Large Population Aggregative Potential Games∗

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Large Population Aggregative Potential Games∗ Large Population Aggregative Potential Games∗ Ratul Lahkary April 27, 2016 Abstract We consider population games in which payoff depends upon the aggregate strategy level and which admit a potential function. Examples of such aggregative potential games include the tragedy of the commons and the Cournot competition model. These games are technically simple as they can be analyzed using a one{dimensional variant of the potential function. We use such games to model the presence of externalities, both positive and negative. We characterize Nash equilibria in such games as socially inefficient. Evolutionary dynamics in such games converge to socially inefficient Nash equilibria. Keywords: Externalities, Aggregative Games, Potential Games, Evolutionary Dynamics. JEL classification: C72; C73; D62. ∗I thank an anonymous associate editor and two anonymous referees for various comments and suggestions. Any remaining error or omission is my responsibility. ySchool of Economics, Ashoka University, Rajiv Gandhi Education City, Kundli, Haryana, 131 028, India. email: [email protected]. 1 1 Introduction Potential games are games in which information about players' payoffs can be summarized using a real valued function. Monderer and Shapley (1996) define and analyze the fundamental properties of a normal form potential game. Sandholm (2001, 2009) extends the notion of a potential game to population games. In the context of a population game, a potential game is one in which payoffs are equal to the gradient of a real valued function called the potential function. Potential games are of interest in evolutionary game theory because a variety of evolutionary dynamics converge to Nash equilibria in such games.1 In this paper, we consider a particularly simple class of potential games which we call aggregative potential games. These are potential games which belong to the class of aggregative population games, i.e. population games in which payoffs depend upon the strategy used, and the aggregate strategy level at a population state. This notion of an aggregative population game is an extension of the original concept introduced by Corch´on(1994) and elaborated further by, for example, Acemoglu and Jensen (2013), in the context of finite player games. We describe aggregative potential games as \simple" because, as we show here, they can be analyzed using a one{dimensional analogue of the potential function, which we call the quasi{ potential function. In general, Nash equilibria of a potential game are related to the maximizers of its potential function (Sandholm, 2001). For aggregative potential games, the maximizers of the potential function are well approximated by the maximizers of the quasi{potential function, partic- ularly if the strategy set of the underlying game is sufficiently dense. Since finding the maximizers of the quasi{potential function is a trivial task in comparison to computing the maximizers of the potential function, identifying Nash equilibria in aggregative potential games becomes particularly easy. In addition to their technical simplicity, aggregative potential games are of interest because they include important economic models like the tragedy of the commons, the model of Cournot competition and models of search. Further, aggregative potential games provide a parsimonious framework to model both negative and positive externalities in the context of population games. This allows us to use existing results on evolution in potential games to explain how, in the presence of externalities, societies may converge to inefficient aggregate economic outcomes. We consider aggregative potential games with a finite set of positive strategies. We analyze two types of such games|one with negative externalities and the other with positive externalities. Our results on games with negative externalities are particularly interesting. In this case, under reasonable assumptions, the equilibrium aggregate strategy level is uniquely defined. This equilib- rium aggregate strategy level is either exactly characterized by or well{approximated by the unique maximizer of the quasi{potential function. Games with positive externalities do not have unique equilibrium aggregate strategy levels. Nor is their characterization as elegant as in the case of neg- ative externalities. Nevertheless, with some relatively strong assumptions on the quasi{potential 1See Sandholm (2010) for a review of such results. 1 function, we are able to relate the equilibrium aggregate strategy levels under positive externalities to the maximizers of the quasi{potential function. Externalities imply a difference between Nash equilibria and the socially efficient state, i.e. the state that maximizes aggregate payoff in the population. For aggregative potential games, we estab- lish this distinction by using a technique similar to characterizing Nash equilibria in these games. We construct a one-dimensional analogue to the aggregate payoff and show that the maximizer of this function corresponds to the socially efficient state, either exactly or with a high degree of approximation. We then show that under negative externalities, the unique equilibrium aggregate social state is higher than the socially efficient aggregate state. Under positive externalities, aggre- gate states at equilibria are lower than the socially efficient state. Such conclusions are, of course, consistent with standard microeconomic theory. The novelty lies in the technique through which we arrive at these conclusions. Use of the one{dimensional analogues of the potential function and the aggregate payoff function makes it particularly easy to relate Nash equilibria and efficient states in aggregative potential games. We can then apply standard results on evolution in potential games (Sandholm, 2001) to con- clude that the social state in aggregative potential games converge to a state that is different from the socially efficient state. Deterministic evolutionary dynamics like the replicator dynamic, the logit dynamic, the BNN dynamic and the Smith dynamic converge either to Nash equilibria or to a perturbed version of Nash equilibria in potential games from all or almost all initial states. In aggregative potential games, therefore, all such dynamics converge to an aggregate social state that differs from the socially efficient aggregate state. From a broader perspective, the evolutionary analysis of aggregative potential games allows us to appreciate how inefficient aggregate behavior becomes prevalent in societies. If we regard the revision protocols that generate the dynamics commonly studied in evolutionary game theory as reasonable descriptions of human behavior, then our analysis helps explain why we should expect social inefficiency under a wide variety of behavioral norms and from a wide range of initial conditions. There are precedents to the evolutionary analysis of the models considered here. For example, Vega{Redondo (1997) and Al´os{Ferrer and Ania (2005) consider an imitative model of Cournot competition with a finite population of players. Their main result is that the evolutionarily stable strategy (ESS) in the finite population model is not the Nash equilibrium of the model, but the Walrasian or competitive equilibrium. The key reason why Nash equilibria are not finite population ESS is that a strategy change by single player affects a finite population state. Hence, when a player playing, say, strategy i observes the payoff of strategy j he is seeking to imitate, he is not observing the payoff that he would actually obtain when he changes the strategy to j. This is because when the i−player shifts to j, the number of j−players increases by one, thereby changing the payoff to strategy j. The failure to appreciate this distinction may make it advantageous to imitate a mutant and change strategy at a Nash equilibrium in a finite population Cournot model. Al´os{Ferrer and Ania (2005) also extend this result on finite population ESS to other models like the tragedy of the 2 commons and the search model that we have considered here. Schaffer (1988) notes an important implication of the finite population ESS. It is as if players seek to maximize not absolute payoffs but relative payoffs. Thus, deviation from a Nash equilibrium becomes worthwhile because even though it may reduce one's absolute payoff, it can reduce oppo- nents' payoff even more, thereby increasing relative payoff. Another significant model that explores the distinction between Nash equilibria and finite population ESS under imitative learning due to the concern with relative payoff is Bergin and Bernhardt (2004). In their model, this distinction does not arise if agents' learn from one's own experience instead of imitating the experience of others. Unlike the models discussed in the previous two paragraphs, in our paper, only Nash equilib- ria can be evolutionarily stable. This is because in an infinite population model such as ours, a strategy change by a single agent has no effect on the population state and on payoffs. Hence, the difference between the payoff observed before a strategy change and the payoff obtained after the strategy change cannot arise in this context. In terms of Schaffer’s (1988) interpretation, concern with relative payoffs is irrelevant and so, deviation from a Nash equilibrium is never worthwhile. We should note, however, a stable Nash equilibrium in our model coincides with the finite popu- lation ESS identified in these other models. Thus, in our Cournot model, Nash equilibria are also Walrasian equilibria. Intuitively, this is because with an infinite
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