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Near Rationality and Competitive Equilibria in Networked Systems Near Rationality and Competitive Equilibria in Networked Systems Nicolas Christin Jens Grossklags John Chuang [email protected] [email protected] [email protected] School of Information Management and Systems University of California, Berkeley 102 South Hall Berkeley, CA 94720-4600 ABSTRACT [9] rely on simple incentive mechanisms. More generally, as sum- A growing body of literature in networked systems research relies marized in [13, 26, 30], a number of recent research efforts have on game theory and mechanism design to model and address the been applying concepts from game theory and mechanism design potential lack of cooperation between self-interested users. Most to networked systems in an effort to align the incentives of each game-theoretic models applied to system research only describe (self-interested) user with the goal of maximizing the overall sys- competitive equilibria in terms of pure Nash equilibria, that is, a tem performance. situation where the strategy of each user is deterministic, and is her A cornerstone of game theory and mechanism design is the no- best response to the strategies of all the other users. However, the tion of competitive equilibrium, which is used to predict user be- assumptions necessary for a pure Nash equilibrium to hold may be havior and infer the outcome of a competitive game. As discussed too stringent for practical systems. Using three case studies on net- in [26], the concept of Nash equilibrium is predominantly used in work formation, computer security, and TCP congestion control, system research to characterize user behavior. Assuming each user we outline the limits of game-theoretic models relying on Nash obtains a utility dependent on the strategy she adopts, a Nash equi- equilibria, and we argue that considering competitive equilibria of librium is defined as a set of strategies from which no user willing a more general form helps in assessing the accuracy of a game the- to maximize her own utility has any incentive to deviate [25]. oretic model, and can even help in reconciling predictions from While Nash equilibria are a very powerful tool for predicting game-theoretic models with empirically observed behavior. outcomes in competitive environments, their application to system design generally relies on a few assumptions, notably, that (1) each participant is infallible (i.e., perfectly rational), and that (2) each Categories and Subject Descriptors user has perfect knowledge of the structure of the game, includ- C.2 [Computer Systems Organization]: Computer-Communication ing strategies available to every other participant and their asso- Networks ciated utilities. There seems to be a class of problems for which these assumptions may be too restrictive, for instance, characteriz- General Terms ing competitive equilibria in systems where participants have lim- ited knowledge of the state of the rest of the network. Design, Economics, Theory As a practical example of the potential limits of a game theoreti- cal analysis of a networked system solely based on Nash equilibria, Keywords one can argue that, in the case of a peer-to-peer file-sharing system Distributed systems, Game theory, Competitive equilibria, Model- that does not provide incentives for users to share, the unique Nash ing equilibrium leads to the “tragedy of the commons [18],” that is, a situation where users do not share anything to minimize the cost they incur, thereby leading the entire system to collapse. The mere 1. INTRODUCTION fact that, in practice, some users are sharing files, even in peer-to- Empirical evidence of phenomena such as free-riding in peer-to- peer systems that do not rely on incentive mechanisms, hints that a peer systems [1] or unfairness in ad-hoc networks [19] challenges Nash equilibrium is not actually reached. the traditional system design assumption that all users of a network The argument that Nash equilibria may be too restrictive to char- are able and willing to cooperate for the greater good of the commu- acterize networked systems is not entirely new. In [14], Friedman nity. Hence, system architects have become increasingly interested and Shenker notably argued that equilibria resulting from learn- in considering network participants as selfish [30] or competing ing behaviors were a tool better suited for characterizing how net- [29] entities. For instance, in an effort to discourage free-riding, work users share network resources than Nash equilibria. In this some deployed peer-to-peer systems such as KaZaA or BitTorrent paper, we take a quite different stand, by advocating to consider competitive equilibria of a slightly more general form than pure c ACM, 2004. This is the authors’ version of the work. It is posted here Nash equilibria. More precisely, we argue that using simple exten- by permission of ACM for your personal use. Not for redistribution. The sions of pure Nash equilibria (1) helps to assess the robustness of a conference version was published in the Proceedings of the ACM SIG- game-theoretic model to small deviations from expected behavior, COMM’04 Workshop on Practice and Theory of Incentives in Networked thereby providing insight in the accuracy of the model, and (2) may Systems (PINS), pages 213–219, Portland, Oregon, September 3 2004. help reconcile empirical observations with analytical modeling. We illustrate our point by presenting three case studies, on net- likely contain a small upward bias. If a substantial part of the other work formation, security, and TCP congestion control, where out- players shares this marginal bias the outcome of the auction can comes predicted by Nash equilibria are not entirely correlated by be surprisingly far away from a Nash prediction [15]. Similarly, empirical observations. In each case study, we investigate if and in a sealed-bid auction the Nash equilibrium outcome predicts that how more general forms of competitive equilibria can be used to a player with a lower valuation will only sometimes win the auc- better describe observed behavior, or if, on the other hand, the tioned good. However, this outcome is more likely if players share model needs to be refined. small imperfections in the execution of their Nash strategies [21]. The remainder of this paper is organized as follows. In Section 2, Such systematic and non-systematic deviations and their out- we provide some background by formally discussing the concept comes have been motivation to formulate more generalized models of Nash equilibrium and its extensions or potential alternatives. In of strategic behavior that include the notion of the Nash equilib- Section 3, we present our case studies. Finally, in Section 4, we rium as a special case. In particular, to relax the assumption of per- discuss our findings, outline a possible agenda for future research, fect rationality required by the concept of Nash equilibrium, some and draw conclusions from our observations. have introduced the concept of bounded rationality. Players that are bounded rational are not necessarily picking the best strategy 2. BACKGROUND available across the entire decision space, but instead are allowed We consider strategic interactions (called games) of the follow- to make small errors on a number of levels, such as the evaluation ing simple form: the individual decision-makers (also called play- of the payoffs associated with a strategy, the assessment of the best ers) of a game simultaneously choose actions that are derived from available strategy, or the execution of a specific strategy. their available strategies. The players will receive payoffs that de- Some techniques to model bounded rationality introduce (possi- pend on the combination of the actions chosen by each player. bly small) amounts of noise into the decision-making process. As an example, the noisy introspection model [16] relies on (possi- More precisely, consider a set N = f1; :::; ng of players. De- bly many) layers of speculation on the beliefs about other players’ note as Si the set of pure (i.e., deterministic) strategies available to beliefs. When all beliefs are perfectly correct, adding layers of player i, and denote as si an arbitrary member of i’s strategy set. A probability distribution over pure strategies is called a mixed strat- speculation can only converge to a Nash equilibrium. However, the authors of [16] show that, by including some small (noisy) uncer- egy σi. Accordingly, the set of mixed strategies for each player, Σi, tainty in the conjectures about other players beliefs, the game can contains the set of pure strategies, Si, as degenerate cases. Each player’s randomization is statistically independent of those of the produce outcomes significantly different from a Nash equilibrium. Another model of equilibrium with bounded rationality, called other players. Then, ui represents player i’s payoff (or utility) func- Quantal Response Equilibrium, or QRE [23], has been used to tion: ui(σi; σ i) is the payoff to player i given her strategy (σi) − characterize equilibria in games where users make errors on the and the other players’ strategies (summarized as σ i). An n-player − computation of the payoffs associated with a given strategy. Given game can then be described as G = fN; Σi; Σ−i; ui; u−ig. Players are in a Nash equilibrium if a change in strategies by any two strategies and their associated payoffs (u1; u2), with u1 > one of them would lead that player to obtain a lower utility than u2, an a priori rational player may choose the strategy yielding if she remained with her current strategy [25]. Formally, we can u2 if she makes errors ("1; "2) in the computation of the pay- define a Nash equilibrium as follows: offs (u1; u2) such that u1 + "1 < u2 + "2. McFadden showed that, in such a context, one could express the probability of choos- ∗ ∗ ∗ DEFINITION 1. A vector of mixed strategies σ = (σ1 ; :::; σn) 2 ing the strategy yielding u1 as a power function Pr(choose 1) = Σ comprises a mixed-strategy Nash equilibrium of a game G if, for λu1 λu1 λu2 0 0 e =(e + e ), where λ is a factor that characterizes the prob- ∗ ∗ ∗ all i 2 N and for all σi 2 Σi, ui(σi ; σ−i) − ui(σi ; σ−i) ≤ 0.
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