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Empirical models, rules, and optimization

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  • Cattaneo, Andrea
  • Robinson, Sherman

Abstract

This paper considers supply decisions by firms in a dynamic setting with adjustment costs and compares the behavior of an optimal control model to that of a rule-based system which relaxes the assumption that agents are explicit optimizers. In our approach, the economic agent uses believably simple rules in coping with complex situations. We estimate rules using an artificially generated sample obtained by running repeated simulations of a dynamic optimal control model of a firm's hiring/firing decisions. We show that (i) agents using heuristics can behave as if they were seeking rationally to maximize their dynamic returns; (ii) the approach requires fewer behavioral assumptions relative to dynamic optimization and the assumptions made are based on economically intuitive theoretical results linking rule adoption to uncertainty; (iii) the approach delineates the domain of applicability of maximization hypotheses and describes the behavior of agents in situations of economic disequilibrium. The approach adopted uses concepts from fuzzy control theory. An agent, instead of optimizing, follows Fuzzy Associative Memory (FAM) rules which, given input and output data, can be estimated and used to approximate any non-linear dynamic process. Empirical results indicate that the fuzzy rule-based system performs extremely well in approximating optimal dynamic behavior in situations with limited noise.

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Bibliographic Info

Paper provided by International Food Policy Research Institute (IFPRI) in its series TMD discussion papers with number 53.

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Date of creation: 2000
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Handle: RePEc:fpr:tmddps:53

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Keywords: Decision-making. ; econometric models ; TMD ;

References

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  1. Winter, Sidney G, 1971. "Satisficing, Selection, and the Innovating Remnant," The Quarterly Journal of Economics, MIT Press, vol. 85(2), pages 237-61, May.
  2. Marimon, Ramon & McGrattan, Ellen & Sargent, Thomas J., 1990. "Money as a medium of exchange in an economy with artificially intelligent agents," Journal of Economic Dynamics and Control, Elsevier, vol. 14(2), pages 329-373, May.
  3. Nickell, S J, 1978. "Fixed Costs, Employment and Labour Demand over the Cycle," Economica, London School of Economics and Political Science, vol. 45(180), pages 329-45, November.
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  5. Heiner, Ronald A., 1989. "The origin of predictable dynamic behavior," Journal of Economic Behavior & Organization, Elsevier, vol. 12(2), pages 233-257, October.
  6. Marimon, Ramon, 1993. "Adaptive learning, evolutionary dynamics and equilibrium selection in games," European Economic Review, Elsevier, vol. 37(2-3), pages 603-611, April.
  7. Holland, John H & Miller, John H, 1991. "Artificial Adaptive Agents in Economic Theory," American Economic Review, American Economic Association, vol. 81(2), pages 365-71, May.
  8. Richard H. Day & E. Herbert Tinney, 1968. "How to Co-operate in Business without Really Trying: A Learning Model of Decentralized Decision Making," Journal of Political Economy, University of Chicago Press, vol. 76, pages 583.
  9. Akerlof, George A & Yellen, Janet L, 1987. "Rational Models of Irrational Behavior," American Economic Review, American Economic Association, vol. 77(2), pages 137-42, May.
  10. Arthur, W Brian, 1994. "Inductive Reasoning and Bounded Rationality," American Economic Review, American Economic Association, vol. 84(2), pages 406-11, May.
  11. Heiner, Ronald A, 1983. "The Origin of Predictable Behavior," American Economic Review, American Economic Association, vol. 73(4), pages 560-95, September.
  12. Dixit, Avinash, 1997. "Investment and Employment Dynamics in the Short Run and the Long Run," Oxford Economic Papers, Oxford University Press, vol. 49(1), pages 1-20, January.
  13. John Conlisk, 1996. "Why Bounded Rationality?," Journal of Economic Literature, American Economic Association, vol. 34(2), pages 669-700, June.
  14. Thaler, Richard H, 1988. "Anomalies: The Winner's Curse," Journal of Economic Perspectives, American Economic Association, vol. 2(1), pages 191-202, Winter.
  15. Conlisk, John, 1983. "Competitive Approximation of a Cournot Market," Review of Economic Studies, Wiley Blackwell, vol. 50(4), pages 597-607, October.
  16. Winter, Sidney G., 1982. "Binary choice and the supply of memory," Journal of Economic Behavior & Organization, Elsevier, vol. 3(4), pages 277-321, December.
  17. Bentolila, Samuel & Bertola, Giuseppe, 1990. "Firing Costs and Labour Demand: How Bad Is Eurosclerosis?," Review of Economic Studies, Wiley Blackwell, vol. 57(3), pages 381-402, July.
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Cited by:
  1. Diaz-Bonilla, Eugenio & Thomas, Marcelle & Robinson, Sherman & Cattaneo, Andrea, 2000. "Food security and trade negotiations in the World Trade Organization," TMD discussion papers 59, International Food Policy Research Institute (IFPRI).
  2. Morley, Samuel A., 2001. "What has happened to growth in Latin America," TMD discussion papers 67, International Food Policy Research Institute (IFPRI).
  3. Robinson, Sherman & El-Said, Moataz, 2000. "GAMS code for estimating a social accounting matrix (SAM) using cross entropy methods (CE)," TMD discussion papers 64, International Food Policy Research Institute (IFPRI).

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