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Computing equilibria for a service provider game with (Im)perfect information
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Source ACM Transactions on Algorithms (TALG) archive
Volume 2 ,  Issue 4  (October 2006) table of contents
Pages: 679 - 706  
Year of Publication: 2006
ISSN:1549-6325
Authors
Rene Beier  Max-Planck-Institut für Informatik, Saarbrücken
Artur Czumaj  University of Warwick, Coventry, United Kingdom
Piotr Krysta  Dortmund University, Dortmund, Germany
Berthold Vöcking  RWTH Aachen University, Aachen, Germany
Publisher
ACM  New York, NY, USA
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ABSTRACT

We study fundamental algorithmic questions concerning the complexity of market equilibria under perfect and imperfect information by means of a basic microeconomic game. Suppose a provider offers a service to a set of potential customers. Each customer has a particular demand of service and her behavior is determined by a utility function that is nonincreasing in the sum of demands that are served by the provider.Classical game theory assumes complete information: the provider has full knowledge of the behavior of all customers. We present a complete characterization of the complexity of computing optimal pricing strategies and of computing best/worst equilibria in this model. Basically, we show that most of these problems are inapproximable in the worst case but admit an FPAS in the average case. Our average case analysis covers large classes of distributions for customer utilities. We generalize our analysis to robust equilibria in which players change their strategies only when this promises a significant utility improvement.A more realistic model considers providers with incomplete information. Following the game theoretic framework of Bayesian games introduced by Harsanyi, the provider is aware of probability distributions describing the behavior of the customers and aims at estimating its expected revenue under best/worst equilibria. Somewhat counterintuitively, we obtain an FPRAS for the equilibria problem in the model with imperfect information although the problem with perfect information is inapproximable under the worst-case measures. In particular, the worst-case complexity of the considered problems increases with the precision of the available knowledge.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
1
Bierman, H. S., and Fernandez, L. 1998. Game Theory with Economic Applications. Addison-Wesley, Reading, MA.
2
 
3
 
4
Feldmann, R., Gairing, M., Lücking, T., Monien, B., and Rode, M. 2003. Nashification and the coordination ratio for a selfish routing game. Proceedings of the 30th Annual International Colloquium on Automata, Languages and Programming Springer-Verlag, Berlin, Germany, 514--526.
 
5
 
6
Harris, M., and Raviv, A. 1981. A theory of monopoly pricing schemes with demand uncertainty. Amer. Econ. Rev. 71, 3, 347--365.
 
7
Harsanyi, J. C. 1967--1968. Games with incomplete information played by Bayesian players, I, II, III. Management Science 14, 159--182, 320--332.
 
8
Harsanyi, J. C. 1973. Games with randomly disturbed payoffs. Int. J. Game Theory 2, 1--23.
 
9
Holzman, R., and Law-Yone, N. 1997. Strong equilibrium in congestion games. Games Econ. Behav. 21, 85--101.
10
 
11
Kellerer, H., Pferschy, U., and Pisinger, D. 2004. Knapsack Problems. Springer-Verlag, Berlin, Germany.
 
12
Klemperer, P. 1999. Auction theory: A guide to the literature. J. Econ. Surv. 13, 3, 227--286.
 
13
Kyle, A. S. 1989. Informed speculation with imperfect competition. Rev. Econ. Stud. 56, 3, 317--355.
 
14
Leland, H. E., and Meyer, A. 1976. Monopoly pricing structures with imperfect discrimination. Bell J. Econ. 7, 2, 449--462.
 
15
Manlove, D. 1998. Minimaximal and maximinimal optimization problems: A partial order-based approach. Ph.D. dissertation. University of Glasgow, Department of Computing Science. http://www.dcs.gla.ac.uk/~davidm/publications.html.
 
16
Maskin, E., and Riley, J. 1984. Monopoly with incomplete information. RAND J. Econ. 15, 2, 171--196.
 
17
McDiarmid, C. 1998. Concentration. In Probabilistic Methods for Algorithmic Discrete Mathematics, Algorithms and Combinatorics, M. Habib, C. McDiarmid, J. Ramirez-Alfonsin, and B. Reed, Eds. Springer-Verlag, Berlin, Germany, pp. 195--247.
 
18
Milchtaich, I. 1996. Congestion games with player-specific payoff function. Games Econ. Behav. 13, 111--124.
 
19
 
20
Monderer, D., and Shapley, L. S. 1996. Potential games. Games Econ. Behav. 14, 124--143.
 
21
Myerson, R. 1981. Optimal auction design. Math. Oper. Research 6, 58--73.
 
22
Oi, W. Y. 1971. A Disneyland dilemma: Two part tariffs for a Mickey Mouse monopoly. Quart. J. Econ. 85, 1, 77--96.
 
23
Owen, G. 1995. Game Theory. Academic Press, New York.
24
 
25
 
26
Rosenthal, R. W. 1973. A class of games possessing pure-strategy Nash equilibria. Int. J. Game Theory 2, 65--67.
 
27
Schmalensee, R. 1981. Monopolistic two-part pricing arrangements. Bell J. Econ. 12, 2, 445--466.
 
28
Scotchmer, S. 1985. Two-tier pricing of shared facilities in a free-entry equilibrium. RAND J. Econ. 16, 456--472.
 
29
Staiger, R. W., and Wolak, F. A. 1992. Collusive pricing with capacity constraints in the presence of demand uncertainty. RAND J. Econ. 23, 2, 203--220.
 
30
Ui, T. 2000. A Shapley value representation of potential games. Games Econ. Behav. 31, 121--135.

Collaborative Colleagues:
Rene Beier: colleagues
Artur Czumaj: colleagues
Piotr Krysta: colleagues
Berthold Vöcking: colleagues