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A Family of Supermodular Nash Mechanisms Implementing Lindahl Allocations

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A Family of Supermodular Nash Mechanisms Implementing Lindahl Allocations Economic Theory 19, 773–790 (2002) A family of supermodular Nash mechanisms implementing Lindahl allocations Yan Chen Department of Economics, University of Michigan, Ann Arbor, MI 48109-1220, USA (e-mail: [email protected]) Received: April 27, 2000; revised version: January 16, 2001 Summary. We present a family of mechanisms which implement Lindahl al- locations in Nash equilibrium. With quasilinear utility functions this family of mechanisms are supermodular games, which implies that they converge to Nash equilibrium under a wide class of learning dynamics. Keywords and Phrases: Public goods mechanisms, Supermodular games. JEL Classification Numbers: H41, C62, D83. 1 Introduction The presence of public goods seriously challenges traditional or “natural” solu- tions for the allocation of private goods. Important policy questions, of whether we can rely on the market to provide optimal amounts of public goods such as air pollution, and how much we can rely on “natural” processes such as voluntary contribution to solve environmental problems, boil down to fundamental issues about human nature, i.e., about whether people are selfish or cooperative. The vast experimental literature on voluntary provision of public goods shows that I thank John Ledyard, David Roth and Tatsuyoshi Saijo for discussions that lead to this project; Klaus Abbink, Beth Allen, Rachel Croson, Roger Gordon, Elisabeth Hoffman, Wolfgang Lorenzon, Laura Razzolini, Sara Solnick, Tayfun Sonmez,¨ William Thomson, Lise Vesterlund, seminar partici- pants in Bonn (EDP), Hamburg, Michigan, Minnesota, Pittsburgh, Purdue and participants of the 1997 North America Econometric Society Summer Meetings (Pasadena, CA), the 1997 Economic Science Association meetings (Tucson, AZ), the 1998 Midwest Economic Theory meetings (Ann Arbor, MI) and the 1999 NBER Decentralization Conference (New York, NY) for their comments and sugges- tions. Special thanks go to Matthew Jackson and an anonymous referee for pointing out an error in the proof of Theorem 2 in a previous version. The hospitality of the Wirtschaftspolitische Abteilung at the University of Bonn, the research support provided by Deutsche Forschungsgemeinschaft through SFB303 at the University of Bonn and NSF grant SBR-9805586 are gratefully acknowledged. 774 Y. Chen “although inexperienced subjects can be led to provide large contributions in one-time decisions with the use of relevant discussions, one cannot rely on these approaches as a permanent organizing feature without expecting an eventual decline to self-interested behavior. ... Since 90 percent of subjects seem to be responsive to private incentives, it is possible to create new mechanisms which focus that self-interest toward the group interest.” (p. 173, “Public Goods: A Survey of Experimental Research” in The Handbook of Experimental Economics, 1995.) These “new mechanisms”, i.e., incentive-compatible mechanisms for public goods provision, are innovative allocation-taxation rules which can achieve a Pareto-optimal allocation of resources with public goods. It is well known that it is impossible to design a mechanism for making collective allocation deci- sions, which is informationally decentralized (i.e., mechanisms which only use the agents’ own messages), non-manipulable (or dominant strategy incentive- compatible), and Pareto optimal1. There are many mechanisms which preserve Pareto optimality at the cost of non-manipulability, some of which preserve “some degree” of non-manipulability. In particular, some mechanisms have been discovered which have the property that Nash equilibria are Pareto optimal2. These can be found in the work of Groves and Ledyard (1977), Hurwicz (1979), Walker (1981), Tian (1989), Kim (1993), Peleg (1996) and Falkinger (1996). Nash implementation theory has mainly focused on establishing static prop- erties of the equilibria. When a mechanism is implemented among real people, i.e., boundedly rational agents, however, we expect the actual implementation to be a dynamic process, starting somewhere off the equilibrium path. Follow- ing Hurwicz (1972), one could interpret the Nash equilibrium strategies of a game form as the stationary messages of some decentralized learning process. The fundamental question concerning implementation of a specific mechanism is whether the dynamic processes will actually converge to one of the equilibria promised by theory. The few theoretical papers on the dynamic properties of public goods mecha- nisms have been using very specific learning dynamics to investigate the stability of mechanisms. Muench and Walker (1983) and de Trenqualye (1988) study the convergence of the Groves-Ledyard mechanism under Cournot best-reply dy- namics. De Trenqualye (1989) and Vega-Redondo (1989) propose mechanisms for which the Cournot best-reply dynamics is globally convergent to the Lindahl equilibrium3 outcome. Kim (1993) proposes a mechanism which implements Lindahl allocations and remains stable under the gradient adjustment process given quasilinear utility functions. One exception is Cabrales (1999) who studies 1 This impossibility has been demonstrated in the work of Green and Laffont (1977), Hurwicz (1975), Roberts (1979) and Walker (1980) in the context of resource allocation with public goods. 2 Other implementation concepts include perfect Nash equilibrium (Bagnoli and Lipman, 1989), undominated Nash equilibrium (Jackson and Moulin, 1991), etc. 3 A Lindahl equilibrium for the public goods economy is characterized by a set of personal- ized prices and an allocation such that utility and profit maximization and feasibility conditions are satisfied. A family of supermodular Nash mechanisms implementing Lindahl allocations 775 dynamic convergence and stability of the canonical mechanism in Nash imple- mentation and the Abreu-Matsushima mechanism (1992) under “naive adaptive dynamics”4. Recent experimental studies on learning strongly reject the Cournot best-reply learning model in favor of other models (e.g., Boylan and El-Gamal, 1993). So far there has been no experimental investigation of the gradient adjustment process, even though it has been used fairly extensively in the theoretical research on stability of games (Arrow and Hurwicz, 1977). Experimental research on learning is still far from reaching a conclusion with regard to a single “true” learning model that describes all adaptive behaviors. Furthermore, there is strong evidence that individual players adopt different learning rules under different circumstances (El-Gamal and Grether, 1995). It is therefore desirable to identify mechanisms which converge under a wide class of learning dynamics. This paper does so by focusing on mechanisms which are supermodular games. The class of supermodular games5 has been identified as having very robust dynamic stability properties (Milgrom and Roberts, 1990): it converges to the set of Nash equilibria that bound the serially undominated set under a wide class of interesting learning dynamics, including Bayesian learning, fictitious play, adaptive learning, Cournot best response and many others. Therefore, instead of using a specific learning dynamic, we investigate whether we can find Nash- efficient public goods mechanisms which are supermodular games. The idea of using supermodularity as a robust stability criterion for Nash- efficient mechanisms is not only based on its good theoretical properties, but also on strong experimental evidence. Chen (forthcoming) examines all experiments on incentive-compatible mechanisms for public goods. She finds that every ex- periment which converge to the Nash equilibrium prediction is a supermodular game, while none of the experiments which does not converge is a supermodular game. She proves that among the Nash-efficient public goods mechanisms the Groves-Ledyard mechanism is a supermodular game in quasilinear environments when the punishment parameter is above a certain threshold, while none of the Hurwicz (1979), Walker (1981) and Kim (1993) mechanisms is supermodular. These results are consistent with the experimental findings. The question remains whether we can find a Nash mechanism, which implements Lindahl allocation in a general environment and has a robust stability property (i.e., supermodular) in quasilinear environments. In this paper, we propose a new family of mechanisms, which implement Lindahl allocations in Nash equilibrium in a general environment and are also supermodular games given quasilinear utility functions. Section 2 introduces the environment. Section 3 discusses supermodular games. Section 4 introduces the new family of mechanisms and proves the im- plementation and stability results. Section 5 concludes the paper. 4 This is different from the adaptive learning in Milgrom and Roberts (1990). For a precise definition see Cabrales (1999). 5 See Section 3 for a formal definition. 776 Y. Chen 2 A general public goods environment We will assume that there is one private good x, one public good y, and n ≥ 3 players, indexed by subscript i. Our results can be generalized to any number of public goods. Production technology for the public good exhibits constant returns to scale, i.e., the production function f (·) is given by y = f (x)=x/b for some b > 0. The assumption of constant returns to scale is made to simplify the production side of the story. Theorem 2 still holds under a general convex pro- duction function, however, the stability results (Proposition 1) might also depend on the parameters of the production function. Each player is characterized by a R2
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