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A Principal-Agent Model of Sequential Testing

Author

Listed:
  • Dino Gerardi
  • Lucas Maestri

Abstract

This paper analyzes the optimal provision of incentives in a sequential testing context. In every period the agent can acquire costly information that is relevant to the principal's decision. Neither the agent's effort nor the realizations of his signals are observable. First, we assume that the principal and the agent are symmetrically informed at the time of contracting. We construct the optimal mechanism and show that the agent is indifferent in every period between performing the test and sending an uninformative message which continues the relationship. Furthermore, in the first period the agent is indifferent between carrying out his task and sending an uninformative message which ends the relationship immediately. We then characterize the optimal mechanisms when the agent has superior information at the outset of the relationship. The principal prefers to offer different contracts if and only if the agent types are sufficiently diverse. Finally, all agent types benefit from their initial private information.

Suggested Citation

  • Dino Gerardi & Lucas Maestri, 2009. "A Principal-Agent Model of Sequential Testing," Carlo Alberto Notebooks 115, Collegio Carlo Alberto.
  • Handle: RePEc:cca:wpaper:115
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    Cited by:

    1. Chen, Chia-Hui & Ishida, Junichiro, 2018. "Hierarchical experimentation," Journal of Economic Theory, Elsevier, vol. 177(C), pages 365-404.
    2. Nicolas Klein & Tymofiy Mylovanov, 2011. "Should the Flatterers be Avoided?," 2011 Meeting Papers 1273, Society for Economic Dynamics.
    3. Carroll, Gabriel, 2019. "Robust incentives for information acquisition," Journal of Economic Theory, Elsevier, vol. 181(C), pages 382-420.
    4. Gomes, Renato & Gottlieb, Daniel & Maestri, Lucas, 2016. "Experimentation and project selection: Screening and learning," Games and Economic Behavior, Elsevier, vol. 96(C), pages 145-169.
    5. Chia-Hui Chen & Junichiro Ishida, 2015. "A Tenure-Clock Problem," ISER Discussion Paper 0919, Institute of Social and Economic Research, The University of Osaka.
    6. Jin Hyuk Choi & Kookyoung Han, 2023. "Delegation of information acquisition, information asymmetry, and outside option," International Journal of Game Theory, Springer;Game Theory Society, vol. 52(3), pages 833-860, September.
    7. Chia‐Hui Chen & Junichiro Ishida, 2018. "Dynamic performance evaluation with deadlines: The role of commitment," Journal of Industrial Economics, Wiley Blackwell, vol. 66(2), pages 377-422, June.
    8. Samuel Häfner & Curtis R. Taylor, 2022. "On young Turks and yes men: optimal contracting for advice," RAND Journal of Economics, RAND Corporation, vol. 53(1), pages 63-94, March.
    9. Khalil, Fahad & Lawarree, Jacques & Rodivilov, Alexander, 2020. "Learning from failures: Optimal contracts for experimentation and production," Journal of Economic Theory, Elsevier, vol. 190(C).
    10. Johannes Hörner & Larry Samuelson, 2013. "Incentives for experimenting agents," RAND Journal of Economics, RAND Corporation, vol. 44(4), pages 632-663, December.
    11. Achim, Peter, 2024. "Innovation through competitive experimentation," Journal of Mathematical Economics, Elsevier, vol. 111(C).
    12. Catherine Bobtcheff & Raphaël Levy, 2017. "More Haste, Less Speed? Signaling through Investment Timing," American Economic Journal: Microeconomics, American Economic Association, vol. 9(3), pages 148-186, August.
    13. Gao, Hong & Xu, Haibo, 2020. "Learning, belief manipulation and optimal relationship termination," Economics Letters, Elsevier, vol. 190(C).
    14. Alessandro Spiganti, 2020. "Can Starving Start‐ups Beat Fat Labs? A Bandit Model of Innovation with Endogenous Financing Constraint," Scandinavian Journal of Economics, Wiley Blackwell, vol. 122(2), pages 702-731, April.
    15. Rodivilov, Alexander, 2022. "Monitoring innovation," Games and Economic Behavior, Elsevier, vol. 135(C), pages 297-326.

    More about this item

    Keywords

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    JEL classification:

    • C72 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Noncooperative Games
    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness

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