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Keywords:
distributed aggregative game; deceptive strategy; hypergame; $\epsilon $-Nash equilibrium for hypergame
distributed aggregative game; deceptive strategy; hypergame; $\epsilon $-Nash equilibrium for hypergame
Summary:
This paper considers a distributed aggregative game problem for a group of players with misinformation, where each player has a different perception of the game. Player's deception behavior is inevitable in this situation for reducing its own cost. We utilize hypergame to model the above problems and adopt $\epsilon$-Nash equilibrium for hypergame to investigate whether players believe in their own cognition. Additionally, we propose a distributed deceptive algorithm for a player implementing deception and demonstrate the algorithm converges to $\epsilon$-Nash equilibrium for hypergame. Further, we provide conditions for the deceptive player to enhance its profit and offer the optimal deceptive strategy at a given tolerance $\epsilon$. Finally, we present the effectiveness of the algorithm through numerical experiments.
References:
[1] Abuzainab, N., Saad, W.: A multiclass mean-field game for thwarting misinformation spread in the internet of battlefield things. IEEE Trans. Commun. 66 (2018), 12, 6643-6658. DOI
[2] Chen, H., Li, Y., Louie, R. H., Vucetic, B.: Autonomous demand side management based on energy consumption scheduling and instantaneous load billing: An aggregative game approach. IEEE Trans. Smart Grid 5 (2014), 4, 1744-1754. DOI
[3] Chen, J., Zhu, Q.: nterdependent strategic security risk management with bounded rationality in the internet of things. IEEE Trans. Inform. Forensics Security {\mi14} (2019), 11, 2958-2971. DOI
[4] Cheng, Z., Chen, G., Hong, Y.: Single-leader-multiple-followers stackelberg security game with hypergame framework. IEEE Trans. Inform. Forensics Security 17 (2022), 954-969. DOI
[5] Hespanha, J. P., Ateskan, Y. S., al., H. Kizilocak et: Deception in non-cooperative games with partial information. In: Proc. 2nd DARPA-JFACC Symposium on Advances in Enterprise Control, Citeseer 2000, pp. 1-9.
[6] Huang, S., Lei, J., Hong, Y.: A linearly convergent distributed Nash equilibrium seeking algorithm for aggregative games. IEEE Trans. Automat. Control 68 (2022), 3, 1753-1759. DOI | MR 4557578
[7] Huang, L., Zhu, Q.: Duplicity games for deception design with an application to insider threat mitigation. IEEE Trans. Inform. Forensics Security 16 (2021), 4843-4856. DOI
[8] Jelassi, S., Domingo-Enrich, C., Scieur, D., Mensch, A., Bruna, J.: Extragradient with player sampling for faster Nash equilibrium finding. In: Proc. International Conference on Machine Learning 2020.
[9] Jin, R., He, X., Dai, H.: On the security-privacy tradeoff in collaborative security: A quantitative information flow game perspective. IEEE Trans. Inform. Forensics Security 14 (2019), 12, 3273-3286. DOI
[10] Johansson, B., Keviczky, T., Johansson, M., Johansson, K. H.: Subgradient methods and consensus algorithms for solving convex optimization problems. In: 47th IEEE Conference on Decision and Control, IEEE 2008, pp. 4185-4190. DOI
[11] Koshal, J., Nedić, A., Shanbhag, U. V.: Distributed algorithms for aggregative games on graphs. Oper. Res. 64 (2016), 3, 680-704. DOI | MR 3515205
[12] Kovach, N. S., Gibson, A. S., Lamont, G. B.: Hypergame theory: a model for conflict, misperception, and deception. Game Theory (2015). MR 3391789
[13] Lei, J., Shanbhag, U. V.: Asynchronous schemes for stochastic and misspecified potential games and nonconvex optimization. Operations Research 68 (2020), 6, 1742-1766. DOI | MR 4217264
[14] Liang, S., Yi, P., Hong, Y., Peng, K.: Exponentially convergent distributed Nash equilibrium seeking for constrained aggregative games. Autonomous Intell. Systems 2 (2022), 1, 6. DOI | MR 4335720
[15] Ma, J., Yang, Z., Chen, Z.: Distributed Nash equilibrium tracking via the alternating direction method of multipliers. Kybernetika 59 (2023), 4, 612-632. DOI | MR 4660381
[16] Meng, Y., Broom, M., Li, A.: Impact of misinformation in the evolution of collective cooperation on networks. J. Royal Soc. Interface 20 (2023), 206, 20230295. DOI
[17] Meng, Y., Cornelius, S. P., Liu, Y. Y., Li, A.: Dynamics of collective cooperation under personalised strategy updates. Nature Commun. 15 (2024), 1, 3125. DOI
[18] Nedic, A., Ozdaglar, A., Parrilo, P. A.: Constrained consensus and optimization in multi-agent networks. IEEE Trans. Automat. Control 55 (2010), 4, 922-938. DOI | MR 2654432
[19] Nguyen, K. C., Alpcan, T., Basar, T.: Security games with incomplete information. In: 2009 IEEE International Conference on Communications, pp. 1-6. DOI
[20] Paccagnan, D., Gentile, B., Parise, F., Kamgarpour, M., Lygeros, J.: Distributed computation of generalized Nash equilibria in quadratic aggregative games with affine coupling constraints. In: 55th IEEE Conference on Decision and Control, IEEE 2016, pp. 6123-6128. DOI
[21] Paccagnan, D., Gentile, B., Parise, F., Kamgarpour, M., Lygeros, J.: Nash and wardrop equilibria in aggregative games with coupling constraints. IEEE Trans. Automat. Control 64 (2018), 4, 1373-1388. DOI | MR 3936417
[22] Pawlick, J., Colbert, E., Zhu, Q.: Modeling and analysis of leaky deception using signaling games with evidence. IEEE Trans. Inform. Forensics Security 14 (2018), 7, 1871-1886. DOI
[23] Sasaki, Y.: Preservation of misperceptions-stability analysis of hypergames. In: Proc. 52nd Annual Meeting of the ISSS-2008, Madison 2008.
[24] Sasaki, Y.: Generalized Nash equilibrium with stable belief hierarchies in static games with unawareness. Ann. Oper. Res. 256 (2017), 271-284. DOI | MR 3697211
[25] Scutari, G., Palomar, D. P., Facchinei, F., Pang, J.-S.: Convex optimization, game theory, and variational inequality theory. IEEE Signal Process. Magazine 27 (2010), 3, 35-49. DOI | MR 2756856
[26] Wang, M., Hipel, K. W., Fraser, N. M.: Modeling misperceptions in games. Behavioral Sci. 33 (1988), 3, 207-223. DOI | MR 0946274
[27] Wang, J., Zhang, J. F., He, X.: Differentially private distributed algorithms for stochastic aggregative games. Automatica 142 (2022), 110440. DOI | MR 4437624
[28] Xu, G., Chen, G., Qi, H., Hong, Y.: Efficient algorithm for approximating Nash equilibrium of distributed aggregative games. IEEE Trans. Cybernet. 53 (2023), 7, 4375-4387. DOI
[29] Yilmaz, T., Ulusoy, Ö.: Misinformation propagation in online social networks: game theoretic and reinforcement learning approaches. IEEE Trans. Comput. Social Systems (2022). DOI | MR 4682418
[30] Yu, S., Sun, Q., Yang, Z.: Recent advances on false information governance. Control Theory Technol. 21 (2023), 1, 110-113. DOI | MR 4253447
[31] Zhang, H., Qin, H., Chen, G.: Bayesian Nash equilibrium seeking for multi-agent incomplete-information aggregative games. Kybernetika (2023), 575-591, 09 2023. DOI | MR 4660379
[32] Zhang, T. Y., Ye, D.: False data injection attacks with complete stealthiness in cyber-physical systems: A self-generated approach. Automatica 120 (2020), 109117. DOI | MR 4118793
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