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Optimal targeting strategies in a network under complementarities

Author

Listed:
  • Gabrielle Demange

    (PSE - Paris School of Economics - UP1 - Université Paris 1 Panthéon-Sorbonne - ENS-PSL - École normale supérieure - Paris - PSL - Université Paris Sciences et Lettres - EHESS - École des hautes études en sciences sociales - ENPC - École nationale des ponts et chaussées - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement, PJSE - Paris Jourdan Sciences Economiques - UP1 - Université Paris 1 Panthéon-Sorbonne - ENS-PSL - École normale supérieure - Paris - PSL - Université Paris Sciences et Lettres - INRA - Institut National de la Recherche Agronomique - EHESS - École des hautes études en sciences sociales - ENPC - École nationale des ponts et chaussées - CNRS - Centre National de la Recherche Scientifique)

Abstract

The paper analyzes the optimal targeting strategies of a planner (a governmental agency, a firm) who aims to increase the aggregate action of a population. The agents interact through a social network and react to their exposure to neighbors' actions. The reaction function describes, for example, the best response in a strategic game, a mechanical influence in a contagion disease or a mimetic behavior. The reaction is assumed to be increasing in exposure, resulting in complementarity in actions. When it is linear, the optimal planner's strategies are explicit, characterized by well-known centralities indices computed on the bilateral impacts. When the reaction function is concave or convex, the optimal strategies depend not only on the impacts but also on the pattern of agents' attentions. The value of information on the interaction structure is shown to be (almost always) positive and related to some form of heterogeneity between agents.

Suggested Citation

  • Gabrielle Demange, 2017. "Optimal targeting strategies in a network under complementarities," Post-Print halshs-01630621, HAL.
    • Handle: RePEc:hal:journl:halshs-01630621
    DOI: 10.1016/j.geb.2017.07.004
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    Citations

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    Cited by:

    1. Francis Bloch & Shaden Shabayek, 2020. "Targeting in social networks with anonymized information," Papers 2001.03122, arXiv.org.
    2. Kor, Ryan & Zhou, Junjie, 2023. "Multi-activity influence and intervention," Games and Economic Behavior, Elsevier, vol. 137(C), pages 91-115.
    3. Thomas J. Sargent & John Stachurski, 2022. "Economic Networks: Theory and Computation," Papers 2203.11972, arXiv.org, revised Jul 2022.
    4. 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.
    5. Belhaj, Mohamed & Deroïan, Frédéric & Safi, Shahir, 2023. "Targeting in networks under costly agreements," Games and Economic Behavior, Elsevier, vol. 140(C), pages 154-172.
    6. Andrea Galeotti & Benjamin Golub & Sanjeev Goyal, 2020. "Targeting Interventions in Networks," Econometrica, Econometric Society, vol. 88(6), pages 2445-2471, November.
    7. Belhaj, Mohamed & Deroïan, Frédéric, 2018. "Targeting the key player: An incentive-based approach," Journal of Mathematical Economics, Elsevier, vol. 79(C), pages 57-64.
    8. Li, Guopeng & Wang, Sijie & Xiong, Yifan & Zhu, Feng, 2025. "Pricing negative externalities in social networks," Journal of Mathematical Economics, Elsevier, vol. 118(C).
    9. Andrea Galeotti & Benjamin Golub & Sanjeev Goyal & Eduard Talam`as & Omer Tamuz, 2021. "Taxes and Market Power: A Principal Components Approach," Papers 2112.08153, arXiv.org, revised Jun 2022.
    10. Colombo, Luca & Labrecciosa, Paola & Rusinowska, Agnieszka, 2025. "A dynamic analysis of criminal networks," Journal of Economic Theory, Elsevier, vol. 223(C).
    11. Andrea Galeotti & Benjamin Golub & Sanjeev Goyal & Eduard Talam`as & Omer Tamuz, 2024. "Robust Market Interventions," Papers 2411.03026, arXiv.org, revised Jan 2025.
    12. Yang Sun & Wei Zhao & Junjie Zhou, 2021. "Structural Interventions in Networks," Papers 2101.12420, arXiv.org, revised Feb 2021.
    13. Harkins, Andrew, 2020. "Network Comparative Statics," The Warwick Economics Research Paper Series (TWERPS) 1306, University of Warwick, Department of Economics.
    14. 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.
    15. Harkins, Andrew, 2020. "Network Comparative Statics," CRETA Online Discussion Paper Series 64, Centre for Research in Economic Theory and its Applications CRETA.
    16. Belhaj, Mohamed & Deroïan, Frédéric, 2019. "Group targeting under networked synergies," Games and Economic Behavior, Elsevier, vol. 118(C), pages 29-46.
    17. Yang Sun & Wei Zhao & Junjie Zhou, 2023. "Structural Interventions In Networks," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 64(4), pages 1533-1563, November.
    18. Ryan Kor & Junjie Zhou, 2021. "Multi-activity Influence and Intervention," Papers 2106.09410, arXiv.org, revised Nov 2022.
    19. Francis Bloch & Shaden Shabayek, 2023. "Targeting in social networks with anonymized information," Post-Print halshs-04208539, HAL.
    20. Bloch, Francis & Shabayek, Shaden, 2023. "Targeting in social networks with anonymized information," Games and Economic Behavior, Elsevier, vol. 141(C), pages 380-402.
    21. 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.
    22. Li, Jian & Zhou, Junjie & Chen, Ying-Ju, 2022. "The limit of targeting in networks," Journal of Economic Theory, Elsevier, vol. 201(C).
    23. Ryan Kor & Yi Liu & Yves Zenou & Junjie Zhou, 2022. "Welfare and Distributional Effects of Joint Intervention in Networks," Papers 2206.03863, arXiv.org, revised Nov 2025.

    More about this item

    JEL classification:

    • C72 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Noncooperative Games
    • L14 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Transactional Relationships; Contracts and Reputation
    • D85 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Network Formation
    • C69 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Other

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