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Optimal Dynamic Incentive Contracts between a Principal and Multiple Agents in Controlled Markov Processes: A Constructive Approach

Author

Listed:
  • Yasuaki Wasa

    (Department of Electrical Engineering and Bioscience, Waseda University, Tokyo 169-8555, Japan.)

  • Ken-Ichi Akao

    (Graduate School of Social Sciences, Waseda University, Tokyo 169-8050, Japan.)

  • Kenko Uchida

    (Research Institute for Science and Engineering, Waseda University, Tokyo 169-8555, Japan.)

Abstract

This paper investigates an optimal dynamic incentive contract between a risk-averse principal (system operator) and multiple risk-averse agents (subsystems) with independently local controllers in continuous-time controlled Markov processes, which can represent various cyber-physical systems. The principal fs incentive design and the agents f decision-makings under asymmetric information structure are known as the principal-agent (PA) problems in economic field. However, the standard framework in economics cannot be directly applied to the realistic control systems including large-scale cyber-physical systems and complex networked systems due to some unrealistic assumptions for an engineering perspective. In this paper, using a constructive approach based on the techniques of the classical stochastic control theory, we propose and solve a novel dynamic control/incentive synthesis for the PA problem under moral hazard.

Suggested Citation

  • Yasuaki Wasa & Ken-Ichi Akao & Kenko Uchida, 2020. "Optimal Dynamic Incentive Contracts between a Principal and Multiple Agents in Controlled Markov Processes: A Constructive Approach," RIEEM Discussion Paper Series 2001, Research Institute for Environmental Economics and Management, Waseda University.
    • Handle: RePEc:was:dpaper:2001
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    File URL: http://www.waseda.jp/prj-rieem/dp/dp2001.pdf
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    References listed on IDEAS

    as
    1. Hyeng Keun Koo & Gyoocheol Shim & Jaeyoung Sung, 2008. "Optimal Multi‐Agent Performance Measures For Team Contracts," Mathematical Finance, Wiley Blackwell, vol. 18(4), pages 649-667, October.
    2. Jakša Cvitanić & Dylan Possamaï & Nizar Touzi, 2018. "Dynamic programming approach to principal–agent problems," Finance and Stochastics, Springer, vol. 22(1), pages 1-37, January.
    3. Patrick Bolton & Mathias Dewatripont, 2005. "Contract Theory," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262025760, April.
    4. Romuald Elie & Dylan Possamai, 2016. "Contracting theory with competitive interacting agents," Papers 1605.08099, arXiv.org.
    5. Williams, Noah, 2015. "A solvable continuous time dynamic principal–agent model," Journal of Economic Theory, Elsevier, vol. 159(PB), pages 989-1015.
    6. Yuliy Sannikov, 2008. "A Continuous-Time Version of the Principal-Agent Problem," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 75(3), pages 957-984.
    7. Schattler, Heinz & Sung, Jaeyoung, 1997. "On optimal sharing rules in discrete-and continuous-time principal-agent problems with exponential utility," Journal of Economic Dynamics and Control, Elsevier, vol. 21(2-3), pages 551-574.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Principal-agent problems; Moral hazard; Cyber-physical systems; Multi-agent systems; Dynamic programming; Risk-sensitive stochastic control; Differential games;
    All these keywords.

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C73 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Stochastic and Dynamic Games; Evolutionary Games
    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design

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