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Out-of-Equilibrium Economics and Agent-Based Modeling

In: Handbook of Computational Economics

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  • Arthur, W. Brian
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    Abstract

    Standard neoclassical economics asks what agents' actions, strategies, or expectations are in equilibrium with (consistent with) the outcome or pattern these behaviors aggregatively create. Agent-based computational economics enables us to ask a wider question: how agents' actions, strategies, or expectations might react to--might endogenously change with--the patterns they create. In other words, it enables us to examine how the economy behaves out of equilibrium, when it is not at a steady state.This out-of-equilibrium approach is not a minor adjunct to standard economic theory; it is economics done in a more general way. When examined out of equilibrium, economic patterns sometimes simplify into a simple, homogeneous equilibrium of standard economics; but just as often they show perpetually novel and complex behavior. The static equilibrium approach suffers two characteristic indeterminacies: it cannot easily resolve among multiple equilibria; nor can it easily model individuals' choices of expectations. Both problems are ones of formation (of an equilibrium and of an "ecology" of expectations, respectively), and when analyzed in formation--that is, out of equilibrium--these anomalies disappear.

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    This chapter was published in:

  • Leigh Tesfatsion & Kenneth L. Judd (ed.), 2006. "Handbook of Computational Economics," Handbook of Computational Economics, Elsevier, edition 1, volume 2, number 2, 00.
    This item is provided by Elsevier in its series Handbook of Computational Economics with number 2-32.

    Handle: RePEc:eee:hecchp:2-32

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    Cited by:
    1. Chad Seagren, 2011. "Examining social processes with agent-based models," The Review of Austrian Economics, Springer, vol. 24(1), pages 1-17, March.
    2. Lengnick, Matthias, 2013. "Agent-based macroeconomics: A baseline model," Journal of Economic Behavior & Organization, Elsevier, vol. 86(C), pages 102-120.
    3. Magliocca, Nicholas & McConnell, Virginia & Walls, Margaret & Safirova, Elena, 2012. "Zoning on the urban fringe: Results from a new approach to modeling land and housing markets," Regional Science and Urban Economics, Elsevier, vol. 42(1-2), pages 198-210.
    4. Steinbacher, Matjaz, 2009. "Acceptable Risk in a Portfolio Analysis," MPRA Paper 13569, University Library of Munich, Germany.
    5. Hommes, C.H., 2007. "Bounded Rationality and Learning in Complex Markets," CeNDEF Working Papers 07-01, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance.
    6. Tas, Derya & Calvo-Pardo, Hector & Arrondel, Luc, 2012. "Subjective Return Expectations, Information and Stock Market Participation : Evidence from France," Economics Papers from University Paris Dauphine 123456789/9805, Paris Dauphine University.
    7. Gaujal, Bruno & Gulyas, Laszlo & Mansury, Yuri & Thierry, Eric, 2014. "Validating an agent-based model of the Zipf׳s Law: A discrete Markov-chain approach," Journal of Economic Dynamics and Control, Elsevier, vol. 41(C), pages 38-49.
    8. Nemet, Gregory F., 2009. "Interim monitoring of cost dynamics for publicly supported energy technologies," Energy Policy, Elsevier, vol. 37(3), pages 825-835, March.
    9. Robert Marks, 2007. "Validating Simulation Models: A General Framework and Four Applied Examples," Computational Economics, Society for Computational Economics, vol. 30(3), pages 265-290, October.
    10. Ostasiewicz, K. & Tyc, M.H. & Radosz, A. & Magnuszewski, P. & Goliczewski, P. & Hetman, P. & Sendzimir, J., 2008. "Multistability of impact, utility and threshold concepts of binary choice models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(25), pages 6337-6352.

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