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CDT403 Research Methodology in Natural Sciences and Engineering

Theory of ScienceRESEARCH, TECHNOLOGY, SOCIETY,

COMPLEXITY AND INTERDISCIPLINARITY

Gordana Dodig-Crnkovic

School of Innovation, Design and Engineering

Mälardalen University

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SCIENCE, RESEARCH, TECHNOLOGY, SOCIETY, COMPLEXITY

SCIENCES ON DIFFERENT LEVELS OF ORGANIZATION

SCIENCE, RESEARCH, TECHNOLOGY

SCIENCE, SOCIETY, PRODUCTION – TRIPLE HELIX

SCIENCE, RESEARCH, TECHNOLOGY, PROGRESS

SCIENCE WARS AND COLLABORATION:TRANSDISCIPLINARY, INTERDISCIPLINARY AND

CROSSDISCIPLINARY RESEARCH

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SCIENCE OBJECTS DOMINATING METHOD

Simple Reductionism (analysis)

Logic &Mathematics

Abstract objects:propositions, numbers, ... Deduction

Natural SciencesNatural objects: physical bodies,

fields and interactions, living organisms ...

Hypothetico-deductive method

Social SciencesSocial objects:

human individuals, groups, society, ..

Hypothetico-deductive method + Hermeneutics

HumanitiesCultural objects: human ideas,

actions and relationships, language, artefacts…

Hermeneutics

  Complex Holism (synthesis)

SCIENCE IN MICRO AND MACROCOSMOSLevels of abstraction/Levels of organization

Sciences, Objects and Methods

http://www.youtube.com/watch?v=akbilxS1dGc&feature=related zoom in – zoom out

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SCIENCE, RESEARCH, TECHNOLOGY Aristotle's Distinctions between Science and Technology

  Science Technology

Object unchangeable changeable

Principle of motion inside outside

End knowing the general knowing the concrete

Activity theoria: end in itself poiesis: end external

Method abstraction modeling complexity

Process conceptualizing optimizing

Innovation form discovery invention

Type of result law-like statements rule-like statements

Time perspective long-term short-term

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SCIENCE, RESEARCH, DEVELOPMENT AND TECHNOLOGY

Science

Research

Development

Technology

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Culture(Religion, Art, …)

CLASSICAL SCIENCES LANGUAGE BASED SCHEME

Natural Sciences(Physics,

Chemistry,Biology, …)

Social Sciences(Economics,

Sociology,Anthropology, …)

The Humanities(Philosophy, History,

Linguistics …)

Logic &

Mathematics

Computing

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SCIENCES BASED ON SEVERAL RESEARCH FIELDS – CROSS DISCIPLINARY RESEARCH

Our basic scheme represents classical sciences.  Many modern sciences however are stretching over several

research fields of our scheme. Computer science e.g. includes the field of AI that has its roots in

mathematical logic and mathematics but uses physics, chemistry and biology and even has parts where medicine and psychology are very important.

Software Engineering include both formal methods and project management.

HCI, human-computer interaction combines knowledge from “hard” and “soft” sciences.

Computer games border with arts.

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TECHNOLOGY EXPANDS OUR WAYS OF THINKING ABOUT THINGS, EXPANDS OUR WAYS OF DOING THINGS.

Herbert A. Simon

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SCIENCE AND SOCIETYTHE “TRIPLE HELIX” MODEL

CULTURE

SCIENCES & HUMANITIES

SOCIETY

Knowledge society based on ICT

The triple helix model: – ACADEMIA– PRODUCTION

(ECONOMY)– GOVERMENT

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SOCIETAL ASPECTS OF SCIENCE

Science has undoubtedly several important facets:

- insights in foundational issues (understanding of the world)- applications (practical use)- societal aspects (impact on the society)

Sciences are promoting rational and analytical discussions of the central issues of concern to scientists and other scholars, and to the public at large, both in terms of knowledge production and in practical applications.

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SOCIETAL ASPECTS OF SCIENCERESEARCH COMMUNITY AS INFORMATIONAL NETWORK

“ .. if we consider Galileo alone in his cell muttering, ‘and yet it moves,’ with the recent meeting at Kyoto – where heads of states, lobbyists, and scientists were assembled together in the same place to discuss the Earth – we measure the difference ..”

Bruno Latour

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SOCIETAL ASPECTS OF SCIENCE

Further reading on current topics:http://www.sciencemag.org

Essays on Science and Society Science magazine

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POSTMODERNISM AND THE NATURE OF SCIENCE

Modernism may be seen as the height of the Enlightenment's quest for an rational aesthetics, ethics, and knowledge.

Postmodernism is a cultural and philosophical movement which formed in reaction to modernism.

Postmodernism is concerned with how the authority of those ideals, sometimes called meta-narratives, are undermined through fragmentation and deconstruction.

Meta-narratives - "grand narratives“, form of ‘universal truth'

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POSTMODERNISM

Postmodernism attacks the primacy of ideas of universals and encourages fractured, fluid and multiple perspectives and is marked by an increasing importance in the ideas from the Sociology of knowledge.

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POSTMODERNISM

All knowledge, scientific knowledge included, is found to be socially constructed. Being “socially constructed” implies not absolute, but relative to the humans and humanity. No (absolute) objectivity exists.

From that correct observation, postmodernism draws a very far-reaching conclusion: “Science is merely one story among others.” The world we know is one that is constructed by human discourses. From the point of view of knowledge (epistemologically ), a scientific text is understood as being on a par with a literary text.

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SCIENCE WARS (1)

In early 1996 the physicist Alan Sokal who was provoked by postmodernist derogatory attitude to science caused a controversy by publishing two controversial journal articles.

The first article, Transgressing the Boundaries: Toward a Transformative Hermeneutics of Quantum Gravity appeared in the journal Social Text.

It pretended to be a serious article on the implications of developments in the field of cultural studies for developments in modern physics, and vice-versa.

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SCIENCE WARS (2)

The second article, A Physicist Experiments with Cultural Studies, appeared in the journal Lingua Franca just as issue of Social Text containing the first article came out. It revealed that the first article was in fact a hoax.

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SCIENCE WARS (3)

But why did he do it?

“And I'm a stodgy old scientist who believes, naively, that there exists an external world, that there exist objective truths about that world, and that my job is to discover some of them. “

Allan Sokal

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SCIENCE WARS (4)

“To test the prevailing intellectual standards, I decided to try a modest (though admittedly uncontrolled) experiment:

Would a leading North American journal of cultural studies (..) publish an article liberally salted with nonsense if (a) it sounded good and (b) it flattered the editors' ideological preconceptions? “

Allan Sokal

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SCIENCE WARS (5)

The post modern ideas were known as Deconstructionism and Social Constructivism.

The branch of sociology, Sociology of Scientific Knowledge (SSK) and Science and Technology Studies (STS) were influences by postmodern movements and had the objective of showing that the results of scientific findings did not represent objective reality, but were basically instruments of the ideology of dominant groups within society. 

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POSTMODERNIST ANTI-SCIENTISM

Post-modernism was a radical critique against science, contemporary philosophy and current understanding of rationality. 

The view of science as a search for truths (or approximate truths) about the world was rejected. 

According to postmodernism, the natural world has a subordinated role in the construction of scientific knowledge.  

Science was just another social practice, producing ``narrations'' and ``myths'' with basically no more validity than any other myths.

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IS THERE ANYTHING NEW UNDER THE SUN? ANY PROGRESS?

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AN EXAMPLE OF PROGRESS - TRANSPORTATION

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AN EXAMPLE OF PROGRESS - TRANSPORTS

Beam me up Scotty next?

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SCIENCE WARS (6)

Sources for further reading:

http://www.physics.nyu.edu/faculty/sokal

http://www.math.gatech.edu/~harrell/cult.html

http://skepdic.com/sokal.html

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WHAT HAVE WE LEARNED FROM POSTMODERNISM?

Humans always produce theories that are context-dependent and based on our (human) perspective. It is good to be aware of that context in which science operates.

Society is an important factor when it comes to politics, including policies in science which provides resources for science.

In a given context, by scientific methods we can reach our best knowledge, which is constantly improving

If not seen as absolute, but our best common knowledge about the world, science has a very distinct position among different possible descriptions of the world.

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END OF SCIENCE WARS AND NEW EMERGING ALLIANCES

At present, a lot of activity in cross-disciplinary, multi-disciplinary and inter-disciplinary collaborations.

Examples: Computing and Philosophy http://ia-cap.organdInterdisciplines (Topics: Adaptation and Representation, Art and Cognition, Causality, Enaction (Action and perception intertwined), Issues in Coevolution of Language and Theory of Mind.) http://www.interdisciplines.org

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RESEARCH, COMMUNICATION AND ICT

New development of collaborations between different research disciplines is enabled by the progress of technology.However, there is a problem of communication: Different knowledge fields traditionally have different languages.

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RESEARCH, COMMUNICATION AND ICT

Sciences cover well defined domains (physics, mathematics, biology, sociology, economy…) where knowledge is produced by specific scientific communities through intense communication within a group and with not much communication with the rest of the world.

However, access to knowledge have become easy and communication between sciences, arts and humanities more and more common.

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Cybernetics as a Language for Interdisciplinary Communication

Stuart A. UmplebyThe George Washington University

Washington, DCwww.gwu.edu/~umpleby

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How is interdisciplinary communication possible?

[Cybernetics is the interdisciplinary study of the structure of regulatory systems. Cybernetics is closely related to control theory and systems theory. Both in its origins and in its evolution in the second-half of the 20th century, cybernetics is equally applicable to physical and social (that is, language-based) systems. (Wikipedia)]

•We need to share a common language

•Perhaps there is a common “deep structure” which is hidden by our more specialized discipline-oriented terms and theories

After Stuart A. Umpleby

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Common processes in the external world

James G. Miller’s suggests that living systems exist at seven levels: - cell, - organ, - organism, - group, - organization, - nation, - supranational organization

After Stuart A. Umpleby

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Basic concepts

In cybernetics there are three fundamental concepts:

Regulation Self-organization

Reflexivity

After Stuart A. Umpleby

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Regulation

Regulation is based on two elements – regulator and system being regulated

Engineering examples – thermostat and heater, automatic pilot and airplane

Biological examples – feeling of hunger and food in stomach, light in eye and iris opening

Social system examples – manager and organization, therapist and patient

After Stuart A. Umpleby

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The law of requisite variety

Information and selection“The amount of selection that can be performed is limited by

the amount of information available”

Regulator and regulated“The variety in a regulator must be equal to or greater than the

variety in the system being regulated”W. Ross Ashby

After Stuart A. Umpleby

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Coping with complexity

When faced with a complex situation, there are two choices

1. Increase the variety in the regulator: hire staff or subcontract

2. Reduce the variety in the system being regulated: reduce the variety one chooses to control

After Stuart A. Umpleby

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The management of complexity

• There has been a lot of discussion of complexity, as if it exists in the world

• Cyberneticians prefer to speak about “the management of complexity”

• Their view is that complexity is observer dependent, that the system to be regulated is defined by the observer

After Stuart A. Umpleby

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Self-organization

• Every isolated, determinate, dynamic system obeying unchanging laws will develop organisms adapted to their environments. W. Ross Ashby

• Many elements within the system• Boundary conditions – open to energy (hence dynamic), – closed to information (interaction rules do not change during the

period of observation)

http://www-lih.univ-lehavre.fr/~bertelle/cossombook/cossombook.html Complex Systems and Self-organization Modelling After Stuart A. Umpleby

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Examples of self-organization

• Physical example – chemical reactions; iron ore, coke, and oxygen heated in a blast furnace will change into steel, carbon dioxide, water vapor and slag

• Biological examples – food in the stomach is transformed into usable energy and materials, species compete to yield animals adapted to their environments, insect swarms

After Stuart A. Umpleby

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SELF-ORGANIZATION IN ARTIFACTS

http://www.youtube.com/watch?v=SkvpEfAPXn4

http://groups.csail.mit.edu/mac/projects/amorphous/Robust/

http://www.calresco.org/links.htm Self-organization resources

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DIGITAL VIDEO FEEDBACK AND MORPHOGENESIS

Video Feedback systems tend toward either stability or chaos. While the stable attractor offers some interest in the subtleties of its decay, the unstable attractor offers an unlimited supply of endless evolving motifs and an emergent behaviour.

The system can be get into chaotic emergence via camera movement (rotation and positioning). The important thing was to catch the movement of ‘catching a shape’ in a particular temporal phase to feed back into the system advancing the complexity and initiating lifelike morphogenesis.

http://www.transphormetic.com/Talysis01.htm

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COMPLEX SYSTEMS

Four Important Characteristics of Complexity:

• Self-Organization

• Non-Linearity

• Order/Chaos Dynamic

• Emergent Properties

http://www.calresco.org/links.htm

http://www.youtube.com/watch?v=QmrWfRX42ZM&feature=related

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COMPLEX SYSTEMS

Computer Programming approaches used for demonstrating, simulating, and analyzing Complex Systems:

• Artificial Life

• Genetic Algorithms

• Neural Networks

• Cellular Automata

• Boolean Networks

http://www.calresco.org/links.htm

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SELF-REFERENCE

http://www.lsd.ic.unicamp.br/~oliva/guarana/docs/design-html/node2.html Computational Reflection

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DOUGLAS HOFSTADTER ON SELF-REFERENCE

“ Self-reference is ubiquitous. It happens every time any one says “I” or “me” or “word” or “speak” or “mouth”. It happens every time a newspaper prints a story about reporters, every time someone writes a book about writing, designs a book about book design, makes a movie about movies, or writes an article about self-reference. Many systems have the capability to represent or refer to themselves somehow, to designate themselves (or elements of themselves) within the system of their own symbolism. Whenever this happens, it is an instance of self-reference.”

“My proposal [...] is to see the “I” as a hallucination perceived by a hallucination, which sounds pretty strange, or perhaps even stranger: the “I” as a hallucination hallucinated by a hallucination.”(I Am a Strange Loop, p. 293 )

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Self-reference (Reflexivity)

• This model has traditionally been avoided and is logically difficult

• Inherent in social systems where observers are also participants, in individual living organisms

• Every statement reveals an observer as much as what is observed

After Stuart A. Umpleby

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EXAMPLES OF SELF-REFERENCE:RECURSIVE ALGORITHMS

This graph is based on a simple recursive algorithm. Recursion is a popular technique used to describe trees and the like, because of the self-referential nature of a tree.

Self-reference can lead to undecidability (and paradoxes like set of all sets that are not members of themselves)

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Observation

Self-awareness

Self-reference (Reflexivity)

After Stuart A. Umpleby

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Reflexivity in a social system

Stuart A. Umpleby

50Three Versions of Cybernetics

By transforming conceptual systems (through persuasion, not coercion), we can change society

If people accept constructivism, they will be more tolerant

Scientific knowledge can be used to modify natural processes to benefit people

An important consequence

Ideas are accepted if they serve the observer’s purposes as a social participant

Ideas about knowledge should be rooted in neurophysiology.

Natural processes can be explained by scientific theories

A key assumption

How people create, maintain, and change social systems through language and ideas

How an individual constructs a “reality”

How the world worksWhat must be explained

Explain the relationship between the natural and the social sciences

Include the observer within the domain of science

Construct theories which explain observed phenomena

The puzzle to be solved

The biology of cognition vs. the observer as a social participant

Realism vs. ConstructivismReality vs. scientific theories

A key distinction

A pragmatic view of epistemology: knowledge is constructed to achieve human purposes

A biological view of epistemology: how the brain functions

A realist view of epistemology: knowledge is a “picture” of reality

The view of epistemology

Social CyberneticsBiological CyberneticsEngineering Cybernetics

After Stuart A. Umpleby

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Cybernetics view of scientific progress:the Correspondence Principle

Proposed by Niels Bohr when developing the quantum theory

Any new theory should reduce to the old theory to which it corresponds for those cases in which the old theory is known to hold

A new dimension is required

After Stuart A. Umpleby

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New philosophy of science        

An Application of the Correspondence Principle 

Old philosophy of science

Amount of attention paid to the observer

After Stuart A. Umpleby

NEW PARADIGM OF COMPUTING: Organic computing

Organic computing is a form of biologically-inspired computing with organic properties. It has emerged recently as a challenging vision for future information processing systems. Organic Computing is based on the insight that we will soon be surrounded by large collections of autonomous systems, which are equipped with sensors and actuators, aware of their environment, communicate freely, and organise themselves in order to perform the actions and services that seem to be required.

http://en.wikipedia.org/wiki/Organic_computing

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ORGANIC COMPUTING

The presence of networks of intelligent systems in our environment opens fascinating application areas but, at the same time, bears the problem of their controllability.

Hence, we have to construct such systems — which we increasingly depend on — as robust, safe, flexible, and trustworthy as possible.

In particular, a strong orientation towards human needs as opposed to a pure implementation of the technologically possible seems absolutely central.

In order to achieve these goals, our technical systems will have to act more independently, flexibly, and autonomously, i.e. they will have to exhibit life-like properties. We call those systems "organic".

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ORGANIC COMPUTING

• An "Organic Computing System" is a technical system, which adapts dynamically to the current conditions of its environment. It is characterised by the self-X properties:

• self-organization, • self-configuration (auto-configuration), • self-optimisation (automated optimization), • self-healing, • self-protection (automated computer security), • self-explaining, • and context-awareness.

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ORGANIC COMPUTING

• In order to be able to build huge intelligent organic-computing systems we have to learn from variety of fields – from physics, chemistry, genetics, biology to sociology and linguistics.

• In other words we have to learn to manage complexity of knowledge production from different research fields.

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CONCLUSIONS: TRANSDISCIPLINARY, INTERDISCIPLINARY AND CROSS

DISCIPLINARY RESEARCH

 Modern sciences are stretching through several classical fields. Computer science e.g. includes the field of AI that has its roots in

mathematical logic and mathematics but uses physics, chemistry and biology and even parts where medicine and psychology are important.

Examples: Environmental studies, Cognitive sciences, Cultural studies, Policy sciences, Information sciences, Women’s studies, Molecular biology, Philosophy of Computing and Information, Bioinformatics, ..

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CONCLUSIONS: TRANSDISCIPLINARY, INTERDISCIPLINARY AND CROSS

DISCIPLINARY RESEARCH

Research into complex phenomena has led to an insight that research problems have many different facets which may be approached differently at different levels of abstraction and that every knowledge field has a specific domain of validity.

This new understanding of a multidimensional many-layered knowledge space have among others resulted in an ecumenical conclusion of science wars by recognition of the necessity of an inclusive and complex knowledge architecture which recognizes importance of a variety of approaches and types of knowledge.

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REPRESENTATIVE WORK:TRANSDISCIPLINARY, INTERDISCIPLINARY AND CROSS

DISCIPLINARY RESEARCH

Based on sources in philosophy, sociology, complexity theory, systems theory, cognitive science, evolutionary biology and fuzzy logic, Smith and Jenks present a new interdisciplinary perspective on the self-organizing complex structures.

They analyze the relationship between the process of self-organization and its environment/ecology. Two central factors are the role of information in the formation of complex structure and the development of topologies of possible outcome spaces.

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The authors argue for a continuous development from emergent complex orders in physical systems to cognitive capacity of living organisms to complex structures of human thought and to cultures.

This is a new understanding of unity of interdisciplinary knowledge, unity in structured diversity, also found in Mainzer.

“Cosmic evolution leads from symmetry to complexity by symmetry breaking and phase transitions. The emergence of new order and structure in nature and society is explained by physical, chemical, biological, social and economic self-organization, according to the laws of nonlinear dynamics.

All these dynamical systems are considered computational systems processing information and entropy.”

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REPRESENTATIVE WORK

“Are symmetry and complexity only useful models of science or are they universals of reality? Symmetry and Complexity discusses the fascinating insights gained from natural, social and computer sciences, philosophy and the arts.

With many diagrams and pictures, this book illustrates the spirit and beauty of nonlinear science. In the complex world of globalization, it strongly argues for unity in diversity.”

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Preface ix

Part IThe Simple and the Complex

1 Prologue: An Encounter in the Jungle 3

2 Early Light 11

3 Information and Crude Complexity 23

4 Randomness 43

5 A Child Learning a Language 51

6 Bacteria Developing Drug Resistance 63

7 The Scientific Enterprise 75

8 The Power of Theory 89

9 What Is Fundamental? 107

REPRESENTATIVE WORK

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Part II The Quantum Universe

10 Simplicity and Randomness in the Quantum Universe 123

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A Contemporary View of Quantum Mechanics: Quantum Mechanics and the Classical Approximation

135

12 Quantum Mechanics and Flapdoodle 167

13 Quarks and All That: The Standard Model 177

14 Superstring Theory: Unification at Last? 199

15 Time's Arrows: Forward and Backward Time 215

Part III Selection and Fitness

16 Selection at Work in Biological Evolution and Elsewhere 235

17 From Learning to Creative Thinking 261

18 Superstition and Skepticism 275

19 Adaptive and Maladaptive Schemata 291

20 Machines That Learn or Simulate Learning 307

Part IV Diversity and Sustainability

21 Diversities Under Threat 329

22 Transitions to a More Sustainable World 345

23 Afterword 367

Philosophy of Information (Handbook of the Philosophy of Science) (Hardcover)by Pieter Adriaans & Johan F.A.K. van Benthem (Editors), 2008

DM. Gabbay, P Thagard & J Woods (Series Editors)

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REPRESENTATIVE WORK

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Philosophy of Science/Theory of Science Assignments

– Assignment 2: Demarcation of Science vs. Pseudoscience (in groups of two)

– Discussion of Assignment 2 - compulsory– Assignment 2-extra (For those who miss the discussion of the

Assignment 2)– Assignment 3: GOLEM: Three Cases of Theory Confirmation (in

groups of two) – Discussion of Assignment 3 - compulsory– Assignment 3-extra (For those who miss the discussion of the

Assignment 3)

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AND AN EXAMPLE OF THE ROLE OF PERSPECTIVE AT THE END!