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Bayesian Representation of Stochastic Processes under Learning: de Finetti Revisited

Author

Listed:
  • Matthew O. Jackson
  • Ehud Kalai
  • Rann Smorodinsky

Abstract

A probability distribution governing the evolution of a stochastic process has infinitely many Bayesian representations of the form mu = integral operator [subscript theta] mu[subscript theta] delta lambda (theta). Among these, a natural representation is one whose components (mu[subscript theta]'s) are 'learnable' (one can approximate mu[subscript theta] by conditioning mu on observation of the process) and 'sufficient for prediction' (mu[subscript theta]'s predictions are not aided by conditioning on observation of the process). The authors show the existence and uniqueness of such a representation under a suitable asymptotic mixing condition on the process. This representation can be obtained by conditioning on the tail-field of the process, and any learnable representation that is sufficient for prediction is asymptotically like the tail-field representation. This result is related to the celebrated de Finetti theorem, but with exchangeability weakened to an asymptotic mixing condition, and with his conclusion of a decomposition into i.i.d. component distributions weakened to components that are learnable and sufficient for prediction.

Suggested Citation

  • Matthew O. Jackson & Ehud Kalai & Rann Smorodinsky, 1999. "Bayesian Representation of Stochastic Processes under Learning: de Finetti Revisited," Econometrica, Econometric Society, vol. 67(4), pages 875-894, July.
    • Handle: RePEc:ecm:emetrp:v:67:y:1999:i:4:p:875-894
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    1. Bayesian consistency
      by Eran in The Leisure of the Theory Class on 2014-08-09 21:07:10

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    1. Maria Minniti & William Bygrave, 2001. "A Dynamic Model of Entrepreneurial Learning," Entrepreneurship Theory and Practice, , vol. 25(3), pages 5-16, April.
    2. Dean Foster & Rakesh Vohra, 2011. "Calibration: Respice, Adspice, Prospice," Discussion Papers 1537, Northwestern University, Center for Mathematical Studies in Economics and Management Science.
    3. Fernandes, Marcos R., 2023. "Confirmation bias in social networks," Mathematical Social Sciences, Elsevier, vol. 123(C), pages 59-76.
    4. ,, 2015. "Merging with a set of probability measures: a characterization," Theoretical Economics, Econometric Society, vol. 10(2), May.
    5. Mario Gilli, 2002. "Rational Learning in Imperfect Monitoring Games," Working Papers 46, University of Milano-Bicocca, Department of Economics, revised Mar 2002.
    6. Beker, Pablo & Chattopadhyay, Subir, 2010. "Consumption dynamics in general equilibrium: A characterisation when markets are incomplete," Journal of Economic Theory, Elsevier, vol. 145(6), pages 2133-2185, November.
    7. Nabil I. Al-Najjar & Luciano De Castro, 2010. "Prediction Markets to Forecast Electricity Demand," Discussion Papers 1529, Northwestern University, Center for Mathematical Studies in Economics and Management Science.
    8. , & , & ,, 2016. "Fragility of asymptotic agreement under Bayesian learning," Theoretical Economics, Econometric Society, vol. 11(1), January.
    9. Turdaliev, Nurlan, 2002. "Calibration and Bayesian learning," Games and Economic Behavior, Elsevier, vol. 41(1), pages 103-119, October.
    10. Luciano Castro & Alain Chateauneuf, 2011. "Ambiguity aversion and trade," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 48(2), pages 243-273, October.
    11. Nabil I. Al-Najjar & Eran Shmaya, 2015. "Uncertainty and Disagreement in Equilibrium Models," Journal of Political Economy, University of Chicago Press, vol. 123(4), pages 778-808.
    12. Al-Najjar, Nabil I. & Sandroni, Alvaro & Smorodinsky, Rann & Weinstein, Jonathan, 2010. "Testing theories with learnable and predictive representations," Journal of Economic Theory, Elsevier, vol. 145(6), pages 2203-2217, November.
    13. Levy, Yehuda John, 2015. "Limits to rational learning," Journal of Economic Theory, Elsevier, vol. 160(C), pages 1-23.
    14. Peter J. Hammond & Yeneng Sun, 2003. "Monte Carlo simulation of macroeconomic risk with a continuum of agents: the symmetric case," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 21(2), pages 743-766, March.
    15. Al-Najjar, Nabil I. & Shmaya, Eran, 2018. "Learning the fundamentals in a stationary environment," Games and Economic Behavior, Elsevier, vol. 109(C), pages 616-624.
    16. Daron Acemoglu & Victor Chernozhukov & Muhamet Yildiz, 2006. "Learning and Disagreement in an Uncertain World," NBER Working Papers 12648, National Bureau of Economic Research, Inc.
    17. Lars Peter Hansen & Thomas J Sargent, 2014. "Robust Control and Model Misspecification," World Scientific Book Chapters, in: UNCERTAINTY WITHIN ECONOMIC MODELS, chapter 6, pages 155-216, World Scientific Publishing Co. Pte. Ltd..
    18. Pe[combining cedilla]ski, Marcin, 2011. "Prior symmetry, similarity-based reasoning, and endogenous categorization," Journal of Economic Theory, Elsevier, vol. 146(1), pages 111-140, January.
    19. John H. Nachbar, 2005. "Beliefs in Repeated Games," Econometrica, Econometric Society, vol. 73(2), pages 459-480, March.
    20. Al-Najjar, Nabil & Sandroni, Alvaro, 2013. "A difficulty in the testing of strategic experts," Mathematical Social Sciences, Elsevier, vol. 65(1), pages 5-9.

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