Open Access
SHS Web of Conf.
Volume 178, 2023
3rd International Conference on Public Relations and Social Sciences (ICPRSS 2023)
Article Number 03005
Number of page(s) 10
Section Corporate Marketing Strategy and Innovation Development
Published online 23 October 2023
  1. Anastassacos, N., Hailes, S., & Musolesi, M. (2020, April). Partner selection for the emergence of cooperation in multi-agent systems using reinforcement learning. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 34, No. 05, pp. 7047-7054). [CrossRef] [Google Scholar]
  2. Axelrod, R. (1980). Effective choice in the prisoner’s dilemma. Journal of conflict resolution, 24(1), 3-25. [CrossRef] [Google Scholar]
  3. Campbell, R., & Sowden, L. (Eds.). (1985). Paradoxes of rationality and cooperation: prisoner’s dilemma and Newcomb’s problem. UBC Press. [Google Scholar]
  4. Chong, S. Y., Humble, J., Kendall, G., Li, J., & Yao, X. (2007). Iterated prisoner’s dilemma and evolutionary game theory. In The Iterated Prisoners’ Dilemma: 20 Years On (pp. 23-62). [Google Scholar]
  5. Collenette, J., Atkinson, K., Bloembergen, D., & Tuyls, K. (2017, September). Mood modelling within reinforcement learning. In ECAL 2017, the Fourteenth European Conference on Artificial Life (pp. 106-113). MIT Press. [Google Scholar]
  6. Collenette, J., Atkinson, K., Bloembergen, D., & Tuyls, K. (2019, July). Stability of cooperation in societies of emotional and moody agents. In Artificial Life Conference Proceedings (pp. 467-474). One Rogers Street, Cambridge, MA 02142-1209, USA journals-info@ mit. edu: MIT Press. [Google Scholar]
  7. Fan, L., Song, Z., Wang, L., Liu, Y., & Wang, Z. (2022). Incorporating social payoff into reinforcement learning promotes cooperation. Chaos: An Interdisciplinary Journal of Nonlinear Science, 32(12), 123140. [CrossRef] [Google Scholar]
  8. Feehan, G., & Fatima, S. (2022). Augmenting Reinforcement Learning to Enhance Cooperation in the Iterated Prisoner’s Dilemma. In ICAART (3) (pp. 146-157). [Google Scholar]
  9. Felkins, L. (2001). The Prisoner’s Dilemma. [Google Scholar]
  10. Fernández-Domingos, E., Loureiro, M., Alvarez-López, T., Burguillo, J. C., Covelo, J., Peleteiro, A., & Byrski, A. (2017). Emerging Cooperation in N-Person Iterated Prisoner’s Dilemma over Dynamic Complex Networks. Computing & Informatics, 36(3). [Google Scholar]
  11. Fujimoto, Y., & Kaneko, K. (2019). Emergence of exploitation as symmetry breaking in iterated prisoner’s dilemma. Physical Review Research, 1(3), 033077. [CrossRef] [Google Scholar]
  12. Gill, D., & Rosokha, Y. (2020). Beliefs, learning, and personality in the indefinitely repeated prisoner’s dilemma. Available at SSRN 3652318. [Google Scholar]
  13. Gotts, N. M., Polhill, J. G., & Law, A. N. R. (2003). Agent-based simulation in the study of social dilemmas. Artificial Intelligence Review, 19, 3-92. [CrossRef] [Google Scholar]
  14. Guo, H., Wang, Z., Song, Z., Yuan, Y., Deng, X., & Li, X. (2022). Effect of state transition triggered by reinforcement learning in evolutionary prisoner’s dilemma game. Neurocomputing, 511, 187-197. [CrossRef] [Google Scholar]
  15. Heller, Y., & Mohlin, E. (2018). Observations on cooperation. The Review of Economic Studies, 85(4), 2253-2282. [CrossRef] [Google Scholar]
  16. Hofstadter, D. R. (1983). Metamagical themas. Scientific American, 248(5), 16-E18. [CrossRef] [Google Scholar]
  17. Ichinose, G., Satotani, Y., & Nagatani, T. (2018). Network flow of mobile agents enhances the evolution of cooperation. Europhysics Letters, 121(2), 28001. [CrossRef] [Google Scholar]
  18. Jiang, J., & Lu, Z. (2018). Learning attentional communication for multi-agent cooperation. Advances in neural information processing systems, 31. [Google Scholar]
  19. Kopelman, S. (2020). Tit for tat and beyond: The legendary work of Anatol Rapoport. Negotiation and Conflict Management Research, 13(1), 60-84. [CrossRef] [Google Scholar]
  20. Lazaridou, A., Peysakhovich, A., & Baroni, M. (2016). Multi-agent cooperation and the emergence of (natural) language. arXiv preprint arXiv:1612.07182. [Google Scholar]
  21. Li, J., Park, J. H., Zhang, J., Chen, Z., & Dehmer, M. (2020). The networked cooperative dynamics of adjusting signal strength based on information quantity. Nonlinear Dynamics, 100(1), 831-847. [CrossRef] [Google Scholar]
  22. Liu, X., Guan, R., Wang, T., Han, L., Qin, Y., & Wang, Y. (2021, August). Multi-hop Learning Promote Cooperation in Multi-agent Systems. In Knowledge Science, Engineering and Management: 14th International Conference, KSEM 2021, Tokyo, Japan, August 14–16, 2021, Proceedings, Part I (pp. 66-77). Cham: Springer International Publishing. [Google Scholar]
  23. Lotfi, N., & Rodrigues, F. A. (2022). On the effect of memory on the Prisoner’s Dilemma game in correlated networks. Physica A: Statistical Mechanics and its Applications, 607, 128162. [CrossRef] [Google Scholar]
  24. McLeod, S. (2015). Operant Conditioning: What It Is, How It Works, and Examples. [Google Scholar]
  25. Moriyama, K., Nakase, K., Mutoh, A., & Inuzuka, N. (2017, July). The resilience of cooperation in a Dilemma game played by reinforcement learning agents. In 2017 IEEE International Conference on Agents (ICA) (pp. 33-39). IEEE. [Google Scholar]
  26. Otsuka, T., & Sugawara, T. (2017, August). Robust spread of cooperation by expectation-of-cooperation strategy with simple labeling method. In Proceedings of the International Conference on Web Intelligence (pp. 483-490). [Google Scholar]
  27. Otsuka, T., & Sugawara, T. (2018). Promotion of robust cooperation among agents in complex networks by enhanced expectation-of-cooperation strategy. In Complex Networks & Their Applications VI: Proceedings of Complex Networks 2017 (The Sixth International Conference on Complex Networks and Their Applications) (pp. 815-828). Springer International Publishing. [Google Scholar]
  28. Rapoport, A. (1989). Prisoner’s dilemma. Game theory, 199-204. [Google Scholar]
  29. Sandholm, T. W., & Crites, R. H. (1996). Multiagent reinforcement learning in the iterated prisoner’s dilemma. Biosystems, 37( [Google Scholar]
  30. Seredyński, F., & Gąsior, J. (2019). Emergence of collective behavior in large cellular automata-based multi-agent systems. In Artificial Intelligence and Soft Computing: 18th International Conference, ICAISC 2019, Zakopane, Poland, June 16–20, 2019, Proceedings, Part II 18 (pp. 676-688). Springer International Publishing. [Google Scholar]
  31. Shang, L., & Luo, H. (2021, July). Environmental adaptability promotes cooperation in the evolutionary game. In 2021 40th Chinese Control Conference (CCC) (pp. 7486-7491). IEEE. [Google Scholar]
  32. Takesue, H. (2018). Evolutionary prisoner’s dilemma games on the network with punishment and opportunistic partner switching. Europhysics Letters, 121(4), 48005. [CrossRef] [Google Scholar]
  33. Takesue, H. (2021). Symmetry breaking in the Prisoner’s Dilemma on two-layer dynamic multiplex networks. Applied Mathematics and Computation, 388, 125543. [CrossRef] [Google Scholar]
  34. Tao, W., Wei, W., Xin, Y., & Meiqi, H. (2022, February). Strategies to Promote Cooperation in Mobile Networks. In 2022 8th International Conference on Automation, Robotics and Applications (ICARA) (pp. 140-145). IEEE. [Google Scholar]
  35. Tucker, A. W., & Straffin Jr, P. D. (1983). The mathematics of Tucker: A sampler. The Two-Year College Mathematics Journal, 14(3), 228-232. [CrossRef] [Google Scholar]
  36. Wang, S. Y., Liu, Y. P., Zhang, F., & Wang, R. W. (2021). Super-rational aspiration induced strategy updating promotes cooperation in the asymmetric prisoner’s dilemma game. Applied Mathematics and Computation, 403, 126180. [CrossRef] [Google Scholar]
  37. Wang, S., & Jiang, L. (2019). Study of Agent Cooperation Incentive Strategy Based on Game Theory in Multi-Agent System. In Communications, Signal Processing, and Systems: Proceedings of the 2017 International Conference on Communications, Signal Processing, and Systems (pp. 1871-1878). Springer Singapore. [Google Scholar]
  38. Wang, T., Li, L., Zhang, S., Peng, H., Yu, L., & Chen, Z. (2016, August). Memory mechanism enhances cooperation in mobile multi-agent system. In 2016 8th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC) (Vol. 2, pp. 476-479). IEEE. [Google Scholar]
  39. Wang, X., Zhang, L., Du, X., & Sun, Y. (2017). Evolving cooperation in spatial population with punishment by using PSO algorithm. Natural Computing, 16, 99-117. [CrossRef] [Google Scholar]
  40. Wu, Y. E., Zhang, Z., & Chang, S. (2017). Effect of self-interaction on the evolution of cooperation in complex topologies. Physica A: Statistical Mechanics and its Applications, 481, 191-197. [CrossRef] [Google Scholar]
  41. Wu, Y. E., Zhang, Z., & Chang, S. (2018). Heterogeneous indirect reciprocity promotes the evolution of cooperation in structured populations. Chaos: An Interdisciplinary Journal of Nonlinear Science, 28(12), 123108. [CrossRef] [Google Scholar]
  42. Xu, C., & Hui, P. M. (2019). Emergence of cooperation in finite populations under biased selection. Physica A: Statistical Mechanics and its Applications, 535, 122371. [CrossRef] [Google Scholar]
  43. Xu, X., Rong, Z., & Tse, C. K. (2018, May). Bounded rationality optimizes the performance of networked systems in prisoner’s dilemma game. In 2018 IEEE International Symposium on Circuits and Systems (ISCAS) (pp. 1-5). IEEE. [Google Scholar]
  44. Xuan, P., Lesser, V., & Zilberstein, S. (2001, May). Communication decisions in multi-agent cooperation: Model and experiments. In Proceedings of the fifth international conference on Autonomous agents (pp. 616-623). [Google Scholar]
  45. Xue, L., Sun, C., Wunsch, D., Zhou, Y., & Yu, F. (2017). An adaptive strategy via reinforcement learning for the prisonerʼs dilemma game. IEEE/CAA Journal of Automatica Sinica, 5(1), 301-310. [Google Scholar]
  46. Yuan, Y., Guo, T., Zhao, P., & Jiang, H. (2022). Adherence Improves Cooperation in Sequential Social Dilemmas. Applied Sciences, 12(16), 8004. [CrossRef] [Google Scholar]
  47. Zeng, W., & Li, M. (2020). Selective attention to historical comparison or social comparison in the evolutionary iterated prisoner’s dilemma game. Artificial Intelligence Review, 53, 6043-6078. [CrossRef] [Google Scholar]
  48. Zeng, W., Li, M., & Feng, N. (2017). The effects of heterogeneous interaction and risk attitude adaptation on the evolution of cooperation. Journal of Evolutionary Economics, 27, 435-459. [CrossRef] [Google Scholar]

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