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Dynamic Resource Allocation Networks in Marketing: Comparing the Effectiveness of Control Methods

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  • N. M. Galieva

    (St. Petersburg Branch of the Higher School of Economics)

  • A. V. Korolev

    (St. Petersburg Branch of the Higher School of Economics)

  • G. A. Ougolnitsky

    (Southern Federal University)

Abstract

The discrete- and continuous-time network models of opinions control and resource allocation in marketing are considered. Three cases of interaction of economic agents are studied: independent behavior, cooperation, and hierarchical control by the resource-owning Principal. The corresponding dynamic games are analytically solved. The agents’ payoffs in these cases are compared. Two concepts, “enough resources” and “a lack of resources,” are introduced and investigated. The theoretical results are illustrated by a numerical example.

Suggested Citation

  • N. M. Galieva & A. V. Korolev & G. A. Ougolnitsky, 2024. "Dynamic Resource Allocation Networks in Marketing: Comparing the Effectiveness of Control Methods," Dynamic Games and Applications, Springer, vol. 14(2), pages 362-395, May.
    • Handle: RePEc:spr:dyngam:v:14:y:2024:i:2:d:10.1007_s13235-023-00494-y
    DOI: 10.1007/s13235-023-00494-y
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    References listed on IDEAS

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    1. Garry Robins & Philippa Pattison & Peter Elliott, 2001. "Network models for social influence processes," Psychometrika, Springer;The Psychometric Society, vol. 66(2), pages 161-189, June.
    2. Michael Suk-Young Chwe, 2000. "Communication and Coordination in Social Networks," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 67(1), pages 1-16.
    3. Rainer Hegselmann & Ulrich Krause, 2002. "Opinion Dynamics and Bounded Confidence Models, Analysis and Simulation," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 5(3), pages 1-2.
    4. David Godes & Dina Mayzlin, 2004. "Using Online Conversations to Study Word-of-Mouth Communication," Marketing Science, INFORMS, vol. 23(4), pages 545-560, June.
    5. Peter M. DeMarzo & Dimitri Vayanos & Jeffrey Zwiebel, 2003. "Persuasion Bias, Social Influence, and Unidimensional Opinions," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 118(3), pages 909-968.
    6. Weber, Gerhard-Wilhelm & Defterli, Ozlem & Alparslan Gök, SIrma Zeynep & Kropat, Erik, 2011. "Modeling, inference and optimization of regulatory networks based on time series data," European Journal of Operational Research, Elsevier, vol. 211(1), pages 1-14, May.
    7. Artem Sedakov, 2020. "Characteristic Function and Time Consistency for Two-Stage Games with Network Externalities," Mathematics, MDPI, vol. 8(1), pages 1-9, January.
    8. Dockner,Engelbert J. & Jorgensen,Steffen & Long,Ngo Van & Sorger,Gerhard, 2000. "Differential Games in Economics and Management Science," Cambridge Books, Cambridge University Press, number 9780521637329, December.
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