Abstract
This chapter introduces the notions of rational expectations and optimal learning extensively used in economic theory.* It has become well known from recent literature that in active learning situations (where the actions of the statistician, or the person learning about some parameters, influences the draws from the distribution about which he/she is learning), full learning may not take place. This challenges the use of the rational expectations hypothesis which is justified on the basis that agents operating in an economy eventually all learn the true structure of the economy and optimize accordingly. In El-Gamal and Sundaram (1989, 1990) we presented a framework where a Bayesian economist imposes priors on agent-priors and we then study the evolution of those economist beliefs. We showed that generically, the economist limit beliefs generically do not have point mass at any particular agent-belief, let alone the true rational expectations belief. We show, however, that in most cases where there is sufficient variability in the law of motion that the agents are trying to learn, in sequential models that are extensively used in the economic literature, the rational expectations hypothesis may indeed be justified on the basis of optimizing and optimally updating agents.
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Easley, D. and N. Kiefer: 1986, ‘Controlling a Stochastic Process with Unknown Parameters’, working paper #372, Dept. of Econ., Cornell University.
Easley, D. and N. Kiefer: 1988, ‘Controlling a Stochastic Process with Unknown Parameters’, Econometrica.
El-Gamal, M. and R. Sundaram: 1989, ‘Bayesian Economists … Bayesian Agents I: An Alternative Approach to Optimal Learning’, Soc. Sc. working paper # 705, Caltech.
El-Gamal, M. and R. Sundaram: 1990, ‘Bayesian Economists … Bayesian Agents II: The Evolution of Beliefs in the Single Sector Growth Model’, Soc. Sc. working paper # 736, California Institute of Technology.
El-Gamal, M.: 1989, On the Optimal Processing of Econometric Information, mimeo, Caltech.
Feldman, M., and A. McLennan: 1989, Learning in a Repeated Statistical Decision Problem with Normal Disturbances, mimeo, University of Minnesota.
Kiefer, N.: 1989, ‘A Value Function Arising in the Economics of Information’, Journal of Economic Dynamics and Control 13.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1991 Springer Science+Business Media Dordrecht
About this chapter
Cite this chapter
El-Gamal, M.A. (1991). The Role of Priors in Active Bayesian Learning in the Sequential Statistical Decision Framework. In: Grandy, W.T., Schick, L.H. (eds) Maximum Entropy and Bayesian Methods. Fundamental Theories of Physics, vol 43. Springer, Dordrecht. https://doi.org/10.1007/978-94-011-3460-6_3
Download citation
DOI: https://doi.org/10.1007/978-94-011-3460-6_3
Publisher Name: Springer, Dordrecht
Print ISBN: 978-94-010-5531-4
Online ISBN: 978-94-011-3460-6
eBook Packages: Springer Book Archive