Agent-based computational economics

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 rationality adapting 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]

The method has benefited from continuing improvements in modeling techniques of computer science and increased computer capabilities. The ultimate scientific objective of the method is to "test theoretical findings against real-world data in ways that permit empirically supported theories to cumulate over time, with each researcher’s work building appropriately on the work that has gone before."[9] The subject has been applied to research areas like asset pricing,[10] competition and collaboration,[11] transaction costs,[12] market structure and industrial organization and dynamics,[13] welfare economics,[14] and mechanism design,[15] information and uncertainty,[16] macroeconomics,[17] and Marxist economics.[18][19]

Overview

The "agents" in ACE models can represent individuals (e.g. people), social groupings (e.g. firms), biological entities (e.g. growing crops), and/or physical systems (e.g. transport systems). The ACE modeler provides the initial configuration of a computational economic system comprising multiple interacting agents. The modeler then steps back to observe the development of the system over time without further intervention. In particular, system events should be driven by agent interactions without external imposition of equilibrium conditions.[20] Issues include those common to experimental economics in general[21] and development of a common framework for empirical validation and resolving open questions in agent-based modeling.[22]

ACE is an officially designated special interest group (SIG) of the Society for Computational Economics.[23] Researchers at the Santa Fe Institute have contributed to the development of ACE.

Example: finance

One area where ACE methodology has frequently been applied is asset pricing. W. Brian Arthur, Eric Baum, William Brock, Cars Hommes, and Blake LeBaron, among others, have developed computational models in which many agents choose from a set of possible forecasting strategies in order to predict stock prices, which affects their asset demands and thus affects stock prices. These models assume that agents are more likely to choose forecasting strategies which have recently been successful. The success of any strategy will depend on market conditions and also on the set of strategies that are currently being used. These models frequently find that large booms and busts in asset prices may occur as agents switch across forecasting strategies.[10][24][25] More recently, Brock, Hommes, and Wagener (2009) have used a model of this type to argue that the introduction of new hedging instruments may destabilize the market,[26] and some papers have suggested that ACE might be a useful methodology for understanding the recent financial crisis.[27][28][29]

gollark: Yes.
gollark: 30kg has not been tested.
gollark: According to empirical testing, I can actually do things with 5kg objects without my bones exploding utterly.
gollark: It's 30kgish.
gollark: I may need to work out how to get wolframalpha to differentiate it.

See also

References
  1. • W. Brian Arthur, 1994. "Inductive Reasoning and Bounded Rationality," American Economic Review, 84(2), pp. 406-411 Archived 21 May 2013 at the Wayback Machine.
       • Leigh Tesfatsion, 2003. "Agent-based Computational Economics: Modeling Economies as Complex Adaptive Systems," Information Sciences, 149(4), pp. 262-268 Archived 26 April 2012 at the Wayback Machine.
  2. Scott E. Page (2008). "agent-based models," The New Palgrave Dictionary of Economics, 2nd Edition. Abstract.
  3. Richard S. Sutton and Andrew G. Barto, Reinforcement Learning: An Introduction, The MIT Press, Cambridge, MA, 1998 Archived 4 September 2009 at the Wayback Machine
  4. • John H. Holland and John H. Miller (1991). "Artificial Adaptive Agents in Economic Theory," American Economic Review, 81(2), pp. 365-370 Archived 5 January 2011 at the Wayback Machine p. 366.
       • Thomas C. Schelling (1978 [2006]). Micromotives and Macrobehavior, Norton. Description Archived 2 November 2017 at the Wayback Machine, preview.
       • Thomas J. Sargent, 1994. Bounded Rationality in Macroeconomics, Oxford. Description and chapter-preview 1st-page links.
  5. • 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 description Archived 11 February 2012 at the Wayback Machine and chapter previews.
  6. • Leigh Tesfatsion (2002). "Agent-Based Computational Economics: Growing Economies from the Bottom Up," Artificial Life, 8(1), pp.55-82. Abstract and pre-pub PDF Archived 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 PDF.
  7. • 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. 75-79 Archived 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.
  8. 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.
  9. • 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. Description Archived 6 March 2012 at the Wayback Machine & and chapter-preview links.
  10. 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.
  11. Robert Axelrod (1997). The Complexity of Cooperation: Agent-Based Models of Competition and Collaboration, Princeton. Description, contents, and preview.
  12. 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.
  13. • Roberto Leombruni and Matteo Richiardi, ed. (2004), Industry and Labor Dynamics: The Agent-Based Computational Economics Approach. World Scientific Publishing ISBN 981-256-100-5. Description Archived 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. Description Archived 26 January 2012 at the Wayback Machine and abstract.
  14. Robert Axtell (2005). "The Complexity of Exchange," Economic Journal, 115(504, Features), pp. F193-F210.
  15. • 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 Nisan et al., ed. (2007). Algorithmic Game Theory, Cambridge University Press. Description Archived 5 May 2012 at the Wayback Machine.
  16. Tuomas W. Sandholm and Victor R. Lesser (2001). "Leveled Commitment Contracts and Strategic Breach," Games and Economic Behavior, 35(1-2), pp. 212-270.
  17. • David Colander, Peter Howitt, Alan Kirman, Axel Leijonhufvud, and Perry Mehrling, 2008. "Beyond DSGE Models: Toward an Empirically Based Macroeconomics," American Economic Review, 98(2), pp. 236-240. Pre-pub PDF.
       • Thomas J. Sargent (1994). Bounded Rationality in Macroeconomics, Oxford. Description and chapter-preview 1st-page links.
       • M. Oeffner (2009). 'Agent-based Keynesian Macroeconomics'. PhD thesis, Faculty of Economics, University of Würzburg.
  18. A. F. Cottrell, P. Cockshott, G. J. Michaelson, I. P. Wright, V. Yakovenko (2009), Classical Econophysics. Routledge, ISBN 978-0-415-47848-9.
  19. 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.
  20. Summary of methods Archived 26 May 2007 at the Wayback Machine: Department of Economics, Politics and Public Administration, Aalborg University, Denmark website.
  21. Vernon L. Smith, 2008. "experimental economics," The New Palgrave Dictionary of Economics, 2nd Edition. Abstract.
  22. Giorgio Fagiolo, Alessio Moneta, and Paul Windrum, 2007. "A Critical Guide to Empirical Validation of Agent-Based Models in Economics: Methodologies, Procedures, and Open Problems," Computational Economics, 30, pp. 195–226.
  23. Society for Computational Economics website.
  24. W. Brock and C. Hommes (1997), 'A rational route to randomness.' Econometrica 65 (5), pp. 1059-1095.
  25. C. Hommes (2008), 'Interacting agents in finance,' in The New Palgrave Dictionary of Economics.
  26. Brock, W.; Hommes, C.; Wagener, F. (2009). "More hedging instruments may destabilize markets" (PDF). Journal of Economic Dynamics and Control. 33 (11): 1912–1928. doi:10.1016/j.jedc.2009.05.004.
  27. M. Buchanan (2009), 'Meltdown modelling. Could agent-based computer models prevent another financial crisis?.' Nature, Vol. 460, No. 7256. (5 August 2009), pp. 680-682.
  28. J.D. Farmer, D. Foley (2009), 'The economy needs agent-based modelling.' Nature, Vol. 460, No. 7256. (5 August 2009), pp. 685-686.
  29. M. Holcombe, S. Coakley, M.Kiran, S. Chin, C. Greenough, D.Worth, S.Cincotti, M.Raberto, A. Teglio, C. Deissenberg, S. van der Hoog, H. Dawid, S. Gemkow, P. Harting, M. Neugart. Large-scale Modeling of Economic Systems, Complex Systems, 22(2), 175-191, 2013
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