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Learning Rational Expectations under Computability Constraints

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  • Spear, Stephen E

Abstract

In this paper, the author considers how boundedly rational agents learn rational expectations when all equilibrium price functions or forecasts of future equilibrium prices are required to be computable. The paper examines two learning environments. In the first, agents have perfect information about the state of nature. In this case, the theory of machine inference can be applied to show that there is a broad class of computable economies whose rational expectations equilibria can be learned by inductive inference. In the second environment, agents do not have perfect information about the state of nature. In this case, a version of Godel's incompleteness theorem implies that rational expectations equilibria cannot be learned. Copyright 1989 by The Econometric Society.

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Bibliographic Info

Article provided by Econometric Society in its journal Econometrica.

Volume (Year): 57 (1989)
Issue (Month): 4 (July)
Pages: 889-910

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Handle: RePEc:ecm:emetrp:v:57:y:1989:i:4:p:889-910

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Cited by:
  1. Anderlini, L., 1990. "Communication, Computability And Common Interest Games," Papers 159, Cambridge - Risk, Information & Quantity Signals.
  2. Kelly, David L. & Shorish, Jamsheed, 2000. "Stability of Functional Rational Expectations Equilibria," Journal of Economic Theory, Elsevier, vol. 95(2), pages 215-250, December.
  3. (Vela) Velupillai, K., 1997. "Expository notes on computability and complexity in (arithmetical) games," Journal of Economic Dynamics and Control, Elsevier, vol. 21(6), pages 955-979, June.
  4. K. Vela Velupillai, 2010. "Reflections on Mathematical Economics in the Algorithmic Mode," ASSRU Discussion Papers 1016, ASSRU - Algorithmic Social Science Research Unit.
  5. Chen, Shu-Heng & Yeh, Chia-Hsuan, 2002. "On the emergent properties of artificial stock markets: the efficient market hypothesis and the rational expectations hypothesis," Journal of Economic Behavior & Organization, Elsevier, vol. 49(2), pages 217-239, October.
  6. Velupillai Kumaraswamy Vela, 2008. "The Mathematization of Macroeconomics. A Recursive Revolution," Economia politica, Società editrice il Mulino, issue 2, pages 283-316.
  7. Richter, Marcel K. & Wong, Kam-Chau, 1999. "Computable preference and utility," Journal of Mathematical Economics, Elsevier, vol. 32(3), pages 339-354, November.
  8. Koye Somefun, 2001. "Posted Offer versus Bargaining: An Example of how Institutions can Facilitate Learning," Computing in Economics and Finance 2001 79, Society for Computational Economics.
  9. repec:dgr:uvatin:2004077 is not listed on IDEAS
  10. DeCanio, Stephen J. & Watkins, William E., 1998. "Information processing and organizational structure," Journal of Economic Behavior & Organization, Elsevier, vol. 36(3), pages 275-294, August.
  11. Francesco Luna, . "Computable Learning, Neural Networks and Institutions," Computing in Economics and Finance 1996 _037, Society for Computational Economics.
  12. Anderlini, Luca, 1998. "Forecasting errors and bounded rationality: An example," Mathematical Social Sciences, Elsevier, vol. 36(2), pages 71-90, September.
  13. Brown, Paul M., 1995. "Learning from experience, reference points, and decision costs," Journal of Economic Behavior & Organization, Elsevier, vol. 27(3), pages 381-399, August.
  14. Mihara, H.R., 1994. "Arrow's Theorem and Turing Computability," Papers 276, Minnesota - Center for Economic Research.
  15. Stephen Kinsella & David M. Ramsey, 2011. "A Model of Partnership Formation with Friction and Multiple Criteria," Working Papers 201119, Geary Institute, University College Dublin.
  16. H. Reiju Mihara, 1997. "Arrow's Theorem, countably many agents, and more visible invisible dictators," Public Economics 9705001, EconWPA, revised 07 May 1997.

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