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Analyzing Power in Weighted Voting Games with Super-Increasing Weights

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9928))

Abstract

Weighted voting games (WVGs) are a class of cooperative games that capture settings of group decision making in various domains, such as parliaments or committees. Earlier work has revealed that the effective decision making power, or influence of agents in WVGs is not necessarily proportional to their weight. This gave rise to measures of influence for WVGs. However, recent work in the algorithmic game theory community have shown that computing agent voting power is computationally intractable. In an effort to characterize WVG instances for which polynomial-time computation of voting power is possible, several classes of WVGs have been proposed and analyzed in the literature. One of the most prominent of these are super increasing weight sequences. Recent papers show that when agent weights are super-increasing, it is possible to compute the agents’ voting power (as measured by the Shapley value) in polynomial-time. We provide the first set of explicit closed-form formulas for the Shapley value for super-increasing sequences. We bound the effects of changes to the quota, and relate the behavior of voting power to a novel function. This set of results constitutes a complete characterization of the Shapley value in weighted voting games, and answers a number of open questions presented in previous work.

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Notes

  1. 1.

    Our definition actually results in super-decreasing weight sequences; for consistent notation with [2, 4] and others, we refer to our sequences as super-increasing.

References

  1. Matsui, Y., Matsui, T.: \(\mathit{NP}\)-completeness for calculating power indices of weighted majority games. Theor. Comput. Sci. 263(1–2), 305–310 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  2. Aziz, H., Paterson, M.: Computing voting power in easy weighted voting games. CoRR abs/0811.2497 (2008)

    Google Scholar 

  3. Chakravarty, N., Goel, A., Sastry, T.: Easy weighted majority games. Math. Soc. Sci. 40(2), 227–235 (2000)

    Article  MathSciNet  MATH  Google Scholar 

  4. Zuckerman, M., Faliszewski, P., Bachrach, Y., Elkind, E.: Manipulating the quota in weighted voting games. Artif. Intell. 180–181, 1–19 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  5. Shapley, L.: A value for \(n\)-person games. In: Contributions to the Theory of Games, vol. 2. Annals of Mathematics Studies, vol. 28, pp. 307–317. Princeton University Press, Princeton (1953)

    Google Scholar 

  6. Shapley, L., Shubik, M.: A method for evaluating the distribution of power in a committee system. Am. Polit. Sci. Rev. 48(3), 787–792 (1954)

    Article  Google Scholar 

  7. Chalkiadakis, G., Elkind, E., Wooldridge, M.: Computational Aspects of Cooperative Game Theory. Morgan and Claypool (2011)

    Google Scholar 

  8. Chalkiadakis, G., Wooldridge, M.: Weighted voting games. In: Brandt, F., Conitzer, V., Endriss, U., Lang, J., Procaccia, A. (eds.) Handbook of Computational Social Choice. Cambridge University Press (2016)

    Google Scholar 

  9. Elkind, E., Goldberg, L., Goldberg, P., Wooldridge, M.: Computational complexity of weighted threshold games. In: Proceedings of the 22nd AAAI Conference on Artificial Intelligence (AAAI 2007), pp. 718–723 (2007)

    Google Scholar 

  10. See, A., Bachrach, Y., Kohli, P.: The cost of principles: analyzing power in compatibility weighted voting games. In: AAMAS (2014)

    Google Scholar 

  11. Bachrach, Y., Markakis, E., Resnick, E., Procaccia, A., Rosenschein, J., Saberi, A.: Approximating power indices: theoretical and empirical analysis. Auton. Agent. Multi-Agent Syst. 20(2), 105–122 (2010)

    Article  Google Scholar 

  12. Fatima, S., Wooldridge, M., Jennings, N.: An approximation method for power indices for voting games. In: Proceedings of the 2nd International Workshop on Agent-Based Complex Automated Negotiations (ACAN 2009), pp. 72–86 (2009)

    Google Scholar 

  13. Maleki, S., Tran-Thanh, L., Hines, G., Rahwan, T., Rogers, A.: Bounding the estimation error of sampling-based shapley value approximation with/without stratifying. CoRR abs/1306.4265 (2013)

    Google Scholar 

  14. Deng, X., Papadimitriou, C.: On the complexity of cooperative solution concepts. Math. Oper. Res. 19(2), 257–266 (1994)

    Article  MathSciNet  MATH  Google Scholar 

  15. Littlechild, S.C., Owen, G.: A simple expression for the shapely value in a special case. Manage. Sci. 20(3), 370–372 (1973)

    Article  MATH  Google Scholar 

  16. Szczepański, P., Michalak, T., Rahwan, T.: Efficient algorithms for game-theoretic betweenness centrality. Artif. Intell. 231, 39–63 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  17. Bachrach, Y., Parkes, D.C., Rosenschein, J.S.: Computing cooperative solution concepts in coalitional skill games. Artif. Intell. 204, 1–21 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  18. Blocq, G., Bachrach, Y., Key, P.: The shared assignment game and applications to pricing in cloud computing. In: AAMAS (2014)

    Google Scholar 

  19. Bachrach, Y.: Honor among thieves: collusion in multi-unit auctions. In: AAMAS (2010)

    Google Scholar 

  20. Bachrach, Y., Lev, O., Lovett, S., Rosenschein, J.S., Zadimoghaddam, M.: Cooperative weakest link games. In: AAMAS (2014)

    Google Scholar 

  21. Bachrach, Y., Graepel, T., Kasneci, G., Kosinski, M., Van Gael, J.: Crowd IQ: aggregating opinions to boost performance. In: AAMAS (2012)

    Google Scholar 

  22. Elkind, E., Pasechnik, D., Zick, Y.: Dynamic weighted voting games. In: Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems, International Foundation for Autonomous Agents and Multiagent Systems, pp. 515–522 (2013)

    Google Scholar 

  23. Bachrach, Y., Kohli, P., Graepel, T.: Rip-off: playing the cooperative negotiation game. In: AAMAS, pp. 1179–1180 (2011)

    Google Scholar 

  24. Bachrach, Y., Elkind, E., Faliszewski, P.: Coalitional voting manipulation: a game-theoretic perspective (2011)

    Google Scholar 

  25. Bachrach, Y., Zuckerman, M., Wooldridge, M., Rosenschein, J.S.: Proof systems and transformation games. Ann. Math. Artif. Intell. 67(1), 1–30 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  26. Bachrach, Y., Porat, E.P., Rosenschein, J.S.: Sharing rewards in cooperative connectivity games. J. Artif. Intell. Res. 47, 281–311 (2013)

    MathSciNet  MATH  Google Scholar 

  27. Bachrach, Y., Meir, R., Feldman, M., Tennenholtz, M.: Solving cooperative reliability games. In: UAI (2012)

    Google Scholar 

  28. Bachrach, Y., Shah, N.: Reliability weighted voting games. In: Vöcking, B. (ed.) SAGT 2013. LNCS, vol. 8146, pp. 38–49. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  29. Zick, Y.: On random quotas and proportional representation in weighted voting Games. IJCAI 13, 432–438 (2013)

    Google Scholar 

  30. Bachrach, Y., Kash, I., Shah, N.: Agent failures in totally balanced games and convex games. In: Goldberg, P.W. (ed.) WINE 2012. LNCS, vol. 7695, pp. 15–29. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  31. Bachrach, Y., Savani, R., Shah, N.: Cooperative max games and agent failures. In: AAMAS (2014)

    Google Scholar 

  32. Aziz, H., Bachrach, Y., Elkind, E., Paterson, M.: False-name manipulations in weighted voting games. J. Artif. Intell. Res. 40, 57–93 (2011)

    MathSciNet  MATH  Google Scholar 

  33. Lasisi, R.O., Allan, V.H.: Manipulation of weighted voting games via annexation and merging. In: Filipe, J., Fred, A. (eds.) ICAART 2012. CCIS, vol. 358, pp. 364–378. Springer, Heidelberg (2013)

    Google Scholar 

  34. Lasisi, R., Allan, V.: New bounds on false-name manipulation in weighted voting games. In: Proceedings of the 27th International Florida Artificial Intelligence Research Society Conference (FLAIRS 2014), pp. 57–62 (2014)

    Google Scholar 

  35. Rey, A., Rothe, J.: False-name manipulation in weighted voting games is hard for probabilistic polynomial time. J. Artif. Intell. Res. 50, 573–601 (2014)

    MathSciNet  MATH  Google Scholar 

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Correspondence to Yoram Bachrach .

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Bachrach, Y., Filmus, Y., Oren, J., Zick, Y. (2016). Analyzing Power in Weighted Voting Games with Super-Increasing Weights. In: Gairing, M., Savani, R. (eds) Algorithmic Game Theory. SAGT 2016. Lecture Notes in Computer Science(), vol 9928. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-53354-3_14

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  • DOI: https://doi.org/10.1007/978-3-662-53354-3_14

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