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Common value experimentation

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  • Eeckhout, Jan
  • Weng, Xi

Abstract

In many economic environments, agents often continue to learn about the same underlying state variable, even if they switch action. For example, a worker's ability revealed in one job or when unemployed is informative about her productivity in another job. We analyze a general setup of experimentation with common values, and show that in addition to the well-known conditions of value matching (level) and smooth pasting (first derivative), this implies that the second derivatives of the value function must be equal whenever the agent switches action. This condition holds generally whenever the stochastic process has continuous increments. The main appeal of our approach is its applicability, which is demonstrated with two applications featuring common value experimentation: strategic pricing, and job search with switching costs.

Suggested Citation

  • Eeckhout, Jan & Weng, Xi, 2015. "Common value experimentation," Journal of Economic Theory, Elsevier, vol. 160(C), pages 317-339.
  • Handle: RePEc:eee:jetheo:v:160:y:2015:i:c:p:317-339
    DOI: 10.1016/j.jet.2015.10.002
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    References listed on IDEAS

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    1. Godfrey Keller & Sven Rady, 1999. "Optimal Experimentation in a Changing Environment," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 66(3), pages 475-507.
    2. Martin W. Cripps & Jeffrey C. Ely & George J. Mailath & Larry Samuelson, 2008. "Common Learning," Econometrica, Econometric Society, vol. 76(4), pages 909-933, July.
    3. Godfrey Keller & Sven Rady & Martin Cripps, 2005. "Strategic Experimentation with Exponential Bandits," Econometrica, Econometric Society, vol. 73(1), pages 39-68, January.
    4. Patrick Bolton & Christopher Harris, 1999. "Strategic Experimentation," Econometrica, Econometric Society, vol. 67(2), pages 349-374, March.
    5. Guido Menzio & Shouyong Shi, 2011. "Efficient Search on the Job and the Business Cycle," Journal of Political Economy, University of Chicago Press, vol. 119(3), pages 468-510.
    6. Bergemann, Dirk & Valimaki, Juuso, 1996. "Learning and Strategic Pricing," Econometrica, Econometric Society, vol. 64(5), pages 1125-1149, September.
    7. Dirk Bergemann & Juuso Välimäki, 2000. "Experimentation in Markets," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 67(2), pages 213-234.
    8. Franz Wirl, 2008. "Reversible stopping (“switching”) implies super contact," Computational Management Science, Springer, vol. 5(4), pages 393-401, October.
    9. Dumas, Bernard, 1991. "Super contact and related optimality conditions," Journal of Economic Dynamics and Control, Elsevier, vol. 15(4), pages 675-685, October.
    10. Alessandro Bonatti, 2011. "Menu Pricing and Learning," American Economic Journal: Microeconomics, American Economic Association, vol. 3(3), pages 124-163, August.
    11. Theodore Papageorgiou, 2014. "Learning Your Comparative Advantages," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 81(3), pages 1263-1295.
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    Citations

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    Cited by:

    1. Felli, Leonardo & Harris, Christopher, 2018. "Firm-specific training," Journal of Economic Theory, Elsevier, vol. 175(C), pages 585-623.
    2. Braz Camargo & Elena Pastorino, 2016. "Learning-by-Employing: The Value of Commitment under Uncertainty," Journal of Labor Economics, University of Chicago Press, vol. 34(3), pages 581-620.
    3. Jan Eeckhout & Xi Weng, 2022. "Assortative Learning," Economica, London School of Economics and Political Science, vol. 89(355), pages 647-688, July.

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    More about this item

    Keywords

    Common values; Experimentation; Jump-diffusion process;
    All these keywords.

    JEL classification:

    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • C02 - Mathematical and Quantitative Methods - - General - - - Mathematical Economics
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis

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