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Multi-Agent Systems, Time Geography, and Microsimulations

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Systems Approaches and Their Application

Conclusion

We have argued that time geography provides a perspective that helps unify the two paradigms of (a) multi-agent systems, as developed within computer science, and (b) microsimulations, as developed within the social sciences. By identifying and defining these two paradigms, and by reasoning about the central concepts of each of them, we have taken a first step in amalgamating them. We have attempted to take a general systems approach in order to avoid myopia and jargon limitations, and hopefully avoid being too narrow in scope (an approach different from, e. g., Gimblett, 2002).

Our claim is that developments based on a synthesis of the three paradigms offer a rich potential for substantial advance of systems analysis methodology. It gives a new angle to classical problems like how to achieve consistency with the world outside a defined core system boundary, how to simultaneously represent processes on very different spatial and temporal scales, how to enable agents to concurrently obey internal and external rules, and how to integrate observable and postulated behavior while preserving achievability of endogenous emergence.

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References

  • Agre, P. & Chapman, D. (1987). pengi-An Implementation of aTheory of Activity. In Proc AAAI (pp. 268–272). San Mateo, Calif.: Morgan Kaufmann.

    Google Scholar 

  • Antcliff, S. (1993). An Introduction to DYNAMOD-A Dynamic Population Microsimulation Model. Canberra, Australia: National Centre for Social and Economic Modelling.

    Google Scholar 

  • Axelrod, R. (1997a). Advancing the Art of Simulation in the Social Sciences. In R. Conte, R. Hegselmann, & P. Terno (Eds.), Simulating Social Phenomena (pp. 21–40). Berlin: Springer Verlag.

    Google Scholar 

  • Axelrod, R. (1997b). The Complexity of Cooperation, Princeton, N.J.: Princeton University Press.

    Google Scholar 

  • Axtell, R. (2000). Why Agents? On the Varied Motivations for Agent Computing in the Social Sciences, Working Paper 17. Center on Social and Economic Dynamics, Brookings Institution.

    Google Scholar 

  • Axtell, R., Axelrod, R., Epstein, J., & Cohen, M. (1996). Aligning Simulation Models: A Case Study and Results. Computational and Mathematical Organization Theory, 1, 123–141.

    Article  Google Scholar 

  • Bertels, K. & Boman, M. (2001). Agent-Based Social Simulation in Markets. Electronic Commerce Research, 1(1–2), 149–158.

    Article  MATH  Google Scholar 

  • Boman, M. (1999). Norms in Artificial Decision Making. Artificial Intelligence and Law, 7, 17–35.

    Article  Google Scholar 

  • Boman, M. (2001). Trading Agents. AgentLink News, 6, 15–17.

    Google Scholar 

  • Boman, M., Bubenko jr., J., & Johannesson, P. (1997). Conceptual Modelling. London: Prentice-Hall.

    Google Scholar 

  • Bond, A.H. & Gasser, L. (Eds.) (1988). Readings in Distributed Artificial Intelligence. San Mateo, Calif.: Morgan Kaufmann.

    Google Scholar 

  • Bratman, M.E. (1987). Intention, Plans, and Practical Reason. Cambridge, Mass., London: Harvard University Press.

    Google Scholar 

  • Brooks, R.A. (1986). A Robust Layered Control System for a Mobile Robot. IEEE Journal of Robotics and Automation, 2(1), 14–23.

    Google Scholar 

  • Brooks, R.A. (1990). Elephants don’t play chess. In P. Maes (Ed.), Designing Autonomous Agents, Theory and Practice from Biology to Engineering and Back (pp. 3–15). Cambridge: The MIT Press.

    Google Scholar 

  • Caldwell, S. & Keister, L.A. (1996). Wealth in America: family stock ownership and accumulation 1960–95. In G.P. Clarke (1996).

    Google Scholar 

  • Carpenter, J. (2002). Evolutionary Models of Bargaining: Comparing Agent-based Computational and Analytical Approaches to Understanding Convention Evolution. Computational Economics 19(1), 25–49.

    Article  MATH  Google Scholar 

  • Castelfranchi, C. (1998). Modelling Social Action for ai Agents. Artificial Intelligence, 103(1–2), 157–182.

    Article  MATH  Google Scholar 

  • Clarke, G.P. (Ed.) (1996). Microsimulation for Urban and Regional Policy Analysis. European Research in Regional Science, 6, 88–116.

    Google Scholar 

  • Clarke, M. & Wilson, A.G. (1986). A framework for dynamic comprehensive urban models: the integration of accounting and Microsimulation approaches. Sistemi Urbani, 213, 145–177.

    Google Scholar 

  • Clarke, M. & Holm, E. (1987). Micro-simulation methods in human geography and planning: a review and further extensions. Geografiska Annaler, 69B, 145–164.

    Google Scholar 

  • Dean, T. & Boddy, M. (1988). An Analysis of Time-Dependent Planning. In Proc AAAI (pp. 49–54). St. Paul MN.

    Google Scholar 

  • Dennett, D.C. (1978). Brainstorms-Philosophical Essays on Mind and Psychology. Cambridge, Mass.: The MIT Press.

    Google Scholar 

  • Durlauf, S.N. (1999). How can Statistical Mechanics Contribute to Social Science? Proc Natl Acad Sci USA, 96, 10582–10584.

    Article  PubMed  MathSciNet  MATH  CAS  ADS  Google Scholar 

  • Epstein, J.M. & Axtell, R. (1996). Growing Artificial Societies-Social Science From theBottom Up. Washington DC: The Brookings Institution.

    Google Scholar 

  • Fagin, R., Halpern, J.V., Moses, Y., & Vardi, M.Y. (1995). Reasoning About Knowledge. Cambridge, Mass.: The MIT Press.

    MATH  Google Scholar 

  • Genesereth, M. & Ketchpel, S. (1994). Software Agents. Communications of the ACM, 37(7), 48–53.

    Article  Google Scholar 

  • Georgeff, M.P. & Lansky, A.L. (1987). Reactive Reasoning and Planning. In Proc AAAI’87 (pp. 677–682). Seattle WA.

    Google Scholar 

  • Giddens, A. (1984). The Constitution of Society-Outline of the Theory of Structuration. Berkeley: University of California Press.

    Google Scholar 

  • Gilbert, N. & Troitzsch, K.G. (1999). Simulation for the Social Scientist. Buckingham: Open University Press.

    Google Scholar 

  • Gimblett, H.R. (Ed.) (2002). Integrating Geographic Information Systems and Agent-Based Modeling Techniques for Simulating Social and Ecological Processes. New York, Oxford: Oxford University Press.

    Google Scholar 

  • Habermas, J. (1981). Theorie des Kommunikativen Handels. Frankfurt AM Main: Suhrkamp Verlag.

    Google Scholar 

  • Hägerstrand, T. (1953). Innovationsförloppet ur kronologisk synpunkt. Meddelanden från Lunds universitets geografiska institution, avhandlingar XXV. Lund University.

    Google Scholar 

  • Hägerstrand, T. (1975a). Space, time and human condition. In A. Karlqvist, L. Lundqvist, & F. Snickars (Eds.), Dynamic Allocation of Urban Space (pp. 2–12). Farnborough: Saxon House.

    Google Scholar 

  • Hägerstrand, T. (1975b). Survival and arena: on the life-history of individuals in relation to their geographical environment. Monadnock, 49, 9–29.

    Google Scholar 

  • Hägerstrand, T. (1995). Action in the physical everyday world. In A.D. Cliff, P. Gould, A. Hoare, & N. Thrift (Eds.), Diffusing Geography: Essays for Peter Haggett, Blackwell.

    Google Scholar 

  • Holm, E. & Sander, L. (2001). Modèles spatiaux de microsimulation. In L. Sander (Ed.), Modèles en analyse spatiale. Lavoisier.

    Google Scholar 

  • Holm, E., Mäkilä, K., & Öberg, S. (1989). Tidsgeografisk handlingsteori-Att bilda betingade biografier. GERUM Rapport No. 8. Umeå: University of Umeå.

    Google Scholar 

  • Holm, E., Lindgren, U., & Malmberg, G. (2000). Dynamic Microsimulation. In A.S. Fotheringham & M. Wegener (Eds.), Spatial Models and GIS: New Potential and New Models (pp. 143–165). GISDATA Series 7. London: Taylor & Francis.

    Google Scholar 

  • Huberman, B.A. & Glance, N.S. (1993). Evolutionary Games and Computer Simulations. Proc Natl Acad Sci USA, 90, 7716–7718.

    Article  PubMed  CAS  ADS  MATH  Google Scholar 

  • Kaelbling, L. & Rosenschein, S.J. (1990). Action and Planning in Embedded Agents. In P. Maes (Ed.), Designing Autonomous Agents: Theory and Practice from Biology to Engineering and Back (pp. 35–48). Cambridge, Mass.: The MIT Press.

    Google Scholar 

  • Krupp, H.-J. (1986). Potential and limitations of Microsimulation models. In G.H. Orcutt, J. Mertz, & H. Quinke (Eds.), Microanalytic Simulation Models to Support Social and Financial Study. Amsterdam, New York: North-Holland.

    Google Scholar 

  • Langton, C. (1986). Studying Artificial Life with Cellular Automata. In D. Farmer, A. Lapedes, N. Packard, & B. Wendroff (Eds.), Evolution, Games and Learning (pp. 120–149). Amsterdam: North-Holland.

    Google Scholar 

  • LeBaron, B. (2000). Agent Based Computational Finance-Suggested Readings and Early Research. Economic Dynamics and Control, 24(5–7), 679–702.

    Article  MATH  Google Scholar 

  • Leontief, W.W. (1951). The Structure of the American Economy, 1919–1939: an Empirical Application of Equilibrium Analysis. New York: Oxford University Press.

    Google Scholar 

  • Lesser, V. (Ed.) (1995). Proc First Intl Conf on Multi-Agent Systems. San Mateo, Calif.: Morgan Kaufmann.

    Google Scholar 

  • Maes, P. (1991). The Agent Network Architecture (ANA), SIGART Bulletin, 2(4), 115–120.

    Article  Google Scholar 

  • Mas-Colell, A., Whinston, M.D., & Green, J.R. (1995). Microeconomic Theory. New York: Oxford University Press.

    Google Scholar 

  • Merz, J. (1991). Microsimulation-a survey of principles, developments and applications. International Journal of Forecasting, 7, 77–104.

    Article  Google Scholar 

  • Möhring, M. & Troitzsch, K.G. (2001). Lake Anderson Revisited by Agents. Artificial Societies and Social Simulation, 4(3). Retrieved on February 3, 2002, from http://www.soc.surrey.ac.uk/JASSS/4/3/1.html.

  • Nakamura, A., Nakamura, M., & Orcutt, G.H. (1976). Testing for relationship between timeseries. Journal of the American Statistical Association, 71, 214–222.

    Article  MATH  Google Scholar 

  • Newell, A. & Simon, H.A. (1961). GPS, a Program that Simulates Human Thought. In R. Billing (Ed.), Lernende Automaten (pp. 109–124). Oldenbourg.

    Google Scholar 

  • O’Donoghue, C. (2001). Dynamic Microsim ulation-A Methodological Survey. Brazilian Electronic Journal of Economics, 4(2), December. Retrieved on February 3, 2002, from http://www.beje.decon.ufpe.br/v4n2/v4n2.htm.

  • O’Sullivan, D. & Haklay, M. (2000). Agent-Based Models and Individualism-Is the World Agent-Based? Environment and Planning A, 32, 1409–1425.

    Google Scholar 

  • Orcutt, G.H. (1957). A new type of socio-economic system. Review of Economics and Statistics, 58, 773–794.

    Google Scholar 

  • Orcutt, G.H. (1986). Views on microanalytic simulation modeling. In G.H. Orcutt, J. Mertz, & H. Quinke (Eds.), Microanalytic Simulation Models to Support Social and Financial Study. Amsterdam, New York: North-Holland.

    Google Scholar 

  • Orcutt, G.H. & Cochrane, D. (1949). A sampling study of the merits of the autoregressive and reduced form transformations in regression analysis. Journal of the American Statistical Association, 44, 356–372.

    Article  MATH  Google Scholar 

  • Orcutt, G.H., Greenberger, M., Korbel, J., & Rivlin, A. (1961). Microanalysis of Socio-economic Systems: A Simulation Study. New York: Harper & Row.

    Google Scholar 

  • Orcutt, G.H., Caldwell, S., & Wertheimer II, R. (1976). Policy Exploration Through Microanalytic Simulation. Washington DC: Urban Institute.

    Google Scholar 

  • Parunak, H.V.D., Savit, R., & Riolo, R.L. (1998). Agent-Based Modeling vs. Equation-Based Modeling: A Case Study and Users’ Guide. In Proc MABS’98 (pp. 10–25), INCS 1534. Berlin: Springer-Verlag.

    Google Scholar 

  • Rao, A.S. & Georgeff, M. (1995). BDI Agents-From Theory to Practice. In V. Lesser (1995), pp. 312–319.

    Google Scholar 

  • Rosenschein, S.J. & Kaelbling, L. (1986). The Synthesis of Digital Machines with Provable Epistemic Properties. In J.V. Halpern (Ed.), Proc Theoretical Aspects of Reasoning About Knowledge (pp. 83–98). San Mateo, Calif.: Morgan Kaufmann.

    Google Scholar 

  • Searle, J.R. (1969). Speech Acts-An Essay in the Philosophy of Language. Cambridge University Press.

    Google Scholar 

  • Shaw, A. (2000). CORSIM Analyst Documentation. Retrieved February 3, 2002, from http://www.strategicforecasting.com/docs.

  • Smith, R. (1980). The Contract Net Protocol-High-Level Communication and Control in a Distributed Problem Solver. IEEE Transactions on Computers, 29(12), 1104–1113.

    Article  Google Scholar 

  • Steels, L. (1990). Cooperation Between Distributed Agents through Self Organization. In Y. Demazeau & J.-P. Müller (Eds.), Decentralized AI (pp. 175–196). Amsterdam: North-Holland.

    Google Scholar 

  • Stone, R. (1966). Mathematics in the Social Sciences and Other Essays. London: Chapman & Hall.

    Google Scholar 

  • Tinbergen, J. (1939). Statistical Testing of Business Cycle Theories Vol. 2, Business Cycles in the United States of America 1919–1932. Geneva: League of Nations.

    Google Scholar 

  • Verhagen, H.J.E. (2000). Norm Autonomous Agents. Dissertation. Stockholm: Dept of Computer & Systems Sciences, Stockholm University.

    Google Scholar 

  • Wegener, M. & Spiekermann, K. (1996). The potential of microsimulation for urban models. In G.P. Clarke (Ed.), Microsimulation for Urban and Regional Policy Analysis (pp. 88–116). European Research in Regional Science 6. London: Pion.

    Google Scholar 

  • Wellman, M.P. (1993). A Market-Oriented Programming Environment and its Application to Distributed Multicommodity Flow Problems. Journal of Artificial Intelligence Research, 1, 1–23.

    MATH  Google Scholar 

  • Winograd, T. & Flores, F. (1986). Understanding Computers and Cognition. Norwood NJ: Ablex Pub. Corp.

    MATH  Google Scholar 

  • Wooldridge, M. (2000). Reasoning about Rational Agents. Cambridge, Mass., London: The MIT Press.

    MATH  Google Scholar 

  • Wurman, P.R., Wellman, M.P., & Walsh, W.E. (1998). The Michigan Internet AuctionBot: A Configurable Auction Server for Human and Software Agents. In Proc Conf Autonomous Agents (pp. 301–308). New York: Association for Computing Machinery.

    Google Scholar 

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Boman, M., Holm, E. (2004). Multi-Agent Systems, Time Geography, and Microsimulations. In: Olsson, MO., Sjöstedt, G. (eds) Systems Approaches and Their Application. Springer, Dordrecht. https://doi.org/10.1007/1-4020-2370-7_4

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