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The Law of Large Demand for Information

Author

Listed:
  • Giuseppe Moscarini

    () (Yale University)

  • Lones Smith

    () (University of Michigan)

Abstract

An unresolved problem in Bayesian decision theory is how to value and price information. This paper resolves both problems assuming inexpensive information. Building on Large Deviation Theory, we produce a generically complete asymptotic order on samples of i.i.d. signals in finite-state, finite-action models. Computing the marginal value of an additional signal, we find it is eventually exponentially falling in quantity, and higher for lower quality signals. We provide a precise formula for the information demand, valid at low prices: asymptotically a constant times the log price, and falling in the signal quality for a given price. Copyright The Econometric Society 2002.

Suggested Citation

  • Giuseppe Moscarini & Lones Smith, 2002. "The Law of Large Demand for Information," Econometrica, Econometric Society, vol. 70(6), pages 2351-2366, November.
  • Handle: RePEc:ecm:emetrp:v:70:y:2002:i:6:p:2351-2366
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    Citations

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    Cited by:

    1. Keppo, Jussi & Moscarini, Giuseppe & Smith, Lones, 2008. "The demand for information: More heat than light," Journal of Economic Theory, Elsevier, vol. 138(1), pages 21-50, January.
    2. Athey, Susan & Levin, Jonathan, 2018. "The value of information in monotone decision problems," Research in Economics, Elsevier, vol. 72(1), pages 101-116.
    3. Cabrales, Antonio & Gossner, Olivier & Serrano, Roberto, 2017. "A normalized value for information purchases," Journal of Economic Theory, Elsevier, vol. 170(C), pages 266-288.
    4. Duffie, Darrell & Malamud, Semyon & Manso, Gustavo, 2010. "The relative contributions of private information sharing and public information releases to information aggregation," Journal of Economic Theory, Elsevier, vol. 145(4), pages 1574-1601, July.
    5. Vlastakis, Nikolaos & Markellos, Raphael N., 2012. "Information demand and stock market volatility," Journal of Banking & Finance, Elsevier, vol. 36(6), pages 1808-1821.
    6. Moussa, Faten & Delhoumi, Ezzeddine & Ouda, Olfa Ben, 2017. "Stock return and volatility reactions to information demand and supply," Research in International Business and Finance, Elsevier, vol. 39(PA), pages 54-67.
    7. Philippas, Dionisis & Rjiba, Hatem & Guesmi, Khaled & Goutte, Stéphane, 2019. "Media attention and Bitcoin prices," Finance Research Letters, Elsevier, vol. 30(C), pages 37-43.
    8. Lindset, Snorre & Lund, Arne-Christian & Matsen, Egil, 2009. "Optimal information acquisition for a linear quadratic control problem," European Journal of Operational Research, Elsevier, vol. 199(2), pages 435-441, December.
    9. Chronopoulos, Dimitris K. & Papadimitriou, Fotios I. & Vlastakis, Nikolaos, 2018. "Information demand and stock return predictability," Journal of International Money and Finance, Elsevier, vol. 80(C), pages 59-74.
    10. De Jaegher, K. & Kamphorst, J.J.A., 2015. "Minimal two-way flow networks with small decay," Journal of Economic Behavior & Organization, Elsevier, vol. 109(C), pages 217-239.
    11. Peter I. Frazier & Warren B. Powell, 2010. "Paradoxes in Learning and the Marginal Value of Information," Decision Analysis, INFORMS, vol. 7(4), pages 378-403, December.
    12. Moscarini, Giuseppe, 2004. "Limited information capacity as a source of inertia," Journal of Economic Dynamics and Control, Elsevier, vol. 28(10), pages 2003-2035, September.
    13. Stefano Ficco & Vladimir A. Karamychev, 2004. "Information Overload in Multi-Stage Selection Procedures," Tinbergen Institute Discussion Papers 04-077/1, Tinbergen Institute.
    14. Stefano Ficco, 2004. "Information Overload in Monopsony Markets," Tinbergen Institute Discussion Papers 04-082/1, Tinbergen Institute.
    15. Carole Haritchabalet & Régis Renault, 2006. "Informational externalities with lump‐sum sampling," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 39(3), pages 1005-1022, August.

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