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Learning to Disagree in a Game of Experimentation

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Abstract

We analyse strategic experimentation in which information arrives through fully revealing, publicly observable "breakdowns." With hidden actions, there exists a unique equilibrium that involves randomization over stopping times. This randomization induces belief disagreement on the equilibrium path. When actions are observable, the equilibrium is pure, and welfare improves. We analyse the role of policy interventions such as subsidies for experimentation and risk-sharing agreements. We show that the optimal risk-sharing agreement restores the first-best outcome, independent of the monitoring structure.

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  • Alessandro Bonatti & Johannes Horner, 2015. "Learning to Disagree in a Game of Experimentation," Cowles Foundation Discussion Papers 1991, Cowles Foundation for Research in Economics, Yale University.
  • Handle: RePEc:cwl:cwldpp:1991
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    1. Bonatti, Alessandro & Hörner, Johannes, 2017. "Learning to disagree in a game of experimentation," Journal of Economic Theory, Elsevier, vol. 169(C), pages 234-269.
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    1. Bonatti, Alessandro & Hörner, Johannes, 2017. "Learning to disagree in a game of experimentation," Journal of Economic Theory, Elsevier, vol. 169(C), pages 234-269.
    2. Azoulay, Pierre & Bonatti, Alessandro & Krieger, Joshua L., 2017. "The career effects of scandal: Evidence from scientific retractions," Research Policy, Elsevier, vol. 46(9), pages 1552-1569.
    3. Aghamolla, Cyrus & Hashimoto, Tadashi, 2020. "Information arrival, delay, and clustering in financial markets with dynamic freeriding," Journal of Financial Economics, Elsevier, vol. 138(1), pages 27-52.
    4. Hendrik Hakenes & Svetlana Katolnik, 2018. "Optimal Team Size and Overconfidence," Group Decision and Negotiation, Springer, vol. 27(4), pages 665-687, August.
    5. Georgiadis, George, 2017. "Deadlines and infrequent monitoring in the dynamic provision of public goods," Journal of Public Economics, Elsevier, vol. 152(C), pages 1-12.
    6. Wong, Tsz-Ning & Yang, Lily Ling, 2021. "Dynamic expert incentives in teams," Games and Economic Behavior, Elsevier, vol. 125(C), pages 27-47.
    7. Li, Jin & Mukherjee, Arijit & Vasconcelos, Luis, 2019. "Managing performance evaluation systems: Relational incentives in the presence of learning-by-shirking," Working Papers 2018-12, Michigan State University, Department of Economics.
    8. Diekert, Florian K., 2017. "Threatening thresholds? The effect of disastrous regime shifts on the non-cooperative use of environmental goods and services," Journal of Public Economics, Elsevier, vol. 147(C), pages 30-49.
    9. Chen, Yi, 2020. "A revision game of experimentation on a common threshold," Journal of Economic Theory, Elsevier, vol. 186(C).

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    More about this item

    Keywords

    Experimentation; Free-riding; Mixed strategies; Monitoring; Delay;
    All these keywords.

    JEL classification:

    • C73 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Stochastic and Dynamic Games; Evolutionary Games
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes

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