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Project Games

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Algorithms and Complexity (CIAC 2019)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11485))

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Abstract

We consider a strategic game called project game where each agent has to choose a project among his own list of available projects. The model includes positive weights expressing the capacity of a given agent to contribute to a given project. The realization of a project produces some reward that has to be allocated to the agents. The reward of a realized project is fully allocated to its contributors, according to a simple proportional rule. Existence and computational complexity of pure Nash equilibria is addressed and their efficiency is investigated according to both the utilitarian and the egalitarian social function.

Supported by ANR Project CoCoRICo-CoDec.

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References

  1. Koutsoupias, E., Papadimitriou, C.H.: Worst-case equilibria. In: Meinel, C., Tison, S. (eds.) STACS 1999. LNCS, vol. 1563, pp. 404–413. Springer, Heidelberg (1999). https://doi.org/10.1007/3-540-49116-3_38

    Chapter  Google Scholar 

  2. Anshelevich, E., Dasgupta, A., Kleinberg, J.M., Tardos, É., Wexler, T., Roughgarden, T.: The price of stability for network design with fair cost allocation. In: Proceedings of the 45th Symposium on Foundations of Computer Science (FOCS), pp. 295–304 (2004)

    Google Scholar 

  3. Morris, S., Ui, T.: Best response equivalence. Games Econ. Behav. 49, 260–287 (2004)

    Article  MathSciNet  Google Scholar 

  4. Vetta, A.: Nash equilibria in competitive societies, with applications to facility location, traffic routing and auctions. In: Proceedings of the 43rd Symposium on Foundations of Computer Science (FOCS), pp. 416–425 (2002)

    Google Scholar 

  5. Augustine, J., Chen, N., Elkind, E., Fanelli, A., Gravin, N., Shiryaev, D.: Dynamics of profit-sharing games. Internet Math. 11, 1–22 (2015)

    Article  MathSciNet  Google Scholar 

  6. Bachrach, Y., Syrgkanis, V., Vojnović, M.: Incentives and efficiency in uncertain collaborative environments. In: Chen, Y., Immorlica, N. (eds.) WINE 2013. LNCS, vol. 8289, pp. 26–39. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-45046-4_4

    Chapter  MATH  Google Scholar 

  7. Gollapudi, S., Kollias, K., Panigrahi, D., Pliatsika, V.: Profit sharing and efficiency in utility games. In: Proceedings of the 25th Annual European Symposium on Algorithms (ESA), pp. 43:1–43:14 (2017)

    Google Scholar 

  8. Goemans, M.X., Li, L., Mirrokni, V.S., Thottan, M.: Market sharing games applied to content distribution in ad hoc networks. IEEE J. Sel. Areas Commun. 24, 1020–1033 (2006)

    Article  Google Scholar 

  9. Kleinberg, J.M., Oren, S.: Mechanisms for (mis)allocating scientific credit. In: Proceedings of the 43rd ACM Symposium on Theory of Computing (STOC), pp. 529–538 (2011)

    Google Scholar 

  10. Marden, J.R., Roughgarden, T.: Generalized efficiency bounds in distributed resource allocation. In: Proceedings of the 49th IEEE Conference on Decision and Control (CDC), pp. 2233–2238 (2010)

    Google Scholar 

  11. Marden, J.R., Wierman, A.: Distributed welfare games. Oper. Res. 61, 155–168 (2013)

    Article  MathSciNet  Google Scholar 

  12. Rosenthal, R.: A class of games possessing pure-strategy Nash equilibria. Int. J. Game Theory 2, 65–67 (1973)

    Article  MathSciNet  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  14. Ieong, S., McGrew, R., Nudelman, E., Shoham, Y., Sun, Q.: Fast and compact: a simple class of congestion games. In: Proceedings of the 20th National Conference on Artificial Intelligence (AAAI), pp. 489–494 (2005)

    Google Scholar 

  15. Milchtaich, I.: Congestion games with player-specific payoff functions. Games Econ. Behav. 13, 111–124 (1996)

    Article  MathSciNet  Google Scholar 

  16. Vöcking, B.: Selfish load balancing. In: Nisan, N., Roughgarden, T., Tardos, E., Vazirani, V.V. (eds.) Algorithmic Game Theory, pp. 517–542. Cambridge University Press, New York (2007)

    Chapter  Google Scholar 

  17. Drèze, J.H., Greenberg, J.: Hedonic coalitions: optimality and stability. Econometrica 48, 987–1003 (1980)

    Article  MathSciNet  Google Scholar 

  18. Darmann, A., Elkind, E., Kurz, S., Lang, J., Schauer, J., Woeginger, G.: Group activity selection problem. In: Goldberg, P.W. (ed.) WINE 2012. LNCS, vol. 7695, pp. 156–169. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-35311-6_12

    Chapter  Google Scholar 

  19. Fotakis, D., Kontogiannis, S., Koutsoupias, E., Mavronicolas, M., Spirakis, P.: The structure and complexity of Nash equilibria for a selfish routing game. In: Widmayer, P., Eidenbenz, S., Triguero, F., Morales, R., Conejo, R., Hennessy, M. (eds.) ICALP 2002. LNCS, vol. 2380, pp. 123–134. Springer, Heidelberg (2002). https://doi.org/10.1007/3-540-45465-9_12

    Chapter  MATH  Google Scholar 

  20. Gairing, M., Lucking, T., Mavronicolas, M., Monien, B.: Computing Nash equilibria for scheduling on restricted parallel links. In: Proceedings of the 36th Annual ACM Symposium on Theory of Computing (STOC), pp. 613–622 (2004)

    Google Scholar 

  21. Fabrikant, A., Papadimitriou, C.H., Talwar, K.: The complexity of pure Nash equilibria. In: Proceedings of the 36th Annual ACM Symposium on Theory of Computing (STOC), pp. 604–612 (2004)

    Google Scholar 

  22. Gairing, M., Monien, B., Tiemann, K.: Routing (un-)splittable flow in games with player-specific linear latency functions. In: Bugliesi, M., Preneel, B., Sassone, V., Wegener, I. (eds.) ICALP 2006. LNCS, vol. 4051, pp. 501–512. Springer, Heidelberg (2006). https://doi.org/10.1007/11786986_44

    Chapter  Google Scholar 

  23. Georgiou, C., Pavlides, T., Philippou, A.: Selfish routing in the presence of network uncertainty. Parallel Process. Lett. 19, 141–157 (2009)

    Article  MathSciNet  Google Scholar 

  24. Biló, V.: A unifying tool for bounding the quality of non-cooperative solutions in weighted congestion games. Theory Comput. Syst. 62, 1288–1317 (2018)

    Article  MathSciNet  Google Scholar 

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Correspondence to Laurent Gourvès .

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Bilò, V., Gourvès, L., Monnot, J. (2019). Project Games. In: Heggernes, P. (eds) Algorithms and Complexity. CIAC 2019. Lecture Notes in Computer Science(), vol 11485. Springer, Cham. https://doi.org/10.1007/978-3-030-17402-6_7

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  • DOI: https://doi.org/10.1007/978-3-030-17402-6_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-17401-9

  • Online ISBN: 978-3-030-17402-6

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