Participating and Anticipating Actors and Agent Networks. Social ComputingGordana Dodig CrnkovicProfessor of Computer ScienceMälardalen University, School of Innovation, Design and Engineering

gordana.dodig-crnkovic@mdh.se

Social Networks: from communication to solidarity (an interdisciplinary approach) Fundación Sierra-Pambley, León (Spain)Understanding complexity: systems, emergence and evolutionLeón, January 28 2014

Mälardalen University, Sweden

Computing is becoming ubiquitous and essential part of human society. As participants in a major technological and cultural change caused by ICT, we want to be able to understand ongoing processes and anticipate future possibilities. When we introduce changes in the society we want to know the possible outcomes. That is the goal of social computing.

Very often programs of social change envisage certain goals, but it is not at all clear how they can be reached. Unfavorable development can result from lack of understanding of possible consequences of interventions, decisions and other changes in social systems.

Abstract

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As social agents we have intuitions on the interpersonal level with focus on individual people. Computational techniques can augment our understanding on a social level, where we have limited everyday experiences.

In this talk I will present an example of the analysis of city as a socio-computational system.

Abstract

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Two aspects of social computing

One important factor in human project of development of prosperous global society is understanding of the behavior of social systems. Computing as a method provides means for this study in a form of computational models and simulations that are being developed.

There are two different types of social computing (Wang et al. 2007), centered on its two different aspects:1. computing mechanisms and principles with computational modeling of groups of agents exchanging information in networks and 2. human aspects of social computing (critical theory), with social side of social web applications such as blogs, wikis, social bookmarking, instant messaging, and social networking sites and crowdsourcing. This lecture focus on computational aspects of social computing and its relation to models of computing as information processing.

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Even though computers were invented in order to automatize calculations [Hilbert program (1900); Turing Machine (1936)], after a while the importance of the computer as a communication device was recognized, with its important consequent shared knowledge and community-building (Licklider and Taylor 1968).

Hilbert, David, 1900, “Mathematische Probleme”, Nachrichten von der Königlichen Gesellschaft der Wissenschaften zu Göttingen, Math.-Phys. Klasse, 253-297. Lecture given at the International Congress of Mathematicians, Paris, 1900.

Turing AM. On Computable Numbers, with an Application to the EntscheidungsproblemÊ. Proceedings of the London Mathematical Society, series 2, 1936; 42:230-265.

Licklider, J.C.R. and Taylor R. W. (1968) The computer as a communication device. Science and Technology (September), 20-41.

Computer as a communication device

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Social computing with the focus on social is a phenomenon which enables extended social cognition, while the Social computing with the focus on computing is about computational modelling and it is a new paradigm of computing used for understanding underlying mechanisms.

From information communication to social intelligence

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Social network

Protein interaction network in human cell Human neural network

Conceptual Basis: Network Modells

Evolutionary network

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http://www.google.com/insidesearch/features/search/knowledge.html Google Knowledge Graphhttp://press.emerson.edu/imc/files/2011/12/social-network.jpg

Human groups are information processing networks and knowledge generators

Networks of networks of information – complexity

Computational approaches to complex systems can be descriptive or generative models

Generative models answer the question: How does the complexity (emergent properties) arise?

Evolution is the most well known generative mechanism for generating increasingly complex systems (organisms).

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In a complex system, what we see is dependent on where we are and what sort of

interaction we use to study the system.

http://www.morphwize.com/company/index.php?option=com_k2&view=itemlist&task=tag&tag=complex+system+solution

Complex systems definition

“ … systems that comprise many interacting parts with the ability to generate a new quality of collective behaviour through self-organization, e. g. the spontaneous formation of temporal, spatial or functional structures. They are therefore adaptive as they evolve and may contain self-driving feedback loops. Thus, complex systems are much more than a sum of their parts. Complex systems are often characterized as having extreme sensitivity to initial conditions as well as emergent behaviours that are not readily predictable or even completely deterministic.”

Robert Meyers, Encyclopaedia of Complexity and Systems Science (2009)

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Social networks (SN) are social structures made of nodes

(which are, generally, individuals or organizations) that are

tied by one or more specific types of interdependency, such

as values, visions, ideas, financial exchange, friends, kinship,

dislike, conflict, trade, web links, disease transmission, etc.

Social Networks

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Social networks analysis plays an important role in studying the way specific problems of social groups are solved (organizational, economic, ecological, medical, epidemiological etc.) and the ways in which individuals within socials groups succeed in achieving their goals.

Social networks analysis has addressed also the dynamics issue, called dynamic networks analysis. This is an emergent research field that brings together traditional social network analysis, link analysis and multi-agent systems.

Social Networks

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Study of networks regularities:Laszlo Barabasi

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See the work of Albert-László Barabási who studies networks on different scales.http://www.barabasilab.com/pubs-talks.php http://www.youtube.com/watch?v=10oQMHadGos

Complex networks have been studied extensively owing to their relevance to many real systems such as the world-wide web, the Internet, energy landscapes and biological and social networks. A large number of real networks are referred to as ‘scale-free’ because they show a power-law distribution of the number of links per node.

However, it is widely believed that complex networks are not invariant or self-similar under a length-scale transformation. This conclusion originates from the ‘small-world’ property of these networks, which implies that the number of nodes increases exponentially with the ‘diameter’ of the network, rather than the power-law relation expected for a self-similar structure.

Characteristics of complex networksSelf-similarity of complex networks1

p. 151Chaoming Song, Shlomo Havlin & Hernán A. Makse

Here we analyse a variety of real complex networks and find that, on the contrary, they consist of self-repeating patterns on all length scales. This result is achieved by the application of a renormalization procedure that coarse-grains the system into boxes containing nodes within a given ‘size’. We identify a power-law relation between the number of boxes needed to cover the network and the size of the box, defining a finite self-similar exponent. These fundamental properties help to explain the scale-free nature of complex networks and suggest a common self-organization dynamics.

Self-similarity of complex networksChaoming Song, Shlomo Havlin & Hernán A. Makse

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http://www.nature.com/nature/journal/v433/n7024/full/nature03248.html Self-similarity of complex networks Nature 433, 392-395 (2005)

Complex networks from such different fields as biology, technology or sociology share similar organization principles. The possibility of a unique growth mechanism promises to uncover universal origins of collective behaviour. In particular, the emergence of self-similarity in complex networks raises the fundamental question of the growth process according to which these structures evolve. Here we investigate the concept of renormalization as a mechanism for the growth of fractal and non-fractal modular networks. http://www.nature.com/nphys/journal/v2/n4/full/nphys266.html Nature Physics 2, 275 - 281 (2006)

Origins of fractality in the growth of complex networksChaoming Song, Shlomo Havlin & Hernán A. Makse

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Song, Havlin and Makse investigate the concept of renormalization as a mechanism for the growth of fractal and non-fractal modular networks. They show that the key principle that gives rise to the fractal architecture of networks is a strong effective ‘repulsion’ (or, disassortativity) between the most connected nodes (that is, the hubs) on all length scales, rendering them very dispersed. A robust network comprising functional modules, such as a cellular network, necessitates a fractal topology, suggestive of an evolutionary drive for their existence.http://www.nature.com/nphys/journal/v2/n4/full/nphys266.html Nature Physics 2, 275 - 281 (2006)

Origins of fractality in the growth of complex networksChaoming Song, Shlomo Havlin & Hernán A. Makse

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An agent-based model (ABM) is a computational model for simulating the actions and interactions of autonomous individuals in a network, with a view to assessing their effects on the system as a whole. They are used in study of complexity and emergence.

It combines elements of game theory, complex systems, emergence, computational sociology and evolutionary programming. Monte Carlo Methods are used to introduce randomness.http://www.youtube.com/watch?v=2C2h-vfdYxQ&feature=related Composite Agents http://en.wikipedia.org/wiki/Agent-based_model

Generative approaches. Agent-based Models (ABM)

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ABMs in general are used to model complex, dynamical adaptive systems. The interesting aspect in ABMs is the micro-macro link (agent-society). Multi-Agent Systems (MAS) models may be used for any number (in general heterogeneous) entities spatially separated by the environment which can be modeled explicitly. Interactions are in general asynchronous which adds to the realism of simulation.

Agent-based models of dynamic adaptive systems

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Social computing represents a new computing paradigm which is one sort of the natural computing, often inspired by biological systems (e.g. swarms).

The main tools in this field are simulation techniques used in order to facilitate the study of society and to support decision-making policies, helping to analyze how changing policies affect social, political, and cultural behavior (Epstein, 2007).

Epstein, J. M. (2007). Generative Social Science: Studies in Agent-Based Computational Modeling. Princeton University.

Simulation

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With regard the human aspect, social computing is radically changing the character of human relationships worldwide (Riedl, 2011). Instead of maximum 150 connections prior to ICT, (Dunbar, 1998), social computing easily leads to networks of several hundred of contacts.

It remains to understand what type of society will emerge from such massive “long-range” distributed interactions instead of traditional fewer and deeper short-range ones.

Emergence of global social computing

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Riedl J. (2011) "The Promise and Peril of Social Computing”, Computer, vol.44, no.1, pp. 93-95

Dunbar R. (1998) Grooming, Gossip, and the Evolution of Language, Harvard Univ. Press

In this process, information overload on individuals is steadily increasing, and social computing technologies are moving beyond simple social information communication toward social intelligence, (Zhang et al. 2011) (Lim et al. 2008) (Wang et al. 2007), which brings an additional level of complexity.

Zhang D., Guo B., Yu Z. (2011) Social and Community Intelligence, Computer, Vol. 99, No. PrePrints. doi:10.1109/MC.2011.65

Lim H. C., Stocker R., Larkin H. (2008) Ethical Trust and Social Moral Norms Simulation: A Bio-inspired Agent-Based Modelling Approach. In: 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, December 2008. pp. 245-251.

Wang F-Y., Carley K. M., Zeng D., and Mao W. (2007) Social Computing: From Social Informatics to Social Intelligence. IEEE Intelligent Systems 22, 2 (March 2007), 79-83. DOI=10.1109/MIS.2007.41 http://dx.doi.org/10.1109/MIS.2007.41

Towards social intelligence

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As Gilbert (2005) points out, novelty of agent based models (ABMs) “offers the possibility of creating ‘artificial’ societies in which individuals and collective actors such as organizations could be directly represented and the effect of their interactions observed. This provides for the first time the possibility of using experimental methods with social phenomena, or at least with their computer representations; of directly studying the emergence of social institutions from individual interaction.”

Gilbert N: (2005) Agent-based social simulation: dealing with complexity, http://www.complexityscience.org/NoE/ABSS-dealing%20with%20complexity-1–1.pdf

The emergence of social institutions from individual interactions

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Crowdsourcing is, according to the Merriam-Webster Dictionary, the practice of obtaining needed services, ideas, or content by obtaining contributions from a large group of people, and especially from an online community, rather than from traditional employees or suppliers.

Tools such as prediction markets, social tagging, reputation and trust systems as well as recommender systems are based on crowdsourcing. Polymath Project is an example of mathematical proofs made by crowdsourcing http://polymathprojects.org/

http://www.nextscientist.com/3-examples-crowdsourcing-science/

Crowdsourcing

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Another focus of social computing is on computational modeling of social behavior, among others through Multi-agent systems (MAS) and Social Networks (SN).

There are several usages of Multi-agent systems: to design distributed and/or hybrid systems; to develop philosophical theory; to understand concrete social facts, or to answer concrete social issues via modelling and simulation.

Computational modelling of social behavior

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Multi-agent systems are used for modelling, among other things, cognitive or reactive agents who interact in dynamic environments where they possibly depend on each other to achieve their goals.

The emphasis is nowadays on constructing complex computational systems composed by agents which are regulated by various types of norms, and behave like human social systems.

Multi-agent systems for modelling of social behavior

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An Interlude:Information, computation, cognition Agency-based Hierarchies of Levels

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Human connectome http://outlook.wustl.edu/2013/jun/human-connectome-project

scientificamerican0612-50.pdf The Human Brain Projecthttp://www.nature.com/scientificamerican/journal/v306/n6/pdf/

information computation

cognitionA framework based on an agents perspective.

http://www.idt.mdh.se/~gdc/work/PT-AI-Oxford-2013.09.21-GDC.pdf

Short summary of the Info-computational framework:

●Information constitutes a structure consisting of differences in one system that cause the differences in another system. In other words, information is <observer>-relative.

●Computation is information processing (dynamics of information). It is physical process of morphological change in the informational structure (physical implementation of information, as there is no information without physical implementation.)

Information, computation, cognition. Agency-based Hierarchies of Levels

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3. Both information and computation appear on many different levels of organisation/abstraction/resolution/granularity of matter/energy in space/time.

4. Of all agents (entities capable of acting on their own behalf) only living agents have the ability to actively make choices so to increase the probability of their own continuing existence. This ability of living agents to act autonomously on its own behalf is based on the use of energy and information from the environment.

Information, computation, cognition. Agency-based Hierarchies of Levels

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5. Cognition consists of all (info-computational) processes necessary to keep living agent’s organizational integrity on all different levels of its existence. Cognition = info-computation

6. Cognition is equivalent with the (process of) life. Its complexity increases with evolution.This complexification is a result of morphological computation.

Information, computation, cognition. Agency-based Hierarchies of Levels

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http://www.idt.mdh.se/~gdc/work/Information-Computation-Agency-Cognition-20131218.pdf

City as Complex System

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As urban planning moves from a centralized, top-down approach to a decentralized, bottom-up perspective, our conception of urban systems is changing. Michael Batty studies urban dynamics in the context of complexity theory, presenting models that demonstrate how complexity theory can combine a huge number of processes and elements into organic wholes. The bottom-up processes -- in which the outcomes are always uncertain -- can combine with new forms of geometry associated with fractal patterns and chaotic dynamics to provide theories that are applicable to highly complex systems such as cities.

http://www.complexcity.info/files/2011/07/batty-cluster-magazine-2008.pdf Generating cities bottom up

City as Complex System

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Models based on cellular automata (CA), simulate urban dynamics through the local actions of automata. Agent-based models (ABM), in which agents are mobile and move between locations relate to many scales, from the scale of the street to patterns and structure at the scale of the urban region. Applications of all these models are used to specific urban situations, discussing concepts of criticality, threshold, surprise, novelty, and phase transition in the context of spatial developments. http://www.complexcity.info

http://www.complexcity.info/ A Science of Cities

The article “The Origins of Scaling in Cities” which appeared in June 2013 issue of Science has a simple mathematical model of a city: it is a combination of a social network of interactions and something actually embedded in physical space, that is what a city is—an agglomeration of people in a specific region.Even though the model is simple, it works surprisingly well.Cities are modeled as massive social networks, made not so much of individual characteristics of people but of their interactions. These social interactions happen, inside other networks – social, spatial, and infrastructural – which together allow people, things, and information to meet across urban space.

A New Model for Urban Scaling – explaining

how quantities scale with population size. Luis Bettencourt

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(1) Mixing population. The city develops so that citizens can explore it fully given the resources at their disposal.(2) Incremental network growth. This assumption requires that infrastructure networks develop gradually to connect people as they join, leading to decentralized networks(3) Human effort is bounded … [as a city grows, our ability to deal with it can't increase beyond some reasonable amount](4) Socioeconomic outputs are proportional to local social interactions … From this perspective, cities are concentrations not just of people, but rather of social interactions.

http://www.santafe.edu/news/item/science-bettencourt-cities-framework/ http://www.wired.com/wiredscience/2013/06/a-new-model-for-urban-scaling

Four simple assumptions of Bettencourt’s model

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Computational models are excellent tools for analysis of social phenomena as they give us an overview and possibility to study different possible scenarios in the development of social systems. However, they cannot replace good human judgment.

All simulations depend on the assumptions of the model, so in other words the old wisdom holds: garbage in – garbage out!

With this in mind, social computing can be expected to develop into everyday tools for decision-making concerning social phenomena – in economics, government, business, medicine, bioinformatics, epidemiology, politics, urban planning, etc.

A word of caution:Using computational models

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Further Reading: A Computable Universe

Computational models and methods can be used not only to study social phenomena, but they can be applied to whole of nature too. Here is some new research on this bigger topic.

Computation, Information, Cognition

Editor(s): Gordana Dodig Crnkovic and Susan

Stuart, Cambridge Scholars Publishing, 2007

Computing Nature

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Information and ComputationEditor(s): Gordana Dodig Crnkovic and

Mark Burgin, World Scientific, 2011

Computing NatureEditor(s): Gordana Dodig Crnkovic and

Raffaela Giovagnoli, Springer, 2013

http://dx.doi.org/10.1007/978-3-642-37225-4

http://astore.amazon.co.uk/books-books-21/detail/9814295477

http://www.amazon.co.uk/Computation-Information-Cognition-Nexus-Liminal/dp/1847180906/ref=sr_1_2?ie=UTF8&qid=1306954122&sr=1-2#reader_1847180906

Dodig-Crnkovic G., Dynamics of Information as Natural Computation, Information 2011, 2(3), 460-477; doi:10.3390/info2030460 Special issue: Selected Papers from FIS 2010 Beijing Conference, 2011. http://www.mdpi.com/journal/information/special_issues/selectedpap_beijing http://www.mdpi.com/2078-2489/2/3/460/ See also: http://livingbooksaboutlife.org/books/Energy_Connections

Dodig-Crnkovic, G.; Rotolo, A.; Sartor, G.; Simon, J. and Smith C. (Editors)Social Computing, Social Cognition. Social Network and Multiagent Systems. Social Turn - SNAMAS 2012AISB/IACAP World Congress 2012. Birmingham, UK, 2-6 July 2012http://events.cs.bham.ac.uk/turing12/proceedings/11.pdf , 2012.

Dodig-Crnkovic G., Agent Based Modeling with Applications to Social Computing, Proceedings IACAP 2011. The Computational Turn: Past, Presents, Futures?, p 305, Mv-Wissenschaft, Münster, Århus University, Danmark, Editor(s): Charles Ess and Ruth Hagengruber, July 2011. http://www.idt.mdh.se/~gdc/work/IACAP11_Gordana-DC.pdf

Dodig-Crnkovic, G. Information, Computation, Cognition. Agency-based Hierarchies of Levels. In V. C. Müller (Ed.), Fundamental Issues of Artificial Intelligence (Synthese Library). Berlin: Springer. (forthcoming) http://www.idt.mdh.se/~gdc/work/Information-Computation-Agency-Cognition-20131218.pdf

This talk is based on the following work

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