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Targeting the key player: An incentive-based approach

Author

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  • Mohamed Belhaj

    (AMSE - Aix-Marseille Sciences Economiques - EHESS - École des hautes études en sciences sociales - AMU - Aix Marseille Université - ECM - École Centrale de Marseille - CNRS - Centre National de la Recherche Scientifique)

  • Frédéric Deroïan

    (AMSE - Aix-Marseille Sciences Economiques - EHESS - École des hautes études en sciences sociales - AMU - Aix Marseille Université - ECM - École Centrale de Marseille - CNRS - Centre National de la Recherche Scientifique)

Abstract

We consider a network game with local complementarities. A policymaker, aiming at minimizing or maximizing aggregate effort, contracts with a single agent on the network to trade effort change against transfer. The policymaker has to find the best agent and the optimal contract to offer. Our study shows that for all utilities with linear best-responses, it only takes two statistics about the position of each agent on the network to identify the key player: the Bonacich centrality and the self-loop centrality. We also characterize key players under linear quadratic utilities for various contractual arrangements.

Suggested Citation

  • Mohamed Belhaj & Frédéric Deroïan, 2018. "Targeting the key player: An incentive-based approach," Post-Print hal-01981885, HAL.
    • Handle: RePEc:hal:journl:hal-01981885
    DOI: 10.1016/j.jmateco.2018.10.001
    Note: View the original document on HAL open archive server: https://amu.hal.science/hal-01981885v1
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    Cited by:

    1. Michel Grabisch & Elena Parilina & Agnieszka Rusinowska & Georges Zaccour, 2025. "Dynamic Network Formation with Farsighted Players and Limited Capacities," Documents de travail du Centre d'Economie de la Sorbonne 25019, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
    2. Mohamed Belhaj & Frédéric Deroïan & Shahir Safi, 2020. "Costly agreement-based transfers and targeting on networks with synergies," AMSE Working Papers 2015, Aix-Marseille School of Economics, France.
    3. Belhaj, Mohamed & Deroïan, Frédéric & Safi, Shahir, 2023. "Targeting in networks under costly agreements," Games and Economic Behavior, Elsevier, vol. 140(C), pages 154-172.
    4. Sun, Yang & Zhao, Wei, 2024. "Relative performance evaluation in spillover networks," Games and Economic Behavior, Elsevier, vol. 145(C), pages 285-311.
    5. Marc Claveria-Mayol, 2024. "Moral Hazard with Network Effects," Papers 2406.11660, arXiv.org.
    6. Li, Jian & Zhou, Junjie & Chen, Ying-Ju, 2021. "The Limit of Targeting in Networks," ISU General Staff Papers 202112081957590000, Iowa State University, Department of Economics.
    7. Yifan Xiong & Youze Lang & Ziyan Li, 2024. "Cost intervention in delinquent networks," Social Choice and Welfare, Springer;The Society for Social Choice and Welfare, vol. 62(2), pages 321-344, March.
    8. Li, Jian & Zhou, Junjie & Chen, Ying-Ju, 2022. "The limit of targeting in networks," Journal of Economic Theory, Elsevier, vol. 201(C).
    9. Frédéric Deroïan & Philippine Escudie, 2025. "Addiction in networks," AMSE Working Papers 2506, Aix-Marseille School of Economics, France.

    More about this item

    JEL classification:

    • C72 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Noncooperative Games
    • D85 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Network Formation

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