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Economics as Distributed Computation

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Abstract

In human societies diverse people act purposively with powerful but limited cognitive processes, interacting directly with one another through technologically-facilitated and physically-mediated social networks. Agent-based computational modeling takes these features of humanity—behavioral heterogeneity, bounded rationality, network interactions—at face value, using modern object-oriented programming techniques to create agent populations that have a high degree of verisimilitude with actual populations. This contrasts with mathematical social science, where fantastic assumptions render models so cartoon-like as to beg credibility—stipulations like identical agents (or a single ‘representative’ agent), omniscient agents (who accurately speculate about other agents), Nash equilibrium (macro-equilibrium arising from agent-level equilibrium) and even the denial of direct agent-agent interaction (as in general equilibrium theory, where individuals interact only with a metaphorical auctioneer). There is a close connection between agent computing in the positive social sciences and distributed computation in computer science, in which individual processors have heterogeneous information that they compute with and then communicate to other processors. Successful distributed computation yields coherent computation across processors. When such distributed computations are executed by distinct software objects instead of physical processors we have distributed artificial intelligence. When the actions of each object can be interpreted as in its ‘self interest’ we then have multi-agent systems, an emerging sub-field of computer science. Viewing human society as a large-scale distributed system for the production of individual welfare leads naturally to agent computing. Indeed, it is argued that agents are the only way for social scientists to effectively harness exponential growth in computational capabilities.

Keywords

  • economics
  • distributed computation
  • multi-agent systems

Preliminary versions of this paper were presented at the U.S. National Academy of Sciences colloquium “Adaptive Agents, Intelligence and Emergent Human Organization: Capturing Complexity through Agent-Based Modeling” held at the Arnold and Mabel Backman Center in Irvine, California (October 2001), the Third Trento Summer School on Adaptive Economics, held at the Computable and Experimental Economics Laboratory at the University of Trento, Italy (July 2002), and at the Agent-Based Approaches to Economic and Social Complex Systems (AESCS), Tokyo, Japan (August 2002).

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References

  • Aaron H (1994) Public policy, values, and consciousness. J Econ Persp vol 8 no 2: 3–21.

    CrossRef  Google Scholar 

  • Anderson PW (1972) More is different. Science vol 177 no 4047: 393–396.

    CrossRef  Google Scholar 

  • Angeles PA. (1981) Dictionary of Philosophy. Barnes and Noble: New York.

    Google Scholar 

  • Arthur WB, Holland JH, LeBaron B, Palmer R, Tayler P (1997) Asset pricing under endogenous expectations in an artificial stock market. In: Arthur WB et al. (eds) The Economy as an Evolving Complex System vol. II. Addison-Wesley: Reading, Mass.

    Google Scholar 

  • Axelrod R (1997) The Complexity of Cooperation. Princeton Univ Press: Princeton, NJ.

    Google Scholar 

  • Axtell RL (2002) The complexity of exchange. Working paper. Center on Social and Economic Dynamics, The Brookings Institution, Washington, DC; available online at www.brookings.edu/dynamics/papers.

    Google Scholar 

  • Axtell RL (2001) Effect of interaction topology and activation regime in several multi-agent models. In: Moss S and Davidsson P (eds) Multi-Agent-Based Simulation. Springer Lecture Notes on Artificial Intelligence vol 1974. Springer-Verlag: NY.

    Google Scholar 

  • Axtell RL (1999) The emergence of firms in a population of agents: local increasing returns, unstable Nash equilibria, and power law size distributions. Working paper 99-03-019. Santa Fe Institute: Santa Fe, NM; available at www.brookings.edu/dynamics/papers.

    Google Scholar 

  • Axtell RL (1992) Theory of Model Aggregation for Dynamical Systems. Ph.D. dissertation, Carnegie Mellon University: Pittsburgh, Penn.

    Google Scholar 

  • Axtell RL, Florida R (2002) Zipfs law of city sizes: a microeconomic explanation. Working paper. Center on Social and Economic Dynamics. The Brookings Institution: Wash, DC; available at www.brookings.edu/dynamics/papers.

    Google Scholar 

  • Axtell RL et al. (2002) Population growth and collapse in a multi-agent model of the Kayenta Anasazi in Long House Valley. Proc Nat Acad Sci vol 99(suppl 3): 7275–7279.

    MathSciNet  CrossRef  Google Scholar 

  • Barbosa V (1996) An Introduction to Distributed Computation. MIT Press: Cambridge, Mass.

    Google Scholar 

  • Bergin J, Lippman B (1996) Evolution with state-dependent mutations. Econometrica vol 64: 943–956.

    MATH  CrossRef  Google Scholar 

  • Bertsekas D, J Tsitsiklis (1993) Parallel and Distributed Computation. Wiley: NY.

    Google Scholar 

  • Binmore KG (1987) Modeling rational players, part I. Econ Phil vol 3: 179–214.

    CrossRef  Google Scholar 

  • Binmore KG — (1988) Modeling rational players, part II.” Econ Phil vol 4: 9–55.

    Google Scholar 

  • Bonabeau E (2002) Agent-based modeling: methods and techniques for simulating human systems. Proc Nat Acad Sci vol 99(suppl. 3): 7280–7287.

    CrossRef  Google Scholar 

  • Cartwright N (1983) How the Laws of Physics Lie. Oxford Univ Press: NY, N.Y.

    CrossRef  Google Scholar 

  • Cederman L-E (1998) Emergent Actors and World Politics. Princeton Univ Press: Princeton NJ.

    Google Scholar 

  • Davies M, Stone T (eds) (1995) Mental Simulation. Blackwell Publishers: Cambridge, Mass.

    Google Scholar 

  • Danielson P (1992) Artificial Morality: Virtuous Robots for Virtual Games. Routledge: NY.

    Google Scholar 

  • Durlauf S (1999) How can statistical mechanics contribute to social science?. Proc Nat Acad Sci vol 96: 10582–10584.

    MathSciNet  MATH  CrossRef  Google Scholar 

  • Eigen M, Winkler R (1993) Laws of the Game. Princeton Univ Press: Princeton NJ.

    Google Scholar 

  • Epstein JM, Axtell R. (1996) Growing Artificial Societies: Social Science from the Bottom Up. MIT Press: Cambridge Mass.

    Google Scholar 

  • Gaylord RJ, D’andria LJ (1997) Simulating Society: A Mathematica Toolkit for Modeling Socioeconomic behavior. Springer-Verlag: New York.

    Google Scholar 

  • Gigerenzer G. et al. (1999) Simple Heuristics That Make Us Smart. Oxford Univ Press: NY.

    Google Scholar 

  • Gilboa I, Matsui A (1991) Social stability and equilibrium. Econometrica vol 59 no 3: 859–867.

    MathSciNet  MATH  CrossRef  Google Scholar 

  • Goeree JK and CA Holt (1999) Stochastic game theory: For playing games, not just for doing theory. Proc Nat Acad Sci 96: 10564–10567.

    MathSciNet  MATH  CrossRef  Google Scholar 

  • Hayek FA (1937) Economics and knowledge. Economica vol 4 (new ser.): 33–54.

    CrossRef  Google Scholar 

  • Hayek FA — (1945) The use of knowledge in society. Am Econ Rev vol 35 no 4: 519–530.

    Google Scholar 

  • Holland JH (1998) Emergence: From Chaos to Order. Perseus: Reading, Mass.

    MATH  Google Scholar 

  • Huberman BA (1998) Computation as economics. J Econ Dyn vol 22: 1169–1186

    MathSciNet  MATH  CrossRef  Google Scholar 

  • Huberman BA, Glance, NS (1993) Evolutionary games and computer simulations. Proc Nat Acad Sci vol 90: 7716–7718.

    MATH  CrossRef  Google Scholar 

  • Huberman BA, Hogg T (1994) Distributed computation as an economic system. J Econ Persp vol 9 no 1: 141–152.

    CrossRef  Google Scholar 

  • Inchiosa ME, Parker MT (2002) Overcoming design and development challenges in agent-based modeling using Ascape. Proc Nat Acad Sci vol 99(suppl. 3): 7304–7308.

    CrossRef  Google Scholar 

  • Intel Corporation (2002) See www.intel.com/research/silicon/mooreslaw.htm.

  • Jones T, Hraber P, Forrest S (1997) The ecology of Echo. Artificial Life vol 3 no 3: 165–190.

    CrossRef  Google Scholar 

  • Joy B (2000) Why the future doesn’t need us. Wired vol 8 no 4.

    Google Scholar 

  • Judd K (1999) Numerical Analysis in Economcis. MIT Press: Cambridge, Mass.

    Google Scholar 

  • Kirman AP (1997) The Economy as an Interactive System. In: Arthur WB et al. (eds) The Economy as an Evolving Complex System vol. II. Addison-Wesley: Reading, Mass.

    Google Scholar 

  • Krugman P (1996) The Self-Organizing Economy. Blackwell: NY.

    Google Scholar 

  • Kurzweil R (2000) The Age of Spiritual Machines. Penguin: New York, N.Y.

    Google Scholar 

  • Laughlin RB, Pines D (2000) The Theory of Everything. Proc Nat Acad Sci vol 97 no 1:28–31.

    MathSciNet  CrossRef  Google Scholar 

  • Laughlin RB, Pines D, Schmalian J, Stojkovic BP, Wolynes P (2000) The middle way. Proc Nat Acad Sci vol 97 no 1: 32–37.

    CrossRef  Google Scholar 

  • Leijonhuvvud A (2002) Adaptive Economic Dynamics. Working paper. Trento Summer School on Adaptive Economic Processes. University of Trento, Italy.

    Google Scholar 

  • Luenberger D (1979) An Introduction to Dynamical Systems: Theory, Models and Applications. Wiley: NY.

    Google Scholar 

  • Luna F, Steffanson B (2000) Economic Models in SWARM. Kluwer: NY.

    Google Scholar 

  • Lux T (1998) The socioeconomic dynamics of speculative markets: interacting agents, chaos and the fat tails of return distributions. J Econ Behav Org vol 33: 143–165.

    CrossRef  Google Scholar 

  • Mirowski P.(2001) Machine Dreams: How Economics Became a Cyborg Science Camrbridge Univ Press: NY.

    Google Scholar 

  • Moravec H (1990) Mind Children: The Future of Robot and Human Intelligence. Harvard Univ Press: Cambridge, Mass.

    Google Scholar 

  • Moss S, Gaylard H, Wallis S, Edmonds B (1998) SDML: A multi-agent language for organizational modeling. Computational and Mathematical Organizational Theory vol 4 no 1:43–70.

    CrossRef  Google Scholar 

  • Nagel, K, Paczuski M (1995) Emergent traffic jams. Physical Rev. E vol 51: 2909.

    CrossRef  Google Scholar 

  • Newell A, Simon HA (1972) Human Problem Solving. Prentice-Hall: Englewood Cliffs, NJ.

    Google Scholar 

  • Nordhaus, W (1992) The ecology of markets. Proc Nat Acad Sci vol 89: 843–850.

    CrossRef  Google Scholar 

  • Nordhaus, W — (2000) The progress of computing. Cowles Foundation discussion paper no. 1324. Cowles Foundation for Research in Economics. Yale University: New Haven, Conn.

    Google Scholar 

  • Nowak MA, May RM (1992) Evolutionary games and spatial chaos. Nature vol 359: 827–829.

    CrossRef  Google Scholar 

  • Orcutt G, Greenberger M, Korbel J, Rivlin AM (1961) Microanalysis of Socioeconomic Systems: A Simulation Study. Harper&Row: NY.

    Google Scholar 

  • Padgett J (1997) Simple ecologies of skill. In: Arthur et al. (eds) The Economy as an Evolving Complex System vol. II. Addison-Wesley: Reading, Mass.

    Google Scholar 

  • Papadimitriou C (1994) On the complexity of the parity argument and other inefficient proofs of existence. J Sys Comp Sci vol 49: 498–532.

    MathSciNet  CrossRef  Google Scholar 

  • Polanyi M (1958) Personal Knowledge: Towards a Post-Critical Philosophy. Univ of Chicago Press: Chicago, 111.

    Google Scholar 

  • Russell SJ, Norvig P (1994) Artificial Intelligence: A Modern Approach. Prentice Hall: NY.

    Google Scholar 

  • Sala-i-Martin X (1997) I just ran two million regressions. Am Econ Rev vol 87 no 2: 178–183.

    Google Scholar 

  • Shubik M (1997) Why equilibrium? A note on the noncooperative equilibria of some matrix games.” J Econ Beh Org vol 29: 537–539.

    CrossRef  Google Scholar 

  • Simon, HA (1978) On how to decide what to do. Bell J Econ vol 9 no 2.

    Google Scholar 

  • Tesfatsion L (2002) Agent-based computational economics: growing economies from the bottom up. Artificial Life vol 8 no 1: 55–82.

    MathSciNet  CrossRef  Google Scholar 

  • Velupillai K (2000) Computable Economics. Oxford Univ Press: New York, NY.

    MATH  CrossRef  Google Scholar 

  • Watts, D (1999) Small Worlds. Princeton Univ Press: Princeton, N.J.

    Google Scholar 

  • Weiss G (ed) (1999) Multi-Agent Systems. MIT Press: Cambridge, Mass.

    Google Scholar 

  • Winston PH (1992) Artificial Intelligence (3rd edn). Addison-Wesley: Reading, Mass.

    Google Scholar 

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L. Axtell, R. (2003). Economics as Distributed Computation. In: Terano, T., Deguchi, H., Takadama, K. (eds) Meeting the Challenge of Social Problems via Agent-Based Simulation. Springer, Tokyo. https://doi.org/10.1007/978-4-431-67863-2_1

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  • DOI: https://doi.org/10.1007/978-4-431-67863-2_1

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