Equilibrium analysis of distributed aggregative game with misinformation

Meng Yuan; Zhaoyang Cheng; Te Ma

Kybernetika (2024)

  • Volume: 60, Issue: 6, page 754-778
  • ISSN: 0023-5954

Abstract

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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 ϵ -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 ϵ -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 ϵ . Finally, we present the effectiveness of the algorithm through numerical experiments.

How to cite

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Yuan, Meng, Cheng, Zhaoyang, and Ma, Te. "Equilibrium analysis of distributed aggregative game with misinformation." Kybernetika 60.6 (2024): 754-778. <http://eudml.org/doc/299897>.

@article{Yuan2024,
abstract = {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.},
author = {Yuan, Meng, Cheng, Zhaoyang, Ma, Te},
journal = {Kybernetika},
keywords = {distributed aggregative game; deceptive strategy; hypergame; $\epsilon $-Nash equilibrium for hypergame},
language = {eng},
number = {6},
pages = {754-778},
publisher = {Institute of Information Theory and Automation AS CR},
title = {Equilibrium analysis of distributed aggregative game with misinformation},
url = {http://eudml.org/doc/299897},
volume = {60},
year = {2024},
}

TY - JOUR
AU - Yuan, Meng
AU - Cheng, Zhaoyang
AU - Ma, Te
TI - Equilibrium analysis of distributed aggregative game with misinformation
JO - Kybernetika
PY - 2024
PB - Institute of Information Theory and Automation AS CR
VL - 60
IS - 6
SP - 754
EP - 778
AB - 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.
LA - eng
KW - distributed aggregative game; deceptive strategy; hypergame; $\epsilon $-Nash equilibrium for hypergame
UR - http://eudml.org/doc/299897
ER -

References

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  1. Abuzainab, N., Saad, W., , IEEE Trans. Commun. 66 (2018), 12, 6643-6658. DOI
  2. Chen, H., Li, Y., Louie, R. H., Vucetic, B., , IEEE Trans. Smart Grid 5 (2014), 4, 1744-1754. DOI
  3. Chen, J., Zhu, Q., , IEEE Trans. Inform. Forensics Security 14 (2019), 11, 2958-2971. DOI
  4. Cheng, Z., Chen, G., Hong, Y., , 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., , IEEE Trans. Automat. Control 68 (2022), 3, 1753-1759. MR4557578DOI
  7. Huang, L., Zhu, Q., , 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., , IEEE Trans. Inform. Forensics Security 14 (2019), 12, 3273-3286. DOI
  10. Johansson, B., Keviczky, T., Johansson, M., Johansson, K. H., , In: 47th IEEE Conference on Decision and Control, IEEE 2008, pp. 4185-4190. DOI
  11. Koshal, J., Nedić, A., Shanbhag, U. V., , Oper. Res. 64 (2016), 3, 680-704. MR3515205DOI
  12. Kovach, N. S., Gibson, A. S., Lamont, G. B., Hypergame theory: a model for conflict, misperception, and deception., Game Theory (2015). MR3391789
  13. Lei, J., Shanbhag, U. V., , Operations Research 68 (2020), 6, 1742-1766. MR4217264DOI
  14. Liang, S., Yi, P., Hong, Y., Peng, K., , Autonomous Intell. Systems 2 (2022), 1, 6. MR4335720DOI
  15. Ma, J., Yang, Z., Chen, Z., , Kybernetika 59 (2023), 4, 612-632. MR4660381DOI
  16. Meng, Y., Broom, M., Li, A., , J. Royal Soc. Interface 20 (2023), 206, 20230295. DOI
  17. Meng, Y., Cornelius, S. P., Liu, Y. Y., Li, A., , Nature Commun. 15 (2024), 1, 3125. DOI
  18. Nedic, A., Ozdaglar, A., Parrilo, P. A., , IEEE Trans. Automat. Control 55 (2010), 4, 922-938. MR2654432DOI
  19. Nguyen, K. C., Alpcan, T., Basar, T., , In: 2009 IEEE International Conference on Communications, pp. 1-6. DOI
  20. Paccagnan, D., Gentile, B., Parise, F., Kamgarpour, M., Lygeros, J., , 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., , IEEE Trans. Automat. Control 64 (2018), 4, 1373-1388. MR3936417DOI
  22. Pawlick, J., Colbert, E., Zhu, Q., , 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., , Ann. Oper. Res. 256 (2017), 271-284. MR3697211DOI
  25. Scutari, G., Palomar, D. P., Facchinei, F., Pang, J.-S., , IEEE Signal Process. Magazine 27 (2010), 3, 35-49. MR2756856DOI
  26. Wang, M., Hipel, K. W., Fraser, N. M., , Behavioral Sci. 33 (1988), 3, 207-223. MR0946274DOI
  27. Wang, J., Zhang, J. F., He, X., , Automatica 142 (2022), 110440. MR4437624DOI
  28. Xu, G., Chen, G., Qi, H., Hong, Y., , IEEE Trans. Cybernet. 53 (2023), 7, 4375-4387. DOI
  29. Yilmaz, T., Ulusoy, Ö., , IEEE Trans. Comput. Social Systems (2022). MR4682418DOI
  30. Yu, S., Sun, Q., Yang, Z., , Control Theory Technol. 21 (2023), 1, 110-113. MR4253447DOI
  31. Zhang, H., Qin, H., Chen, G., , Kybernetika (2023), 575-591, 09 2023. MR4660379DOI
  32. Zhang, T. Y., Ye, D., , Automatica 120 (2020), 109117. MR4118793DOI

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