Agent-based computational economics (ACE) is the area of computational economics that studies economic processes, including whole economies, as dynamic systems of interacting agents. As such, it falls in the paradigm of complex adaptive systems.[1] In corresponding agent-based models, the "agents" are "computational objects modeled as interacting according to rules" over space and time, not real people. The rules are formulated to model behavior and social interactions based on incentives and information.[2] Such rules could also be the result of optimization, realized through use of AI methods (such as Q-learning and other reinforcement learning techniques).[3]
The theoretical assumption of mathematical optimization by agents in equilibrium is replaced by the less restrictive postulate of agents with bounded rationalityadapting to market forces.[4] ACE models apply numerical methods of analysis to computer-based simulations of complex dynamic problems for which more conventional methods, such as theorem formulation, may not find ready use.[5] Starting from initial conditions specified by the modeler, the computational economy evolves over time as its constituent agents repeatedly interact with each other, including learning from interactions. In these respects, ACE has been characterized as a bottom-up culture-dish approach to the study of economic systems.[6]
ACE has a similarity to, and overlap with, game theory as an agent-based method for modeling social interactions.[7] But practitioners have also noted differences from standard methods, for example in ACE events modeled being driven solely by initial conditions, whether or not equilibria exist or are computationally tractable, and in the modeling facilitation of agent autonomy and learning.[8]
^Richard S. Sutton and Andrew G. Barto, Reinforcement Learning: An Introduction, The MIT Press, Cambridge, MA, 1998 [1]Archived 4 September 2009 at the Wayback Machine
^• Kenneth L. Judd, 2006. "Computationally Intensive Analyses in Economics," Handbook of Computational Economics, v. 2, ch. 17, Introduction, p. 883. [Pp. 881- 893. Pre-pub PDF.
• _____, 1998. Numerical Methods in Economics, MIT Press. Links to descriptionArchived 11 February 2012 at the Wayback Machine and chapter previews.
^• Leigh Tesfatsion (2002). "Agent-Based Computational Economics: Growing Economies from the Bottom Up," Artificial Life, 8(1), pp.55-82. Abstract and pre-pub PDFArchived 14 May 2013 at the Wayback Machine. • _____ (1997). "How Economists Can Get Alife," in W. B. Arthur, S. Durlauf, and D. Lane, eds., The Economy as an Evolving Complex System, II, pp. 533-564. Addison-Wesley. Pre-pub PDFArchived 15 April 2012 at the Wayback Machine.
^• Joseph Y. Halpern (2008). "computer science and game theory," The New Palgrave Dictionary of Economics, 2nd Edition. Abstract. • Yoav Shoham (2008). "Computer Science and Game Theory," Communications of the ACM, 51(8), pp.
75-79Archived 26 April 2012 at the Wayback Machine. • Alvin E. Roth (2002). "The Economist as Engineer: Game Theory, Experimentation, and Computation as Tools for Design Economics," Econometrica, 70(4), pp. 1341–1378.
^Tesfatsion, Leigh (2006), "Agent-Based Computational Economics: A Constructive Approach to Economic Theory," ch. 16, Handbook of Computational Economics, v. 2, part 2, ACE study of economic system. Abstract and pre-pub PDF.
^• Leigh Tesfatsion (2006). "Agent-Based Computational Economics: A Constructive Approach to Economic Theory," ch. 16, Handbook of Computational Economics, v. 2, [pp. 831-880] sect. 5. Abstract and pre-pub PDF. • Kenneth L. Judd (2006). "Computationally Intensive Analyses in Economics," Handbook of Computational Economics, v. 2, ch. 17, pp. 881- 893. Pre-pub PDF. • Leigh Tesfatsion and Kenneth L. Judd, ed. (2006). Handbook of Computational Economics, v. 2. DescriptionArchived 6 March 2012 at the Wayback Machine & and chapter-preview
links.
^B. Arthur, J. Holland, B. LeBaron, R. Palmer, P. Taylor (1997), 'Asset pricing under endogenous expectations in an artificial stock market,' in The Economy as an Evolving Complex System II, B. Arthur, S. Durlauf, and D. Lane, eds., Addison Wesley.
^Tomas B. Klosa and Bart Nooteboom, 2001. "Agent-based Computational Transaction Cost Economics," Journal of Economic Dynamics and Control 25(3–4), pp. 503–52. Abstract.
^• Roberto Leombruni and Matteo Richiardi, ed. (2004), Industry and Labor Dynamics: The Agent-Based Computational Economics Approach. World Scientific Publishing ISBN981-256-100-5. DescriptionArchived 27 July 2010 at the Wayback Machine and chapter-preview links. • Joshua M. Epstein (2006). "Growing Adaptive Organizations: An Agent-Based Computational Approach," in Generative Social Science: Studies in Agent-Based Computational Modeling, pp. 309- 344. DescriptionArchived 26 January 2012 at the Wayback Machine and abstract.
^Robert Axtell (2005). "The Complexity of Exchange," Economic Journal, 115(504, Features), pp. F193-F210.
^• The New Palgrave Dictionary of Economics (2008), 2nd Edition: Roger B. Myerson "mechanism design." Abstract. _____. "revelation principle." Abstract. Tuomas Sandholm. "computing in mechanism design." Abstract. • Noam Nisan and Amir Ronen (2001). "Algorithmic Mechanism Design," Games and Economic Behavior, 35(1-2), pp. 166–196. • Noam Nisanet al., ed. (2007). Algorithmic Game Theory, Cambridge University Press. DescriptionArchived 5 May 2012 at the Wayback Machine.
^Tuomas W. Sandholm and Victor R. Lesser (2001). "Leveled Commitment Contracts and Strategic Breach," Games and Economic Behavior, 35(1-2), pp. 212-270.
^A. F. Cottrell, P. Cockshott, G. J. Michaelson, I. P. Wright, V. Yakovenko
(2009), Classical Econophysics. Routledge, ISBN978-0-415-47848-9.
^Leigh Tesfatsion (2006), "Agent-Based Computational Economics: A Constructive Approach to Economic Theory," ch. 16, Handbook of Computational Economics, v. 2, part 2, ACE study of economic system. Abstract and pre-pub PDF.