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Information Preferences of Individual Agents in Linear-Quadratic-Gaussian Network Games

Author

Listed:
  • Furkan Sezer
  • Ceyhun Eksin
Abstract
We consider linear-quadratic-Gaussian (LQG) network games in which agents have quadratic payoffs that depend on their individual and neighbors' actions, and an unknown payoff-relevant state. An information designer determines the fidelity of information revealed to the agents about the payoff state to maximize the social welfare. Prior results show that full information disclosure is optimal under certain assumptions on the payoffs, i.e., it is beneficial for the average individual. In this paper, we provide conditions based on the strength of the dependence of payoffs on neighbors' actions, i.e., competition, under which a rational agent is expected to benefit, i.e., receive higher payoffs, from full information disclosure. We find that all agents benefit from information disclosure for the star network structure when the game is symmetric and submodular or supermodular. We also identify that the central agent benefits more than a peripheral agent from full information disclosure unless the competition is strong and the number of peripheral agents is small enough. Despite the fact that all agents expect to benefit from information disclosure ex-ante, a central agent can be worse-off from information disclosure in many realizations of the payoff state under strong competition, indicating that a risk-averse central agent can prefer uninformative signals ex-ante.

Suggested Citation

  • Furkan Sezer & Ceyhun Eksin, 2022. "Information Preferences of Individual Agents in Linear-Quadratic-Gaussian Network Games," Papers 2203.13056, arXiv.org.
    • Handle: RePEc:arx:papers:2203.13056
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    References listed on IDEAS

    as
    1. Bergemann, Dirk & Morris, Stephen, 2016. "Bayes correlated equilibrium and the comparison of information structures in games," Theoretical Economics, Econometric Society, vol. 11(2), May.
    2. Dirk Bergemann & Stephen Morris, 2019. "Information Design: A Unified Perspective," Journal of Economic Literature, American Economic Association, vol. 57(1), pages 44-95, March.
    3. Kostas Bimpikis & Shayan Ehsani & Rahmi İlkılıç, 2019. "Cournot Competition in Networked Markets," Management Science, INFORMS, vol. 67(6), pages 2467-2481, June.
    4. Pooya Molavi & Ceyhun Eksin & Alejandro Ribeiro & Ali Jadbabaie, 2016. "Learning to Coordinate in Social Networks," Operations Research, INFORMS, vol. 64(3), pages 605-621, June.
    5. Furkan Sezer & Hossein Khazaei & Ceyhun Eksin, 2021. "Maximizing Social Welfare and Agreement via Information Design in Linear-Quadratic-Gaussian Games," Papers 2102.13047, arXiv.org, revised Feb 2023.
    6. Takashi Ui, 2020. "LQG Information Design," Working Papers on Central Bank Communication 018, University of Tokyo, Graduate School of Economics.
    7. Manxi Wu & Saurabh Amin & Asuman E. Ozdaglar, 2021. "Value of Information in Bayesian Routing Games," Operations Research, INFORMS, vol. 69(1), pages 148-163, January.
    8. Ozan Candogan & Kimon Drakopoulos, 2020. "Optimal Signaling of Content Accuracy: Engagement vs. Misinformation," Operations Research, INFORMS, vol. 68(2), pages 497-515, March.
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