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International Conference on Principles and Practice of Constraint Programming

CP 2018: Principles and Practice of Constraint Programming pp 707–724Cite as

Distributed Constrained Search by Selfish Agents for Efficient Equilibria

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Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 11008))

Abstract

Search for stable solutions in games is a hard problem that includes two families of constraints. The global stability constraint and multiple soft constraints that express preferences for socially, or otherwise, preferred solutions. To find stable solutions (e.g., pure Nash equilibria - PNEs) of high efficiency, the multiple agents of the game perform a distributed search on an asymmetric distributed constraints optimization problem (ADCOP). Approximate (local) distributed search on ADCOPs does not necessarily guarantee convergence to an outcome that satisfies the stability constraints, as well as optimizes the soft constraints. The present paper proposes a distributed search algorithm that uses transfer of funds among selfish agents. The final outcome of the algorithm can be stabilized by transfer of funds among the agents, where the transfer function is contracted among the agents during search. It is shown that the proposed algorithm - Iterative Nash Efficiency enhancement Algorithm (INEA) - guarantees improved efficiency for any initial outcome.

The proposed distributed search algorithm can be looked at as an extension to best response dynamics, that uses transfer functions to guarantee convergence and enforce stability in games. The best-response-like nature of INEA establishes its correct behavior for selfish agents in a multi-agents game environment. Most important, unlike best response, the proposed INEA converges to efficient and stable outcomes even in games that are not potential games.

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References

  1. Osborne, M., Rubinstein, A.: A Course in Game Theory. The MIT Press, London (1994)

    MATH  Google Scholar 

  2. DallAsta, L., Pin, P., Ramezanpour, A.: Optimal equilibria of the best shot game. J. Public Econ. Theory 13(6), 885–901 (2011)

    Article  Google Scholar 

  3. Nisan, N., Roughgarden, T., Tardos, E., Vazirani, V.V.: Algorithmic Game Theory. Cambridge University Press, Cambridge (2007)

    Book  Google Scholar 

  4. Nguyen, T.-V.-A., Lallouet, A.: A complete solver for constraint games. In: O’Sullivan, B. (ed.) CP 2014. LNCS, vol. 8656, pp. 58–74. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10428-7_8

    Chapter  Google Scholar 

  5. Palmieri, A., Lallouet, A.: Constraint games revisited. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI 2017), Melbourne, AU, pp. 729–735 (2017)

    Google Scholar 

  6. Roughgarden, T.: Selfish Routing and the Price of Anarchy. The MIT Press, Cambridge (2005)

    MATH  Google Scholar 

  7. Monderer, D., Tennenholtz, M.: Strong mediated equilibrium. Artif. Intell. 173(1), 180–195 (2009)

    Article  MathSciNet  Google Scholar 

  8. Grubshtein, A., Meisels, A.: Finding a nash equilibrium by asynchronous backtracking. In: Milano, M. (ed.) CP 2012. LNCS, pp. 925–940. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33558-7_66

    Chapter  Google Scholar 

  9. Litov, O., Meisels, A.: Distributed search for pure nash equilibria in graphical games. In: Proceedings of the 2016 International Conference on Autonomous Agents & Multiagent Systems (AAMAS-2016), pp. 1279–1280 (2016)

    Google Scholar 

  10. Wahbi, M., Brown, K.N.: A distributed asynchronous solver for nash equilibria in hypergraphical games. In: ECAI, pp. 1291–1299 (2016)

    Google Scholar 

  11. Grinshpoun, T., Grubshtein, A., Zivan, R., Netzer, A., Meisels, A.: Asymmetric distributed constraint optimization problems. JAIR 47, 613–647 (2013)

    Article  MathSciNet  Google Scholar 

  12. Monderer, D., Shapley, L.S.: Potential games. Games Econ. Behav. 14, 124–143 (1996)

    Article  MathSciNet  Google Scholar 

  13. Jackson, M.O., Wilkie, S.: Endogenous games and mechanisms: side payments among players. Rev. Econ. Stud. 72(2), 543–566 (2005)

    Article  MathSciNet  Google Scholar 

  14. Kearns, M.J.: Graphical games. In: Vazirani, V.V., Nisan, N., Roughgarden, T., Tardos, É. (eds.) Algorithmic Game Theory, pp. 159–178. Cambridge University Press (2007)

    Google Scholar 

  15. Jackson, M.O.: Social and Economic Networks, vol. 3. Princeton University Press, Princeton (2008)

    MATH  Google Scholar 

  16. Levit, V., Komarovsky, Z., Grinshpoun, T., Meisels, A.: Tradeoffs between incentive mechanisms in Boolean games. IJCA I, 68–74 (2015)

    Google Scholar 

  17. Jackson, M.O., Zenou, Y.: Games on networks. In: Young, P., Zamir, S. (eds.) Handbook of Game Theory , vol. 4, pp. 102–191 (2014)

    Google Scholar 

  18. Komarovsky, Z., Levit, V., Grinshpoun, T., Meisels, A.: Efficient equilibria in a public goods game. In: WI-IAT, pp. 214–219 (2015)

    Google Scholar 

  19. Grubshtein, A., Meisels, A.: A distributed cooperative approach for optimizing a family of network games. In: Brazier, F.M.T., Nieuwenhuis, K., Pavlin, G., Warnier, M., Badica, C. (eds.) IDC 2011. Studies in Computational Intelligence, vol. 382, pp. 49–62. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-24013-3_6

    Chapter  Google Scholar 

  20. Grubshtein, A., Zivan, R., Grinshpoun, T., Meisels, A.: Local search for distributed asymmetric optimization. In: AAMAS, pp. 1015–1022 (2010)

    Google Scholar 

  21. Zivan, R., Okamoto, S., Peled, H.: Explorative anytime local search for distributed constraint optimization. Artif. Intell. 212, 1–26 (2014)

    Article  MathSciNet  Google Scholar 

  22. Zivan, R., Parash, T., Cohen, L., Peled, H., Okamoto, S.: Balancing exploration and exploitation in incomplete min/max-sum inference for distributed constraint optimization. Auton. Agents Multi Agent Syst. 31, 1165–1207 (2017)

    Article  Google Scholar 

  23. Shapley, L.: A value for n-person games. In: kunh, H.W., Tucker, A.W. (eds.) Contributions to the Theory of Games. Annals of Mathematical Studies, vol. 28, pp. 307–317 (1953)

    Google Scholar 

  24. Hirshleifer, J.: From weakest-link to best-shot: the voluntary provision of public goods. Public Choice 41(3), 371–386 (1983)

    Article  Google Scholar 

  25. Nisan, N., Schapira, M., Valiant, G., Zohar, A.: Best-response mechanisms. In: Innovations in Computer Science - ICS, Beijing, China, pp. 155–165 (2011)

    Google Scholar 

  26. Nisan, N., Schapira, M., Valiant, G., Zohar, A.: When is it best to best-respond? SIGecom Exch. 10, 16–18 (2011)

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science, Ben-Gurion University, Beersheba, Israel

    Vadim Levit & Amnon Meisels

Authors
  1. Vadim Levit
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  2. Amnon Meisels
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Corresponding author

Correspondence to Amnon Meisels .

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Editors and Affiliations

  1. Carnegie Mellon University, Pittsburgh, PA, USA

    John Hooker

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Levit, V., Meisels, A. (2018). Distributed Constrained Search by Selfish Agents for Efficient Equilibria. In: Hooker, J. (eds) Principles and Practice of Constraint Programming. CP 2018. Lecture Notes in Computer Science(), vol 11008. Springer, Cham. https://doi.org/10.1007/978-3-319-98334-9_45

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  • DOI: https://doi.org/10.1007/978-3-319-98334-9_45

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-98333-2

  • Online ISBN: 978-3-319-98334-9

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