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 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 predict behavior and social interactions based on incentives and information.[2] The theoretical assumption of mathematical optimization by agents is replaced by the less restrictive postulate of agents adapting to market forces.[3] Starting from initial conditions specified by the modeler, the computational economy evolves over time as its constituent agents repeatedly interact with each other and learn from their interactions. In these respects, ACE has been characterized as a bottom-up culture-dish approach to the study of economic systems.[4]

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."[5] The subject has been applied to research areas like asset pricing,[6] industry dynamics,[7] macroeconomics,[8] and Marxist economics.[9]

Contents

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.[10] Issues include those common to experimental economics in general[11] and development of a common framework for empirical validation and resolving open questions in agent-based modeling.[12]

ACE is an officially designated special interest group (SIG) of the Society for Computational Economics.[13] 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.[6][14][15] 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,[16] and some papers have suggested that ACE might be a useful methodology for understanding the recent financial crisis.[17][18]

See also

References

  1. ^ Leigh Tesfatsion, 2003. "Agent-based Computational Economics: Modeling Economies as Complex Adaptive Systems," Information Sciences, 149(4), pp. 262-268.
  2. ^ Scott E. Page (2008). "agent-based models," The New Palgrave Dictionary of Economics, 2nd Edition. Abstract.
  3. ^ • John H. Holland and John H. Miller (1991). "Artificial Adaptive Agents in Economic Theory," American Economic Review, 81(2), [pp. 365-370] p. 366.
       • Thomas J. Sargent, 1994. Bounded Rationality in Macroeconomics, Oxford. Description and chapter-preview 1st-page links.
  4. ^ • 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.
       • _____ (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.
  5. ^ • 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 & and chapter-preview links.
  6. ^ a b 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.
  7. ^ R. Leombruni and M. Richiardi (2004), Industry and Labor Dynamics: The Agent-Based Computational Economics Approach. World Scientific Publishing, ISBN 9812561005.
  8. ^ M. Oeffner (2009), 'Agent-based Keynesian Macroeconomics'. PhD thesis, Faculty of Economics, University of Würzburg.
  9. ^ A. F. Cottrell, P. Cockshott, G. J. Michaelson, I. P. Wright, V. Yakovenko (2009), Classical Econophysics. Routledge, ISBN 978-0-415-47848-9.
  10. ^ Summary of methods: Department of Economics, Politics and Public Administration, Aalborg University, Denmark website.
  11. ^ Vernon L. Smith, 2008. "experimental economics," The New Palgrave Dictionary of Economics, 2nd Edition. Abstract.
  12. ^ 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.
  13. ^ Society for Computational Economics website.
  14. ^ W. Brock and C. Hommes (1997), 'A rational route to randomness.' Econometrica 65 (5), pp. 1059-1095.
  15. ^ C. Hommes (2008), 'Interacting agents in finance,' in The New Palgrave Dictionary of Economics.
  16. ^ W. Brock, C. Hommes, and F. Wagener (2009), 'More hedging instruments may destabilize markets.' CeNDEF Working Paper.
  17. ^ M. Buchanan (2009), 'Meltdown modelling. Could agent-based computer models prevent another financial crisis?.' Nature, Vol. 460, No. 7256. (05 August 2009), pp. 680-682.
  18. ^ J.D. Farmer, D. Foley (2009), 'The economy needs agent-based modelling.' Nature, Vol. 460, No. 7256. (05 August 2009), pp. 685-686.

Further reading

External links