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: