Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
184 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
45 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Probably approximately correct stability of allocations in uncertain coalitional games with private sampling (2312.08573v1)

Published 14 Dec 2023 in math.OC, cs.GT, cs.SY, and eess.SY

Abstract: We study coalitional games with exogenous uncertainty in the coalition value, in which each agent is allowed to have private samples of the uncertainty. As a consequence, the agents may have a different perception of stability of the grand coalition. In this context, we propose a novel methodology to study the out-of-sample coalitional rationality of allocations in the set of stable allocations (i.e., the core). Our analysis builds on the framework of probably approximately correct learning. Initially, we state a priori and a posteriori guarantees for the entire core. Furthermore, we provide a distributed algorithm to compute a compression set that determines the generalization properties of the a posteriori statements. We then refine our probabilistic robustness bounds by specialising the analysis to a single payoff allocation, taking, also in this case, both a priori and a posteriori approaches. Finally, we consider a relaxed $\zeta$-core to include nearby allocations and also address the case of empty core. For this case, probabilistic statements are given on the eventual stability of allocations in the $\zeta$-core.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (33)
  1. Learning cooperative games. In IJCAI15: Proceedings of the 24th International Conference on Artificial Intelligence, 2015.
  2. The scenario approach to robust control design. IEEE Transactions on Automatic Control, 51(5):742–753, 2006. 10.1109/TAC.2006.875041.
  3. Introduction to the scenario approach. Society for Industrial and Applied Mathematics, 2018a.
  4. Wait-and-judge scenario optimization. Mathematical Programming, 167:155–189, 2018b.
  5. A theory of the risk for optimization with relaxation and its application to support vector machines. Journal of Machine Learning Research, 22(288):1–38, 2021.
  6. A general scenario theory for nonconvex optimization and decision making. IEEE Transactions on Automatic Control, 63(12):4067–4078, 2018. 10.1109/TAC.2018.2808446.
  7. Bayesian reinforcement learning for coalition formation under uncertainty. In AAMAS 2004: Proceedings of the 3rd International Joint Conference on Autonomous Agents and Multiagent Systems., pages 1090–1097, 2004.
  8. Sequential decision making in repeated coalition formation under uncertainty. In AAMAS08: Proceedings of the 7th International Joint Conference on Autonomous Agents and Multiagent Systems, 2008.
  9. Computational aspects of cooperative game theory. Synthesis Lectures on Artificial Intelligence and Machine Learning, 5(6):1–168, 2011.
  10. Prior solutions: Extensions of convex nucleus solutions to chance-constrained games. In Proceedings of the Computer Science and Statistics Seventh Symposium at Iowa State University, pages 323–332, 1973.
  11. Coalitional and chance-constrained solutions to n-person games I: The prior satisficing nucleolus. SIAM Journal on Applied Mathematics, 31(2):358–367, 1976.
  12. Coalitional and chance-constrained solutions to n-person games II: Two-stage solutions. Operations Research, 25(6):1013–1019, 1977.
  13. Probabilistic feasibility guarantees for solution sets to uncertain variational inequalities. Automatica, 137:110120, 2022. ISSN 0005-1098.
  14. A scenario-based approach to multi-agent optimization with distributed information. IFAC-PapersOnLine, 53(2):20–25, 2020. ISSN 2405-8963. 10.1016/j.ifacol.2020.12.034. 21st IFAC World Congress.
  15. Coalitional control: Cooperative game theory and control. IEEE Control Systems Magazine, 37(1):53–69, 2017.
  16. Coalitional control for self-organizing agents. IEEE Transactions on Automatic Control, 63(9):2883–2897, 2018. 10.1109/TAC.2018.2792301.
  17. Risk and complexity in scenario optimization. Mathematical Programming, 191:243–279, 2022.
  18. Are exploration-based strategies of interest for repeated stochastic coalitional games? Advances in Practical Applications of Agents, Multi-Agent Systems, and Social Good. The PAAMS Collection, pages 89–100, 2021.
  19. Incentivizing prosumer coalitions with energy management using cooperative game theory. IEEE Transactions on Power Systems, 34(1):303–313, 2019.
  20. Bayesian coalitional games. In Proceedings of the 23rd AAAI Conference on Artificial Intelligence, pages 95–100, 2008.
  21. Core-selecting mechanisms in electricity markets. IEEE Transactions on Smart Grid, 11(3):2604–2614, 2020. 10.1109/TSG.2019.2958710.
  22. Cooperative game solution concepts that maximize stability under noise. In Proceedings of the 29th AAAI Conference on Artificial Intelligence, page 979–985, 2015.
  23. On the connection between compression learning and scenario-based single-stage and cascading optimization problems. IEEE Transactions on Automatic Control, 60(10):2716–2721, 2015.
  24. Distributed constrained optimization and consensus in uncertain networks via proximal minimization. IEEE Transactions on Automatic Control, 63(5):1372–1387, 2018. 10.1109/TAC.2017.2747505.
  25. Roger A. McCain. Cooperative games and cooperative organizations. The Journal of Socio-Economics, 37(6):2155–2167, 2008. ISSN 1053-5357.
  26. Dynamic coalitional tu games: Distributed bargaining among players’ neighbors. IEEE Transactions on Automatic Control, 58(6):1363–1376, 2013. 10.1109/TAC.2012.2236716.
  27. Probabilistically robust stabilizing allocations in uncertain coalitional games. IEEE Control Systems Letters, 6:3128–3133, 2022a. 10.1109/LCSYS.2022.3182152.
  28. On the probabilistic feasibility of solutions in multi-agent optimization problems under uncertainty. European Journal of Control, 63:186–195, 2022b. ISSN 0947-3580.
  29. Distributionally robust stability of payoff allocations in stochastic coalitional games. Accepted for publication at the IEEE Conference on Decision and Control 2023, 2023.
  30. Learning to identify winning coalitions in the PAC model. In AAMAS06 - 5th International Joint Conference on Autonomous Agents and Multi-agent Systems, page 673–675. Association for Computing Machinery, NY, US, 2006. 10.1145/1160633.1160751.
  31. Payoff distribution in robust coalitional games on time-varying networks. IEEE Transactions on Control of Network Systems, 2021.
  32. Randomized solutions to convex programs with multiple chance constraints. SIAM J. Optim., 23:2479–2501, 2012.
  33. Cooperative games with stochastic payoffs. European Journal of Operational Research, 113(1):193–205, 1999. ISSN 0377-2217. 10.1016/S0377-2217(97)00421-9.

Summary

We haven't generated a summary for this paper yet.