmut Eschrig, Leibniz-Institut für Festkörper- und Werkstoffforschung Dresden, Ger-manyMilena Grifoni, Universität Regensburg, GermanyRudolf Gross, Bayerische Akademie der Wissenschaften, Garching, GermanyEduardo Hernandez, Institut de Ciencia de Materials de Barcelona (ICMAB-CSIC), SpainUlrich Hohenester Karl-Franzens-Universität Graz, AustriaJanusz Holyst, Warsaw University of Technology, PolandKatalin Kamarás, Research Institute for Solid State Physics and Optics, Budapest, Hun-garyHolger Kantz, Max Planck Institute for the Physics of Complex Systems, Dresden, Ger-manyBernhard Keimer, Max-Planck-Institut für Festkörperforschung, Stuttgart, GermanyÅke Kvick, Lund University, SwedenMichael Lang, Johann-Wolfgang-Goethe-Universität, Frankfurt, GermanyKristian Lindgren, Chalmers University of Technology, Göteborg, SwedenFabio Marchesoni, University of Camerino, ItalyMatteo Marsili, The Abdus Salam International Centre for Theoretical Physics, Trieste ,ItalyStéphane Nonnenmacher, CEA Saclay, FranceJean-François Pinton, Ecole Normale Supérieure, Lyon, FranceRaimund Podloucky, Universität Wien, AustriaRanko Richert, Arizona State University, Tempe, AZ, USAAchim Rosch, Universität zu Köln, GermanyMaxi San Miguel, Universitat de les Illes Balears, Palma de Mallorca, SpainLutz Schimansky-Geier, Humboldt Universität, Berlin, GermanyKurt Schönhammer, Universität Göttingen, GermanyManfred Sigrist, ETH Zürich, SwitzerlandAttilio L. Stella, Università degli studi di Padova, ItalyHenryk Szymczak, Polish Academy of Sciences, Warsaw, PolandAlessandro Vespignani, Indiana University, Bloomington, IN, USAAlexander Volokitin, Samara State Technical University, Russia

8.1. Journals 99

8.1.2 ACS – Advances in Complex Systems

Publisher World Scientific, Singapore

Website http://www.worldscinet.com/acs/

The journal ACS is a classic in the area of com-plex systems research. It was established in1997, and Frank Schweitzer was an editor ofACS since the very beginning. He became theeditor-in-chief of ACS in the end of 2006 andtook responsibility for a major overhaul of thejournal. Changes involved a complete new ed-itorial board of 20 editors, a refined aims andscope, a faster publishing schedule and, afterall, a completely new cover layout.

Ever since, ACS has grown both in visibil-ity and reputation and is now considered a ma-jor outlet for truely interdisciplinary research.Different from EPJ-B, which is still a physicsjournal, ACS, in addition to physics and math-ematics, also covers computer sciences, biolog-ical systems, social and economic systems, andtraffic and environmental systems. ACS is pub-lished as a bi-monthly journal, the 6 issues con-tain more than 50 papers per year. Currently,ACS receives about 160 submissions per year,with an acceptance rate of about 30 percent.

Notably, ACS publishes topical sections onhot interdisciplinary topics such as ‘LanguageDynamics’ or ‘Artificial Life’, but also topi-cal issues from major conferences in the areaof complex systems research. For example,the European Conference on Complex Systems(ECCS) and the European Social SimulationAssociation (ESSA) publish topical issues inACS on a regular basis.

Aims and Scope

ACS – Advances in Complex Systems aims toprovide a unique medium of communication formultidisciplinary approaches, either empiricalor theoretical, to the study of complex sys-tems. The latter are seen as systems com-prised of multiple interacting components, oragents. Nonlinear feedback processes, stochas-tic influences, specific conditions for the sup-ply of energy, matter, or information may leadto the emergence of new system qualities onthe macroscopic scale that cannot be reducedto the dynamics of the agents. Quantitativeapproaches to the dynamics of complex sys-

tems have to consider a broad range of con-cepts, from analytical tools, statistical methodsand computer simulations to distributed prob-lem solving, learning and adaptation. This isan interdisciplinary enterprise.

The goal of ACS, therefore, is to promotecross-fertilization of ideas among all the scien-tific disciplines having to deal with their owncomplex systems. These include, but are notlimited to, biology, physics, engineering, com-puter sciences, economics, cognitive science andthe social sciences. It is in fact the exchange ofconcepts and techniques developed within ar-eas as diverse as spin glass physics, game the-ory, molecular biology, evolutionary optimiza-tion, or psychology – which has proven itselfto be a major driving force in complex systemsresearch.

ACS predominantly publishes original researcharticles in the field of complex systems andencourages submissions of papers which resultfrom collaborations across traditional academicdisciplines. As a peer-reviewed journal, ACS iscommitted to the highest scientific standards.

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Papers published in ACS should be written in away that makes them accessible to a wide rangeof scientific disciplines. For details, please seethe Guidelines for Contributors in this journal.

To encompass all aspects in the field of com-plex systems, papers in ACS are organized intofive research sections, each of which is handled

by a section editor. The list below – which isneither complete nor exclusive – gives some in-formation about the possible topics covered ineach section. This demonstrates the position ofACS as a truly multidisciplinary scientific jour-nal in the field of complex systems research.

Overview of Sections and Selected Topics

1. Physics and Mathematics

• Fluctuation phenomena and stochastic processes

• Phase transitions

• Pattern formation, non-equilibrium systems

• Many-particle systems

• Discrete dynamics and chaos

2. Computer Sciences

• Learning and adaptation

• Distributed/evolutionary optimization and control

• Multi-agent coordination

• System complexity signatures, computational complexity

• Computational methods for large-scale simulations

3. Biological Systems

• Multicellular phenomena, systems biology

• Ecological and population dynamics

• Molecular networks

• Neurons and cognition

• Genomics

4. Social and Economic Systems

• Dynamics of organizations and collective decision processes

• Evolutionary game theory, cooperation

• Economic growth and innovation dynamics

• Financial markets, econophysics

• Social and economic networks

5. Traffic and Environmental Systems

• Emergent properties of traffic flow and travel behavior

• Urban growth and land use models

• Simulation of built and non-built environments

• Critical infrastructure

• Environmental change

8.1. Journals 101

Editorial Board

• Editor-in-ChiefFrank Schweitzer, Chair of Systems Design, ETH Zurich, Switzerland

• Section EditorsPhysics and Mathematics: Neil Johnson, Physics Department, University of MiamiComputer Science: Kagan Tumer, Mechanical Engineering Department, Oregon State Uni-versityBiological Systems: Peter F. Stadler, Interdisciplinary Centre for Bioinformatics, Univer-sity of Leipzig, GermanySocial and Economic Systems: Frank Schweitzer, Chair of Systems Design, ETH Zurich,SwitzerlandTraffic and Environmental Systems: Kai Nagel, Institute of Land and Sea Transport Sys-tems, Technical University of Berlin, Germany

• Associate EditorsJohn Abraham, University of Calgary, CanadaAna L. Bazzan, Universidade Federal do Rio Grande do Sul (UFRGS), BrazilArmando Bazzani, University of Bologna, ItalyIain D. Couzin, University of Oxford, UK and Princeton University, USAGuillaume Deffuant, Cemagref, FrancePeter Dittrich, Friedrich Schiller University Jena, GermanyVíctor M. Eguíluz, Universitat de les Illes Balears, SpainGiorgio Fagiolo, Sant’Anna School of Advanced Studies, ItalyAndreas Flache, University of Groningen, The NetherlandsMarc-Thorsten Hütt, Jacobs University Bremen, GermanyMarkus Kirkilionis, University of Warwick, UKKonstantin Klemm, Leipzig University, GermanySven Koenig, University of Southern California, USACharlotte K. Hemelrijk, University of Groningen, The NetherlandsMichael Meyer-Hermann, Frankfurt Institute for Advanced Studies (FIAS), GermanyDaniel Polani, University of Hertfordshire, UKChristian Reidys, Nankai University, ChinaMatteo G. Richiardi, Universita’ Politecnica delle Marche, ItalyEnrico Scalas, Universita’ del Piemonte Orientale, ItalyStefan Thurner, Medical University of Vienna, Austria

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8.2 Boards and Program Committees

Editorial Boards (Frank Schweitzer)

• Journal of Economic Interaction and CoordinationSpringer Berlin HeidelbergEditor - 2005–

• International Journal of Modern Physics CWorld Scientific Singapore Associate Editor - 2005–

• Springer ComplexitySpringer Berlin HeidelbergEditorial and Program Advisory Board - 2006–

• Springer Series in SynergeticsSpringer Berlin HeidelbergEditorial and Program Advisory Board - 2004–

Advisory Boards (Frank Schweitzer)

• German Physical Society (DPG)Council - 2006–2009

• Society for Economic Science with Heterogeneous Interacting AgentsCouncil, 2006–

• Complex Agent-Based Dynamic Networks (CABDyN)International Management Board, 2005–

Program committees of conferences (Frank Schweitzer)

• Econophysics Colloquium 2010Taipei, Taiwan, November 04–06, 2010

• Econophysics Colloquium 2009Erice, Italy, Oktober 26–31, 2009

• 6th European Social Simulation Association Conference (ESSA 2009)Guildford, UK, September 14–18, 2009

• Winter Workshop on Economics with Heterogeneous Interacting Agents (WEHIA 2008)Taoyuan, Taiwan, December 05–07, 2008

• Agent Based Spatial Simulation (ABS2)Paris, France, November 24–25, 2008

• GWAL-8: 8th German Workshop on Artifical LifeLeipzig, Germany, July 30–August 01, 2008

• Econophysics Colloquium 2008Kiel, Germany, August 28–29, 2008

• 4th International Conference in Statistical Physics - SigmaPhiCrete, Greece, July 14–18 , 2008

• 2nd World Congress on Social Simulation (WCSS-08)Washington DS, USA, July 14–17 , 2008

• Conference on Economic Science with Heterogenous Interacting Agents (ESHIA 08)Warsaw, Poland, June 19–21, 2008

8.3. Reviews 103

• 3rd International Workshop on Emergent Intelligence on Networked Agents (WEIN’08)7th International Joint Conference on Autonomous Agents and Mulitagent Systems (AA-MAS 2008)Estoril, Portugal, May 12–13, 2008

• Conference Data in Complex Systems within the European Coordination Action GIACS(General Integration of the Applications of Complexity in Sience)Palermo, Italy, April 07–09, 2008

• Workshop on Heterogeneous Agent Systems and Complex NetworksECCS’07 - European Conference of Complex SystemsDresden, Germany, October 04, 2007

• ECCS’07 - European Conference of Complex SystemsDresden, Germany, October 01-05, 2007

• Econophysics Colloquium 2007Ancona, Italy, September 27–29, 2007

• The Fourth European Social Simulation Association Conference - ESSA’07Toulouse, France, September 10–14, 2007

• SPIE International Symposium Complex Systems IICanberra, Australia, December 04-07, 2007

• Econophysics Colloquium 2006 Tokyo, Japan, November 23–25, 2006• ECCS’06 - European Conference of Complex Systems

Oxford, UK, September 25–29, 2006• GWAL-7: 7th German Workshop on Artificial Life

Jena, Germany, July 26–28, 2006• DCC’06: Second International Conference on Design Computing and Cognition

Eindhoven, Netherlands, July 10–12, 2006• 4th Workshop on Agents in Traffic and Transportation (ATT)

Fifth International Joint Conference on Autonomous Agents and Multi-agent Systems (AA-MAS 2006)Hakodate, Japan, May 09, 2006

• Workshop on Emergent Intelligence on Networked Agents (WEIN 2006) 5th InternationalJoint Conference on Autonomous Agents and Multi-agent Systems (AAMAS 2006)Hakodate, Japan, 8 May, 2006

• ECCS’05 - European Conference of Complex SystemsParis, France, November 14–18, 2005

8.3 Reviews

As a service to the scientific community, we write quite a number of reviews on submissionsfor journals and conferences. This activity involves all members of our team and is supervisedby Frank Schweitzer. Certainly, it is very time consuming, but it also helps to learn about thestrengths and weaknesses in scientific publications and gives a good overview of recent researchtrends. So far, around 200 reviews were prepared by members of our team:

2009 24 reviews2008 22 reviews2007 34 reviews2006 89 reviews2005 22 reviews

104 8. Services

8.4 Software

8.4.1 Cuttlefish: Dynamic Network Visualization

At the Chair of Systems Design, networks from very different domains are analyzed: economicnetworks, social networks, biological networks, networks of software dependencies, and networksin technological systems. This variety poses a challenge, as the classical network analysis toolsdo not account for the idiosyncrasy of each network type. A second challenge results the factthat most of these networks exhibit a highly heterogeneous degree distribution. Classical forcedirected layout algorithms, such as the spring-layout, do not cope very well with such a topologyas the Figure shows. Highly connected clusters are compressed to undifferentiated lumps andnodes with low degrees spread out disproportionally.

We have addressed these challenges by developing our own tools, for instance a new variationof force-directed layout: ARF uses a different set of force equations than the spring-layoutalgorithm. The new equations balance attraction and repulsion and thus distribute also highlyheterogeneous networks well in the layout space, as the comparison of Figure 8.1 shows.

Figure 8.1: Scale-free network visualized with the Kamada-Kawai and the ARF algorithm

Another new tool developed at the Chair of Systems Design is the network analysis toolCuttlefish. It offers a versatile plug-in mechanism which allows users to extend its functionality.This way Cuttlefish adapts to the problem domain like the animal from which it borrowed thename. Furthermore, Cuttlefish is a a platform independent open source project in SourceForge,released under GNU General Public License. Its code and documentation are available for thewhole community and the participation of external users and developers is encouraged.

Its high extensibility and multi-purpose features make Cuttlefish an useful tool also for re-searchers outside of the Chair of Systems Design. GraphML and pajek files can be loaded, aswell as file formats for special visualization, or file formats for changes taking place in the net-work. Cuttlefish creates figures from snapshots and tex files using PSTricks or Tikz for scalablegraphics. Eventually, Cuttlefish allows browsing a large network stored in a database by applyingfilters to the nodes and edges displayed. In addition to ARF, other well-known layout algorithmsare implemented, such as Kamada-Kawai, Fruchterman-Reingold, Spring and ISOM.

Selected Publications

• Geipel, Markus Michael: Self-Organization applied to Dynamic Network Layout, InternationalJournal of Modern Physics C vol. 18, no. 10 (2007), pp. 1537-1549.

• Cuttlefish: http://sourceforge.net/projects/cuttlefish/

8.4. Software 105

8.4.2 Working paper series

For the Competence Center “Coping with Crises in Complex Socio-Economic Systems” (CCSS),it is important to broadly disseminate its output to the scientific comunity. In particular, thereshould be a technical framework to efficiently host the research papers produced by the Chairsinvolved in the CCSS and to allow an automated aggregation of the respective information foractivity reports, etc. As a deliverable for the CCSS, our Chair has developed a Working PaperSeries portal, which meets these requirements, which can be found at: http://web.sg.ethz.ch/wps.

The WPS portal allows members of the CCSS to upload recent papers they want to sharewith other scientists. Their submissions become instantaneously available thought this portal,but also get automatically indexed in two leading social science repositories: RePEc [1] and SSRN[2]. RePEc (Research Papers in Economics) is a collaborative effort of hundreds of volunteersin 69 countries to enhance the dissemination of research in economics. The heart of the projectis a decentralized database of working papers, journal articles and software components. AllRePEc material is freely available. On the other hand, Social Science Research Network (SSRN)is devoted to the rapid worldwide dissemination of social science research and is composed of anumber of specialized research networks in each of the social sciences. This way, our researchpapers become immediately available for the larger scientific community.

From a technical standpoint, the WPS portal was developed in the open source Django webdevelopment framework [3]. Django enables development of database driven web sites, and it isbased on the Python language [4].

Figure 8.2: Snapshot of the CCSS Working Paper Series as of 20.10.2009.

References

1. RePEc web site: http://www.repec.org2. SSRN web site: http://www.ssrn.com3. Django web site: http://www.djangoproject.com4. Python web site: http://www.python.org

Preface - Chair of Systems Design - ETH Z¼rich

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