Simulation in the Social Sciences
The course, MNGT0783 Simulation in the Social Sciences, is an elective course in the PhD programme at the Australian School of Business.In Session 2, 2010, the course will run between 2pm and 5pm in the Pioneer International Lecture Theatre in the AGSM Building at UNSW Kensington, on Friday afternoons. The first class will not be until August 13th.
Comments from some of the 2007 participants: Here.
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.
Classes
- 2010 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. Here.
- 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 4.1.1 was released on August 4, 2010)
- First steps with NetLogo 1.2 (obsolete version) here.
- The Institute for Modelling Complexity's NetLogo Learning Labs (2004) here.
- The 4.1.1 NetLogo User Manual and tutorials here.
- A six-page Quick Guide on NetLogo 4.0 written by Luis Izquierdo. Here.
- A NetLogo User Manual. 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.
- The TurtleZERO NetLogo Model Library Here.
- An AB NetLogo model of the Sons & Daughters (or Boys & Girls) model on page 69 of March & Lave, 1975. Here.
- A NetLogo GA model of agents learning their best risk profiles, by maximising the CARA Certain Equivalent across three two-prize lotteries. Here.
- A NetLogo GA model of agents learning their best risk profiles, by maximising the Expected CARA Utility across three two-prize lotteries. Here.
- A NetLogo GA model of agents learning their best risk profiles, by maximising the Expected CRRA Utility across three two-prize lotteries. Here.
- 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.
- 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)
- Joshua M. Epstein (2008), Why Model? Journal of Artificial Societies and Social Simulation vol. 11, no. 4 12, Here.
- Marks, R.E. (2007) Validating Simulation Models: A General Framework and Four Applied Examples, Computational Economics, 30(3): 265-290, October. Here.
http://www.springerlink.com/content/l3111153q6278403/?p=470f38ced07044e4b7afb2c5e5f400a9&pi=11- Marks, R.E. (2010) Validating simulations with historical data: the State Similarity Measure (SSM), Joint Statistics Meetings, Vancouver. 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.
- 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.
- Luis R. Izquierdo, Segismundo S. Izquierdo, José Manuel Galán and José Ignacio San (2009), Techniques to Understand Computer Simulations: Markov Chain Analysis, Journal of Artificial Societies and Social Simulation vol. 12, no. 1, 6. 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.
- Annie Abello, Sharyn Lymer, Laurie Brown, Ann Harding and Ben Phillips (2008), Enhancing the Australian National Health Survey Data for Use in a Microsimulation Model of Pharmaceutical Drug Usage and Cost, Journal of Artificial Societies and Social Simulation vol. 11, no. 3 2 Here.
- Stephen Wolfram's A New Kind of Science on-line 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.
- Francesc S. Beltran, Salvador Herrando, Doris Ferreres, Marc-Antoni Adell, Violant Estreder and Marcos Ruiz-Soler (2009), Forecasting a Language Shift Based on Cellular Automata, Journal of Artificial Societies and Social Simulation Vol. 12, no. 3, 5. 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 3D version of Life: Here.
- Class 3: Agent-based models.
G&T: Ch 8 and 9.
Bob's lecture overheads: OHs || Printable
- Mark Buchanan, This Economy Does Not Compute, NYT 1 Oct 2008. Here.
- 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.
- Nigel Gilbert, Agent-Based Models, L.A.: SAGE, 2008. The book has web pages 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.
- Nature presented several papers on Agent-Based Modelling in its issue of 6 August 2009:
- Editorial: A model approach, Nature Vol.460, 6 August 2009. Here.
- Mark Buchanan, Meltdown modelling: Could agent-based computer models prevent another financial crisis? Nature Vol.460, 6 August 2009. Here.
- J. Doyne Farmer and Duncan Foley, The economy needs agent-based modelling, Nature Vol.460, 6 August 2009.
- Josh Epstein, Modelling to contain pandemics, Nature Vol.460, 6 August 2009.
- Nigel Gilbert (2004), Agent-based social simulation: dealing with complexity , Here.
- Leigh Tesfatsion (2006), Agent-based computational economics: a constructive approach to economic theory, 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, Proc. of the National Academy of Sciences, U.S.A., 99: 7243-7250. Here.
- C.R. Shalizi, Agent-Based Modeling, Here.
- Kristina Lerman and Aram Galstyan, A General Methodology for Mathematical Analysis of Multi-Agent Systems, Here.
- S. Sunder, Markets as Artifacts: Aggregate Efficiency from Zero-Intelligence Traders, in Models of a Man: Essays in Memory of Herbert A. Simon, ed. by M. Augier and J. March, 2002. Here.
- Pietro Terna (2001), Creating Artificial Worlds: A Note on Sugarscape and Two Comments, Journal of Artificial Societies and Social Simulation 4(2). Here.
- Gilbert, N., Terna P., How to build and use agent-based models in social science, Mind & Society, no. 1, 2000, 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
- Marco A. Janssen, Lilian Na'ia Alessa, Michael Barton, Sean Bergin and Allen Lee (2008), Towards a Community Framework for Agent-Based Modelling, Journal of Artificial Societies and Social Simulation vol. 11, no. 2 6, Here.
- Michael J. North and Charles M. Macal, Managing Business Complexity: Discovering Strategic Solutions with Agent-Based Modeling and Simulation, OUP, 2007. See a review by Terna Here (via the UNSW Library).
- Gulyás, L. On the Transition to Agent-Based Modeling: Implementation Strategies From Variables to Agents, Social Science Computer Review 20(4): 389-399, 2002. Here.
- Bankes, S. Lempert, R. Popper, S., Making Computational Social Science Effective: Epistemology, Methodology, and Technology, Social Science Computer Review 20(4): 377-388, 2002. Here.
- Leigh Tesfatsion's information on application areas for agent-based model, from her course at Iowa State. Here.
- David, Sichman, and Coelho's critique: "Simulation as formal and generative social science: the very idea" 2007, Here.
- Brett Paris' agent-based modelling page at Monash 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 OpenABM Model Archive Here.
- The Icosystem Game, a simple agent-based simulation to demonstrate emergent behaviour.
- Roberto Leombruni, Matteo Richiardi, Why are economists sceptical about agent-based simulations? Physica A, 355 (2005) 103-109. Here.
- Brian Heath, Raymond Hill and Frank Ciarallo (2009), A Survey of Agent-Based Modeling Practices (January 1998 to July 2008), Journal of Artificial Societies and Social Simulation 12 (4) 9, Here.
- 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.
- The DARPA Urban Challenge page, http://www.darpa.mil/grandchallenge/index.asp Here.
and a piece in the Economist of Nov 1, 2007 Here.
- Class 4: Space and Networks
Bob's lecture overheads: OHs || Printable
- Michelle Girvan, lecture 1 on The structure and dynamics of complex networks.
- Australian sociologist Duncan Watts on the importance of peer networks in determining tastes, from the NYT April 2007. Here.
- Leigh Tesfatsion, Introductory Notes on the Structural and Dynamical Analysis of Networks, Here.
- Giorgio Fagiolo, Games on Networks: Rationality, Dynamics and Interactions, Here.
- Frank Schweitzer, Giorgio Fagiolo, Didier Sornette, Fernando Vega-Redondo, Alessandro Vespignani, and Douglas R. White, Economic Networks: The New Challenges, Science 24 July 2009: Vol. 325. no. 5939, pp. 422 - 425 Here.
- Lynne Hamill and Nigel Gilbert (2009), Social Circles: A Simple Structure for Agent-Based Social Network Models, Journal of Artificial Societies and Social Simulation vol. 12, no. 2 3, Here.
- Rob Stocker, David Cornforth and T. R. J. Bossomaier (2002), Network Structures and Agreement in Social Network Simulations, Journal of Artificial Societies and Social Simulation vol. 5, no. 4, Here.
- Paul Ormerod and Rich Colbaugh (2006), Cascades of Failure and Extinction in Evolving Complex Systems, Journal of Artificial Societies and Social Simulation vol. 9, no. 4, Here.
- Wilhite, Allen (2001), "Bilateral Trade and `Small-World' Networks" Computational Economics, Vol. 18, No. 1, August, pp. 49-64. 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
- Steven H. Strogatz, "Exploring Complex Networks", Nature, Volume 410, No. 6825, March 8, 2001. Here.
- Leigh Tesfatsion's page on Evolution of Interaction Networks, with further links Here.
- Computer Programs for Social Network Analysis. Here.
and a paper ("Software for Social Network Analysis " by Mark Huisman and Marijtje A.J. van Duijn, 3rd October 2003) comparing some of these programs, Here.- The Center for Complex Network Research (CCNR) Web page: Here.
- Class 5: Shayne Garry and System Dynamics in the social sciences.
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.
- Oliva, R., & Sterman, J. D. (2001). Cutting corners and working overtime: quality erosion in the service industry. Management Science, 47(7), 894-914. Here.
- 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.
- Agent-Based (AB) compared with System Dynamics (SD) Simulations
- For seven papers that compare the two families of simulations, see Here.
- Class 6: 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.
- Two papers on learning to be risk averse (or not): the first was published, the second not yet:
- 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.
- Stéphane Airiau, Sabyasachi Saha and Sandip Sen (2007), Evolutionary Tournament-Based Comparison of Learning and Non-Learning Algorithms for Iterated Games, Journal of Artificial Societies and Social Simulation vol. 10, no. 3, 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.
- Edmund Chattoe (1998) Just How (Un)realistic are Evolutionary Algorithms as Representations of Social Processes? Journal of Artificial Societies and Social Simulation vol. 1, no. 3 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.
- A Day Workshop on Agent-Based Modelling in Economics and Finance, December 6, 2007, Here.
Software:
- Repast.
- Swarm.
- MASON
- 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:
- John Miller's links on Computational Economic Modeling Here.
- Ken Judd's pages on Economics and Computation. Here.
- 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:
- Other courses:
- J. Padgett's Santa Fe Institute projects Here.
- US National Science Foundation (NSF) resources:
- NSF Grant Application on agent-based models of electricity power markets: Here.
- 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.)- Przeworski and Salomon's The Art of Writing Proposals (pp. 8) Social Science Research Council, 1998, Here
- 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 (E:CO) Here.
- Computational Management Science Here.
- The International Journal of Microsimulation Here.
- Cognitive Systems Research 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.
- Philip Mirowski, Markets Come to Bits: Evolution, Computation and Markomata in Economic Science, Journal of Economic Behavior and Organization, 2007; 63 (2) Here.
- Proc. of the Fourth Conf. on Human Complex Systems, UCLA, Lake Arrowhead, April 2007 Here.
- Podcast of Stanford's Deborah Gordon on "Ants, Humans, the Division of Labor, and Emergent Order" Here.
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Last Updated 13 August, 2010 Robert Marks, bobm@agsm.edu.au