Simulation in the Social Sciences

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The course, Simulation in the Social Sciences, is an elective course on the PhD programme at the Australian School of Business.

In 2007, the course will run on five Wednesday afternoons in the Boral Lecture Theatre in the AGSM Kensington building, from 2pm October 10th.

Simulation of social interactions focuses on the complex adaptive behavior that emerges in social systems. To better understand the behavior of such complex adaptive social systems, "artificial worlds" composed of interacting adaptive agents can be created and analyzed. Such models often exhibit properties that are strikingly similar to the actual social world, e.g., cooperation, social norms, and social stratification into different classes, and provide a unique window into understanding such phenomena. Using simulation methods, previously inaccessible, yet fundamental, questions are now becoming amenable to analysis. There is much research to be done in this area---along with creating and understanding these types of complex systems, efforts need to be directed toward developing accessible versions of these models for the classroom.

The Fourth Herbert Simon Lecture Series presented by Bob in Taiwan in October 2005 Here.

The New England Complex Systems Institute course Complex Physical, Biological and Social Systems, delivered by Prof. Yaneer Bar-Yam, President, NECSI, December 5-9, 2005, at the AGSM Here.

The course: Complex Systems: Beyond the Metaphor was offered in February 2007, organised by the School of Mathematics, UNSW, at the AGSM and co-sponsored by COSNet (the ARC Complex Open Systems Research Network), MASCOS (the ARC Centre of Excellence for Mathematics and Statistics of Complex Systems), and AMSI (the Australian Mathematical Sciences Institute). Here. Bob presented three 90-minute lectures.

• Subject Outline (PDF)
• Text: Nigel Gilbert and Klaus G. Troitzsch, Simulation for the Social Scientist, Buckingham: Open University Press, 2nd edition, 2005. See its web site here.
• and also:
  John H. Miller & Scott E. Page, Complex Adaptive Systems: An Introduction to Computational Models of Social Life, Princeton Uni. Press, 2007.

• A glossary of terms encountered in this course is here.
• An informative reading on the Monte Carlo method here.
• NetLogo is a programmable environment for exploring the workings of decentralised systems, specifically designed to be user-friendly for students: here. (NetLogo 4.0 was released on September 25, 2007.)
  • The latest NetLogo User Manual and tutorials here.
  • The NetLogo programs in Chapters 7-10 of Gilbert & Troitzsch here. (Run them from your browser, or download them.)
  • NetLogo models contributed by the user community here.
  • A NetLogo model of the Sons & Daughters (or Boys & Girls) model on page 69 of March & Lave, 1975. Here.

Classes

• Class 1: Introduction,
  Simulations: concepts and grand challenges.
  G&T: Ch 1 and 2.
  Bob's Model and Simulation lecture overheads, after March & Lave: OHs || Printable
  • James G. March, Charles A. Lave Introduction to Models in the Social Sciences, New York: HarperCollins, 1975; University Press of America, 1992. Extract Here.
    • Execute my NetLogo simulation of March & Lave's Boys & Girls model Here.
  • Miller & Page, Chapter 3, Modeling.
  • Axelrod: Advancing the art of simulation in the social sciences (revised 2005) reprinted in the Handbook of Research on Nature-Inspired Computing for Economics and Management, Jean-Philippe Rennard (Ed.), Hersey, PA: Idea Group, 2006. Here
    • The Replication of Eight Simulation Models from Axelrod 2003: Here.
  • Marks, R.E. (2007) Validating Simulation Models: A General Framework and Four Applied Examples, forthcoming Computational Economics. Here.
  • Midgley D.F., Marks R.E., and Kunchamwar D. (2007) The Building and Assurance of Agent-Based Models: An Example and Challenge to the Field, Journal of Business Research, 60: 884-893. Here.
  • A. Hailu & S. Schilizzi, Are Auctions More Efficient Than Fixed Price Schemes When Bidders Learn? Australian Journal of Management, 29(2): 147-168, December 2004. Here
  • Andrea Schertler reviews Simulation for the Social Scientist by Gilbert and Troitzsch, 1999, Here.
  • R. M. Burton, Computational laboratories for Organizational Science: questions, validity and docking, Computational & Mathematical Organizational Theory 9: 91-108, 2003. Here.
  • R. Axtell, R. Axelrod, J.M. Epstein, and M.D. Cohen, Aligning Simulation Models: A Case Study and Results, Computational & Mathematical Organization Theory 1(2): 123-141, 1996. Here.
  • Kauffman, S., (1995). At Home in the Universe: The Search for Laws of Self-Organization and Complexity. Oxford and New York: Oxford University Press. See especially 252-64. (handout)
  • Schelling, T. (1978). Micromotives and macrobehavior. New York: W. W. Norton. (See especially 137-55.) (handout)
  • J.P. Marney and Heather F.E. Tarbert (2000), Why do simulation? Towards a working epistemology for practitioners of the dark arts, Journal of Artificial Societies and Social Simulation vol. 3, no. 4, , Here.
  • Pietro Terna reviews Dynamics of Organizations: Computational Modeling and Organization Theories, edited by Lomi and Larsen, Cambridge, MA: The M. I. T. Press, 2001 Here.
  • Paul E. Johnson reviews Tools and Techniques for Social Science Simulation, edited by Suleiman, Troitzsch & Gilbert, 2000. Here.
  • Robert Marks, Simulating economics, Australian Journal of Management, Vol. 27, No. 1, June 2002, pp. i-iii. Here.
  • Robert Marks, Models rule, Australian Journal of Management, Vol. 28, No. 1, June 2003, pp. i-iii. Here.
  • Scott Moss, Canonical Task Environments for Social Simulation: Here.
  • Magda Fontana, Simulation in Economics: Evidence on Diffusion and Communication, Here.
  • Nadav M. Shnerb, Yoram Louzoun, Eldad Bettelheim, and Sorin Solomon, The importance of being discrete: Life always wins on the surface, PNAS September 12, 2000, vol. 97, no. 19. Here.

• Class 2: Micro-analysis and Cellular automata.
  G&T: Ch 4 and 7.
  • Miller & Page, Chapter 8, Complex Adaptive Systems in One Dimension
  • Sauerbier, T. (2002), UMDBS - A new tool for dynamic microsimulation, Journal of Artificial Societies and Social Simulation, vol. 5, no. 2: Here.
  • Laurie Brown and Ann Harding (2002), Social Modelling and Public Policy: Application of Microsimulation Modelling in Australia, Journal of Artificial Societies and Social Simulation, vol. 5, no. 4 Here.
  • Rainer Hegselmann and Andreas Flache (1998), Understanding Complex Social Dynamics: A Plea For Cellular-Automata-Based Modelling, Journal of Artificial Societies and Social Simulation, vol. 1, no. 3: Here.
  • Phillip Bonacich, The Evolution of Exchange Networks: A Simulation Study, Journal of Social Structure vol. 2, no. 5, 2001, Here.
  • Parker, D. C. 1999. Landscape Outcomes in a Model of Edge-Effect Externalities: A Computational Economics Approach. Santa Fe, NM: Santa Fe Institute Publication 99-07-051 E. Here.
  • Hegselmann reviews Simulating Society - A Mathematica Toolkit for Modeling Socioeconomic Behavior by Richard J. Gaylord and Lou D'Andria (NY: TELOS/Springer Verlag), 1998: AGSM 302.0113/1 Here.
    "The models in the book are basically two-dimensional cellular automata. What the book is about therefore is, how to do cellular-automata-based modelling of social dynamics with Mathematica."
  • Play the Game of Life on-line:
    • Paul Callaghan's page (many dead buttons). Here.
    • A Game of Life exhibit, with many patterns: Here.
      Examine the MWSSGUN. Be sure to look in the Patterns Collection under "Rakes and Breeders" and "Ships and Trains" -- but don't stop there because there are lots more interesting things to do.
    • On-line tool for playing with the Game of Life Here.
      A Java-based program with which to study the Game of Life. Play this game at least five times. When you see the game either stop or cycle, hit the "Pause" button, and record how many generations have been played and how many are in the population.
      What would happen if this game board wrapped around, instead of ending at the edges?
    • A Probabilistic Life Here.
    • A 3D version of Life: Here.

• Class 3: Agent-based models.
  G&T: Ch 8 and 9.
  Bob's lecture overheads: OHs || Printable
  • Miller & Page, Chapter 6, Why Agent-Based Objects?
  • Agent-based simulation comes of age, OR/MS Today, August 2006, pp 34-38. Here.
  • Robert Axelrod and Leigh Tesfatsion, On-Line Guide for Newcomers to Agent-Based Modeling in the Social Sciences, in Leigh Tesfatsion and Kenneth L. Judd (Eds.), Handbook of Computational Economics, Vol. 2: Agent-Based Computational Economics, Handbooks in Economics Series, North-Holland, Amsterdam, 2006. Here.
    • Web support materials (readings and demonstration software). Here.
    • Leigh's Trento materials Here.
  • J.M. Epstein, Remarks on the Foundations of Agent-Based Generative Social Science, Handbook of Computational Economics, Volume 2: Agent-Based Modeling, edited by Leigh Tesfatsion and Kenneth L. Judd, Amsterdam: Elsevier Science, 2006. Here.
  • Nigel Gilbert (2004), Agent-based social simulation: dealing with complexity , Here.
  • John Miller and Scott Page (2004), The standing ovation problem, Complexity, vol 9 no 5: 8-16. Here.
  • Adaptive Agents, Intelligence, and Emergent Human Organization: Capturing Complexity through Agent-Based Modeling: a supplement of the PNAS 99(3): May, 2002: Contents listed here, including:
    • E. Bonabeau, Agent-based modeling: Methods and techniques for simulating human systems, Proc. National Academy of Sciences, 99(suppl. 3): 7280-7287, 2002. Here.
    • Epstein, Joshua M., John D. Steinbruner, and Miles T. Parker (2002), Modeling Civil Violence: An Agent-Based Computational Approach, Proceedings of the National Academy of Sciences, U.S.A., 99: 7243-7250. Here.
  • Markets as Artifacts: Aggregate Efficiency from Zero-Intelligence Traders - S Sunder - Models of a Man: Essays in Memory of Herbert A. Simon, 2002 - http://www.som.yale.edu/Faculty/sunder/MarketArtifact/MarketArtifact.pdf
  • Pietro Terna (2001), Creating Artificial Worlds: A Note on Sugarscape and Two Comments, Journal of Artificial Societies and Social Simulation 4(2). Here.
  • Sallach D.L., Macal C.M., Introduction: The Simulation of Social Agents, Social Science Computer Review, 19(3): 245-248, August 2001. Here.
  • Robert Axtell: Why agents? On the varied motivations for agent computing in the social sciences, Center on Social and Economic Dynamics Working Paper No. 17, November 2000, in Agent Simulation: Applications, Models, and Tools, 1999. Here.
  • Marks: Market Design using Agent-Based Models, in Leigh Tesfatsion and Kenneth L. Judd (Eds.), Handbook of Computational Economics, Vol. 2: Agent-Based Computational Economics, Handbooks in Economics Series, North-Holland, Amsterdam, 2006. Here
  • Duncan A. Robertson: Agent-Based Models of a Banking Network as an Example of a Turbulent Environment: The Deliberate vs. Emergent Strategy Debate Revisited, EMERGENCE, 5(2), 56-71, 2003. Here
  • Gulyás, L. On the Transition to Agent-Based Modeling: Implementation Strategies From Variables to Agents, Social Science Computer Review 20(4): 389-399, 2002.
  • Bankes, S. Lempert, R. Popper, S., Making Computational Social Science Effective: Epistemology, Methodology, and Technology, Social Science Computer Review 20(4): 377-388, 2002.
  • Leigh Tesfatsion's information on application areas for agent-based model, from her course at Iowa State. Here.
  • SimWorld Ltd, a UK consulting company that includes "Innovative Business Solutions in Forecasting, Optimisation and Planning. Neural Nets, Genetic Algorithms, Fuzzy Logic and Complex Adaptive Systems Simulations."
  • The Icosystem Game, a simple agent-based simulation to demonstrate emergent behaviour.
  • AnyLogic software Java demos on Social Dynamics. Here.
  • Roberto Leombruni, Matteo Richiardi, Why are economists sceptical about agent-based simulations? Physica A, 355 (2005) 103-109.
  • Agent-Based Modelling Resources in Wikipedia. Here.
  • Gintis reviews Handbook of Computational Economics, Volume II: Agent-Based Computational Economics, Leigh Tesfatsion and Kenneth L. Judd (eds.) Elsevier/North-Holland: Amsterdam, 2006, Here.

• Class 4: Space and Networks
  G&T: Ch 3.

  I would suggest you read the chapters/articles in the following order. The session is intended to give you a broad overview of: 1) What are the defining characteristics of system dynamics as a modeling approach and 2) How has system dynamics modeling been used to advance and test theory in management research.

  • Repenning, N. (2002), A Simulation-Based Approach to Understanding the Dynamics of Innovation Implementation. Organization Science, 13(2), 109-127. Here.
    • Download the model: Here.
    • Read the Appendix: Here.
  • Oliva, R., & Sterman, J. D. (2001). Cutting corners and working overtime: quality erosion in the service industry. Management Science, 47(7), 894-914. Here. (handout)
    • Download the model Here.
  • Paiche, M., & Sterman, J. D. (1993). Boom, Bust, and failures to learn in experimental markets. Management Science, 39(12), 1439-1458. Here.
  • Black, L. J., Carlile, P. R., & Repenning, N. P. 2005. A Dynamic Theory of expertise and occupational boundaries in new technology implementation: Building on Barley's study of CT Scanning. Administrative Science Quarterly , 49: 572-607. Here.
  • Oliva, R. 2003. Model calibration as a testing strategy for system dynamics models. European Journal of Operational Research, 151: 552-568. Here.
  • Repenning, N. P. & Sterman, J. D. 2001. Nobody ever gets the credit for fixing problems that never happened: Creating and sustaining process improvement. California Management Review, 43(4): 64. Here.
  • Rudolph, J. W. & Repenning, N. P. 2002. Disaster dynamics: understanding the role of quantity in organizational collapse. Administrative Science Quarterly, 47: 1-30. Here.
  • Sterman, J. D., Reppenning, N., & Kofman. 1997. Unanticipated Side Effects of Successful Quality Programs. Management Science, 43(4): 503-521. Here.
  • You can access the models used in articles 2 and 3 on the course web sites. You can open both models using the Vensim modeling software which can be downloaded free for educational purposes at the following web site:
    Download a free version of Vensim PLE software: Here.

• Class 5: Learning and evolutionary models: Genetic Algorithms and Neural Nets.
  G&T: Ch 10.
  Bob's lecture overheads: OHs || Printable
  • Miller & Page, Chapter 10, Evolving Automata
  • Duffy J., Agent-Based Models and Human Subject Experiments, in Leigh Tesfatsion and Kenneth L. Judd (Eds.), Handbook of Computational Economics, Vol. 2: Agent-Based Computational Economics, Handbooks in Economics Series, North-Holland, Amsterdam, 2006. Here.
  • Szpiro G., The emergence of risk aversion, Complexity, 2(4), 31-39, 1997. Here.
  • Marks, R.E. (1988) Niche strategies: the Prisoner's Dilemma computer tournaments revisited, Here.
  • Marks, R.E. (1992) Breeding optimal strategies: optimal behaviour for oligopolists, Journal of Evolutionary Economics, 2: 17-38. Here.
  • Midgley, D.F., Marks, R.E., and Cooper, L.G. (1997) Breeding competitive strategies, Management Science, 43(3): 257-275. [The lead article.]
  • Marks, R.E. (1998), Evolved perception and behaviour in oligopolies, Journal of Economic Dynamics and Control. 22(8-9): 1209-1233. Here.
  • Marks R.E., Midgley D.F., Cooper L.G., 2006, Co-evolving better strategies in oligopolistic price wars, Here.
  • Robert E. Marks, Hermann Schnabl, 1999, Genetic Algorithms and Neural Networks: A Comparison Based on the Repeated Prisoner's Dilemma, Here.
  • Robert Hoffmann (2000), Twenty Years on: The Evolution of Cooperation Revisited, Journal of Artificial Societies and Social Simulation vol. 3, no. 2, Here.
  • Marks reviews Neural Networks for Economic and Financial Modelling, by A. Beltratti, S. Margarita and P. Terna, London: International Thomson Computer Press, 1996. Here.
  • Daniel John Zizzo reviews Neural Networks: An Introductory Guide for Social Scientists by G. David Garson, London: Sage Publications, 1998: Here.
  • The Uses of Genetic Programming in Social Simulation: A Review of Five Books by Bruce Edmonds, Here.
  • Carl Henning Reschke (2001), Evolutionary Perspectives on Simulations of Social Systems, Journal of Artificial Societies and Social Simulation vol. 4, no. 4 Here.
  • Nicolaas J. Vriend, An illustration of the essential difference between individual and social learning, and its consequences for computational analyses, Journal of Economic Dynamics & Control, vol, 24: 1-19, 2000 Here.
  • N.N. Schraudolf & J.J. Grefenstette, A User's Guide to GAucsd 1.4, Technical Report CS92-249, CSE Department, UC San Diego, 1992 Here.

• Class: Networks
  • Australian sociologist Duncan Watts on the importance of peer networks in determining tastes, from the NYT April 2007. Here.
  • Networks and Markets by James E. Rauch, Alessandra Casella Author(s) of Review: Ezra W. Zuckerman Journal of Economic Literature, Vol. 41, No. 2 (Jun., 2003), pp. 545-565

Software:

• RePast.
  • Download Repast software from Here.
  • Leigh Tesfatsion's RePast Study Group: Here.
  • First steps with RePast : Here
  • D.A. Robertson, Using RePast to Simulate Banking Firms in a Turbulent Environment: Here.
  • Druckenmiller et al on Agent-Based Modeling and Simulation of Strategic Scenarios with RePast 2.0. Here
• Swarm.
  • Swarm home page (a Wiki): Here.
  • Pietro Terna (1998), Simulation Tools for Social Scientists: Building Agent-Based Models with SWARM, Journal of Artificial Societies and Social Simulation vol. 1, no. 2. Here.
• MASON
  • MASON's home page Here
    (A Java-based platform for experienced Java coders.)
  • MASON's documentation is Here.
• Blake LeBaron, Building the Santa Fe Artificial Stock Market, Here.
• Robert Tobias and Carole Hofmann (2004), Evaluation of free Java-libraries for social-scientific agent based simulation, Journal of Artificial Societies and Social Simulation vol. 7, no. 1 Here.
• Dugdale's An Evaluation of Seven Software Simulation Tools for Use in the Social Sciences: Here.

Other links:

• Some student projects from the First World Congress on Social Simulation. Here.
• Complete text of Dynamics of Complex Systems, by Yaneer Bar-Yam, (Westview, 2003) in PDF Here.
• Leigh Tesfatsion's web resources:
  • Leigh Tesfatsion's Computational Laboratories and links: Here.
  • Leigh Tesfatsion's links to other simulation courses: Here.
• Other courses:
  • A similar PhD course at Wharton. Here.
  • A similar PhD course at Carnegie Mellon. Here.
  • Koblenz pages:
    • Modelling and Simulation -- Koblenz: Here.
    • 2005 Koblenz seminar: Here.
• J. Padgett's Santa Fe Institute projects Here.
• US NSF resources:
  • Sample NSF Grant Application on "Human and Social Dynamics of Renaissance Florence": Here.
  • Some Guidelines from the NSF Here.
    (From http://www.nsf.gov/pubs/gpg/nsf04_23/nsf04_23.pdf.)
• Simulating ancient settlement systems: Here.
• Jeff Brantingham's use of agent-based modelling in archæology. Here.
• Journals:
  • The Journal of Artificial Societies and Social Simulation: Here
  • Social Science Computer Review: Here and here.
  • Computational & Mathematical Organization Theory Here.
  • The Journal of Economic Interaction and Coordination Here.
  • The Electronic Journal of Evolutionary Modeling and Economic Dynamics (e-JEMED) Here.
  • Emergence: Complexity & Organization Here.
  • Computational Management Science Here.
  • The International Journal of Microsimulation Here.
• The University of Michigan's Center for the Study of Complex Systems Computer Lab documentation: Here.
• Applications of AI to the Social Sciences. Here.
• Centre for Research on Simulation in the Social Sciences Here.
• Edmund Chattoe's Simulation Bibliography: Here.
• Philip Mirowski, Markets Come to Bits: Evolution, Computation and Markomata in Economic Science, Draft 1.3, November 2004. Here.
• Proceedings of the Fourth Conference on Human Complex Systems, UCLA, Lake Arrowhead, April 2007 Here.

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Last Updated 27 July, 2006

Robert Marks, bobm@agsm.unsw.edu.au