Revised 12/4/2000

Cluster Course Development Proposal (1 December 2000):

Simulating Society: Cyber Models of Cultural Complexity

I 

Teaching Team

1.        Nicholas Gessler (Coordinator), Instructor, Geography.

2.       Phil Bonacich, Professor, Sociology.

3.       Dwight Read, Professor, Anthropology.

4.       Susanne Lohmann, Professor, Political Science.

5.       Bill McKelvey, Professor, Anderson School of Management.

6.       Lars-Erik Cederman (Alternate), Assistant Professor, Political Science.

Guest Lecturers

1.        Steven Bankes, Rand, Santa Monica and Evolving Logic, Los Angeles.  “Simulation Successes and Failures for Strategic Planning.”

2.       David Fogel, Natural Selection Inc., San Diego.  “Simulating Creativity: Evolutionary Computation.”

3.       Alex Singer, USC Integrated Media Systems Center & Institute for Creative Technologies, Director Star Trek TNG, Paramount.  “The Army and the Holodeck.”

II

Course Description, Aims and Objectives

We focus on the new sciences of complexity and chaos and their implications for the social sciences.  What is “new” in science is our ability to describe a social situation in a special language and then have that language work out its own entailments in a simulation.  This is a revolutionary development in the philosophy of the sciences that is forcing us to rethink traditional ways of knowing the world around us.  Students will learn current theories in the evolution of cultural complexity, will have hands-on experience experimenting with and writing simulations, and will gain critical insight into the practice of social science.

We begin the course with a brief historical perspective on simulations in the social sciences, moving through five introductory lectures from each of our teaching team.  These provide us with an in-depth overview of the breadth of modeling and simulation theory in the social sciences.  We then proceed to build a social science in simulations from the bottom-up.  From the “great ideas” of social origins, maintenance, growth and change we construct models from a kit of simple elements.  We add to this kit, piece by piece, forging a pathway step by step through the social sciences, a pathway that parallels the evolution of culture from simpler prehistoric societies to the complex societies of today.  We see how social organization emerges from the biological and cultural underpinnings of the evolution of the individual.  We see how individuals interact with one another, the population as a whole, and the physical environment, creating increasingly larger and more complex interrelationships.  We see how simpler structures originating in our evolutionary past are reused and nested at different scales to form a framework for more complex culture.  Early in the course, students begin to model these systems with an easily accessible language called StarLogo.  As we move progressively across the spectrum from simple to complex theory, we add complexity to the simple artificial worlds we originally created.  As we do this, we also add sophistication to the students’ critical and creative skills in equal measure.  We begin our story with individual human cognition, consider patterns of emergent behavior in populations, and then embrace more realistic layered geographic spaces using selected phrases from a ubiquitous language called C++.  We conclude by bringing students up to speed at the cutting-edge of multiagent simulation, the fascinating challenges of evolutionary computation and creating culture among communities of robots.

We have all played computer games and have been stimulated by simulated worlds brought to life in movies (e.g. War Games).  We have all heard about artificial intelligence, artificial life, and virtual reality, which respectively model complex individual thinking, complex populations, and complex physical environments.  Our social science simulations lay somewhere among all three.  We model individuals interacting with other individuals, individuals interacting with groups in a social environment, and individuals interacting with objects situated in space and time in a physical environment.  We call any object that can sense, think, and act an “agent.”  We call the entire model a “multiagent simulation.”  Using this approach we can study what constitutes a culture: is it the behavior of individuals thinking all alike, or individuals with differing perspectives on the world (Rashomon)?  Although our simulations are nowhere as complex and immersive as those suggested by the movies (Thirteenth Floor & Dark City), they serve us measurably well.

We offer students the hands-on experience of writing multiagent simulations from the bottom-up and experimenting with them as desktop laboratories for evaluating alternative explanations in social science.  By studying these simulations we can distinguish what is possible from what is not.  We can run alternative “what if” scenarios to test a theory or an explanation.  What if we changed this behavior or event, or that?  Then what?  We can ask, “what would have happened” had some other course of action had been taken (Run Lola Run)?  Within a simulation model, we have access to every piece of information about an artificial world.  We require no special knowledge of computers from our students.  We only suggest that they have some familiarity with Windows on a PC.  Even if they’ve never touched a computer, we can transport them into these artificial worlds quite comfortably and provide them with some stimulating and useful insights.  By the end of the course students will be in a better position to evaluate existing software designed to solve social problems, to direct a team of programmers creating new simulations and models, and to express and test their own ideas through simulations.  Participants will be prepared to understand and critically assess the quality of models and simulations that are used to direct social policy that affects us every day.  They will gain a competitive advantage by learning the workings of one of the most compelling planning tools in use today.

III

Course Format

·         Two lectures per week.

·         Weekly laboratory at CLICC for hands-on simulations.

·         Maximum enrollment 140 (negotiable).

·         GE fulfillment Social Sciences.

Facilities & Equipment Required

·         Classroom, CLICC Computer Lab (PC), and Staging area, all equipped with:

·         Computer, projector, VHS and DVD players, and overhead and slide projectors.

IV

Assignments

·         Unscheduled assignments are those that can only accommodate a fraction of the class at any given time.  For this reason, students are encouraged to schedule and complete them early.  They may include viewing and critiquing videos and field trips.

1.        Nightline: Brave New World, Ted Koppel, ABC News,  8-5-99 (video on reserve).

2.       Artificial Life, VPRO Amsterdam, 3-29-95 (video on reserve).

3.       Fast, Cheap, and Out of Control, Errol Morris (theatrical video).

4.       The Thirteenth Floor, Josef Rusnak (theatrical video).

5.       USC Institute for Creative Technology, Marina del Ray (field trip).

·         Scheduled assignments are those that everyone has the resources to complete in a timely fashion.  They will be given weekly and are due at the beginning of lab.

Grading

Fall and Winter quarter lectures and labs

·         Fall grades will be deferred until the end of the Winter quarter.

·         There will be both Fall and Winter quarter final exams.

·         Grading will be based:

o        25% upon the Fall quarter final exam.

o        25% upon the Winter quarter final exam.

o        25% upon the Fall and Winter quarter assignments.

o        25% upon the Fall and Winter quarter term papers.

·         Weekly ungraded quizzes will enable students to monitor their own progress.

V

Readings

·         Textbooks (all in print paperbacks at circa $20 each):

1.        Axelrod, Robert.  1984.  The Evolution of Cooperation.  New York: Basic Books.

2.       Epstein, Josh and Rob Axtell. 1996. Growing Artificial Societies.  Cambridge: MIT Press.

3.       Hillis, Daniel.  1999.  The Pattern on the Stone.  New York: Basic Books.

4.       Levi-Strauss, Claude.  1971.  The Elementary Structures of Kinship.  Beacon Press.

5.       Schelling, Thomas C.  1978. Micromotives and Macrobehavior.  New York: W.W. Norton

·         Course Reader:

1.        Notes on Laboratory Assignments.

2.       Selected Articles.

Desktop Simulations:

·         Customizable examples written in StarLogo (freeware).

·         Customizable examples written in Borland C++ (licensed).

·         Supporting software:  SynEdit (freeware), PhotoShop (licensed), DreamWeaver (licensed), and CuteFTP (licensed).

·         Many of the software modules for this course have already been developed and used successfully with students.

Web Simulations:

·         Java Applet Review Service.  http://www.jars.com/jarssearch.html

·         The Temple of Alife.  http://alife.fusebox.com/

VI

Fall Quarter

Five Introductory Lectures:

Week 1

·        Gessler:  How we know, understand and explain the world through representations.  A broad view of simulations, computers and languages.  The building blocks of agents, senses, thoughts, actions, of the social and the physical environments.  Film clips and simulations.  Historic readings:

1.        Anon.  “Science: The Thinking Machine.”  In Time: The Weekly Magazine, January 23, 1950, pp. 54-60.

2.       Jay W. Forrester, "Counterintuitive Behavior of Social Systems".  In Technology Review, Vol. 73, No. 3, Jan. 1971, pp. 52-68.

3.       Ludwig von Bertalanffy.  “The History and Status of General Systems Theory.”  In Trends in General Systems Theory, edited by George J. Klir.  John Wiley & Sons, New York (1972) pp. 21-41.

·         Read:  Beginning where biology leaves off, cultural organization originates from kinship.  Rules for marriage and the exchange of goods and labor through reciprocity create a boundary which defines who is inside and who is outside the community. 

1.        Elementary Structures of Kinship by Claude, Beacon Press (1969 (1949)).  Lectures are based on the first 5 chapters (pp. 1 - 68).

Week 2

·         Bonacich:  The problem of social order and how it arises from individual decision-making.  How do we resolve the issue of independent and often selfish local decisions leading to negative global consequences?  Through informal dispersed social arrangements or strong central governments?  Simulations.

1.        G. Hardin. 1968.  The Tragedy of the Commons. Science 162: 1343-48.

·         Lohmann:  Collective action, the use of coordination and cooperation to achieve well-being beyond what individuals could achieve on their own.  Welfare reached through leadership, religion, emotions and norms.  Political institutions to shape social action and allow even higher levels of well-being.  Simulations.

1.        Ostrom 1990. Chapter 4 (pp. 103-142): The Los Angeles Water Pumping Race

Week 3

·         McKelvey:  What causes organizations to be the way they are?  Why do they succeed or fail?  How do they learn new ways of functioning in a changing world?  How might we winnow out bad theories in favor of more truthful ones.  The role of agent models in improving truth-finding in social science.  Simulations.

1.        Carley, K. M. and D. M. Svoboda (1996). “Modeling Organizational Adaptation as a Simulated Annealing Process,” Sociological Methods and Research, 25, 138–168.

 

Fall Quarter

Building Social Science from the Bottom-Up

Week 3

·         Bonacich:  Residential Segregation.  Simulations in StarLogo.

Week 4

·         Bonacich:  Mathematics of Musical Chairs.  Simulations in StarLogo.

·         Bonacich:  Other Families of Models.  Simulations in StarLogo.

Week 5

·         Bonacich:  Cooperation and Tit for Tat Strategies.  Simulations in StarLogo.

·         Bonacich:  Cooperation in Warfare and Biology.  Simulations in StarLogo.

Week 6

·         Bonacich:  Practical Advice.  Simulations in StarLogo.

·         Bonacich:  Conclusions.  Simulations in StarLogo.

Week 7

·         Read:  The Netsilik Eskimo and Kung! San.  How cultural rules arising from environmental conditions lead to disjunctures requiring further rules to ensure cooperation.  Simulations.

·         Read:  How different theoretical frameworks in anthropology arise from nature, accounting for the universality of forms, and culture, accounting for the variation of forms we observe in human social groups.

Week 8

·         Read:  Materialism and Idealism.  Neither the focus on external conditions, materialism, nor the focus on internal conditions, idealism, are alone sufficient explanations for culture.  How multiagent simulation provides a means to bring both into play.  Simulations.

·         Read:  Self and Induced Organization.  How does each arise?

Week 9

·         Read:  Endogamy and Exogamy.  How a community is constructed from the perspective of its participants?  The central role of kinship.

·         Read:  How exchange, a result of the incest rule, integrates groups that would otherwise be independent, into an organized community.

Week 10

·         Read:  How we may explore the consequences of the absence of cultural rules, such as incest and marriage, through multiagent simulations? 

·         Lohmann:  Coordination.  Real world cases, web experiments, and simulations.

Week 11

·         Final Exam

Winter Quarter

Week 1

·         Lohmann:  Public Goods.  Real world cases, web experiments, and simulations.

·         Lohmann:  Payoff Externalities & Information Cascades.  Real world cases, web experiments, and simulations.

Week 2

·         Lohmann:  Majority Rule & Democratic Deliberation.  Real world cases, web experiments, and simulations.

·         Lohmann:  Nested & Federalist Structures & Politics.  Real world cases, web experiments, and simulations.

Week 3

·         Lohmann:  Social Life of Information.  Real world cases, web experiments, and simulations.

·         Lohmann:  Why Decentralized Systems are Hard to Understand.  Real world cases, web experiments, and simulations.

Week 4

·         McKelvey:  The SugarScape Model and Science from the Bottom-Up.  Darwinian selectionist theory, competition and survival, and the emergence of firms as competitive entities.  SugarScape simulation.

·         McKelvey:  Ecology, Competition, Selection & Emergent Economies.  A biological analogy for the study of firms:  organizational ecologies, organizational populations, and organizational population ecology.

Week 5

·         McKelvey:  Exploitation, the assumption of a static world to be optimized, and exploration, the assumption of a changing world to be understood, and the trade-offs between cost advantage and product differentiation. 

·         McKelvey:  Organizational learning and adaptation, knowledge and human capital, networks and social capital, and the optimization of distributed intelligence.

Week 6

·         McKelvey:  Landscape design.  Hierarchy versus local action, emergent self-organization, cross-functional integration, tuning computational search spaces.

·         McKelvey:  Structuration. 

Week 7

·         McKelvey:  Emergent culture and order creation.  The coevolutionary process between theory and model development and model development and real-world phenomena.

·         Gessler:  How repeated local “random” processes produce global patterned behaviors.  How paths are formed through complex spaces: schooling, flocking, herding, and dispersion.  Video.  Using C++ to simulate moving halfway to random destinations.

Week 8

·         Gessler:  Geometric geographic distributions of resources, the problems of optimizing search behaviors and carrying capacity.  Simulation in C++.

·         Gessler:  Realistic geographic cultural and resource spaces using a digital elevation model.  The exchange of resources and materials in complex “layered” environments.  Simulation in C++.

Week 9

·         Gessler:  Acquiring geographic information, using and exchanging it, the brokering of goods and information.  Simulation in C++.

·         Gessler:  Boundaries and communities of information and goods exchange.  The control of exchange and the origins of deception.  Simulation in C++.

Week 10

·         Gessler:  Continuation of simulation in C++.  “All but war is simulation,” counterfactual analysis of the Gulf War battle of “73-Easting.”  Video. 

·         Gessler:  Karl Sims’ “Evolved Virtual Creatures,” the evolution of sensory, thought, and behavioral processes from nearly nothing.  Video.  Accomplishments and challenges of evolutionary computation, including the creation of robotic societies for planetary exploration.

Week 11

·         Final Exam

VII - Spring Seminars

Multiagent Spatial Modeling for Artificial Societies & Cultures.

Nicholas Gessler:

The seminar will build an "artificial culture," a theoretical framework incorporating the social, technological, and natural environments, and the roles of the individual and the population, shared and unshared beliefs, and material and ideational culture.  It will investigate the coevolutionary and behavioral processes at work in an attempt to integrate the strategies of Marvin Harris' cultural materialism, Lewis Binford's processual archaeology, and Marvin Minsky’s society of mind within one structure.  Emphasis will be placed on the trade and flow of goods and information and its management through emergent social processes.  Students will learn to pseudocode and code in C++ or Java.

Readings:

1.        Marvin Harris.  Theories of Culture in Postmodern Times.  Altamira 1998.

2.       Lewis Binford, Paula Sabloff.  Conversations with Lewis Binford: Drafting the New Archaeology.  Oklahoma 1998.

3.       Marvin Minsky.  The Society of Mind.

4.       Bruce Eckel.  Thinking in C++ Volume 1: Introduction to Standard C++.

5.       Bruce Eckel.  Thinking in Java.

Rationality and Society

Phil Bonacich

This seminar will examine the usefulness and limitations of models of rationality in sociology and other social sciences.  After a basic introduction to game theory we will read selected classic works that use rational choice theory or which discuss its limitations.

Readings:

1.        R. Duncan Luce and Howard Raiffa, Games and Decisions

2.       Karl Sigmund, Games of Life: Explorations in Ecology, Evolution, and Behavior

3.       Brian Skyrms, Evolution of the Social Contact

4.       James Coleman, Foundations of Social Theory

5.       Michael Hechter, Principles of Group Solidarity

6.       Elliott Sober and David Sloan Wilson, Unto Others: The Evolution and Psychology of Unselfish Behavior

7.       Mancur Olson, The Logic of Collective Action

Modeling the !Kung san and Netsilik Eskimo

Dwight Read

Topics touched upon in the Fall and Winter quarters will be expanded.  Specific ethnographic cases will be considered, such as the Netsilik Eskimo where one can easily show how cultural rules, whose origin likely lies in environmental conditions, leads to internal "disjunctures" resolved by yet other cultural rules that are needed to ensure cooperation in the specialized kind of seal hunting that must have taken place in the winter -- sealing that was based on "beating the odds" -- and how sharing had to be institutionalized as a means to ensure regularity of food supply and avoidance of internal factions or cliques.  Another example would be a multi-agent simulation based on the !Kung san that demonstrates the kind of organization that is induced by incest rules, affecting how one camp may interrelate with another camp with regard to access to resources.

Biopolitics

Susanne Lohmann

This course studies applied ethics and governance. It takes a case-based approach, mixing normative and positive perspectives. Is action X morally right or wrong? How do people reason about whether action X is morally right or wrong? How do governance structures influence how people reason about whether action X is morally right or wrong? How can we design governance structures that encourage people to act ethically, contribute to public goods, and lead productive and fulfilled lives?

Agent-Based Modeling in the Social Sciences

Lars-Erik Cederman (alternate)

Are you struggling to analyze complex social systems? Are you trying to bridge the gap between powerful, but reductionist, rationalistic models and wide-ranging, but often imprecise, qualitative macro-social theories? The most recent advances in computational modeling may provide the solution that you are looking for!  This seminar offers an introduction to agent-based computational modeling with applications to the social sciences, including political science, economics and sociology. It will cover the theoretical foundations of the method and the existing applied literature. No previous knowledge of programming is required. Yet the participants will be given opportunities to tool up and to develop their own models. The course relies entirely on a Java-based simulation package called RePast.

Readings

1.        Axelrod, R. 1997. The Evolution of Complexity. Princeton U Press.

2.       Axelrod, R. and M. Cohen. 1999. Harnessing Complexity. Free Press.

3.       Casti, J. 1997. Would-Be Worlds. Wiley. (Best popular introduction.)

4.       Cederman, L-E. 1997. The Emergence of Actors in World Politics. Princeton U Press.

5.       Epstein, J. and R. Axtell. 1996. Growing Artificial Societies. MIT Press.

6.       Holland, J. 1995. Hidden Order. Addison-Wesley

7.       Holland, J. 1998. Emergence. Addison-Wesley.

8.       Schelling, T. 1978. Micromotives and Macrobehavior. W. W. Norton.

9.       RePast, Univ. of Chicago: http://repast.sourceforge.net/

10.     Eckel, B. 1998. Thinking in Java. Prentice Hall.