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256
Computationally feasible VCG mechanisms
 In Proceedings of the Second ACM Conference on Electronic Commerce (EC’00
, 2000
"... A major achievement of mechanism design theory is a general method for the construction of truthful mechanisms called VCG. When applying this method to complex problems such as combinatorial auctions, a difficulty arises: VCG mechanisms are required to compute optimal outcomes and are therefore comp ..."
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Cited by 188 (5 self)
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A major achievement of mechanism design theory is a general method for the construction of truthful mechanisms called VCG. When applying this method to complex problems such as combinatorial auctions, a difficulty arises: VCG mechanisms are required to compute optimal outcomes and are therefore computationally infeasible. However, if the optimal outcome is replaced by the results of a suboptimal algorithm, the resulting mechanism (termed VCGbased) is no longer necessarily truthful. The first part of this paper studies this phenomenon in depth and shows that it is near universal. Specifically, we prove that essentially all reasonable approximations or heuristics for combinatorial auctions as well as a wide class of cost minimization problems yield nontruthful VCGbased mechanisms. We generalize these results for affine maximizers. The second part of this paper proposes a general method for circumventing the above problem. We introduce a modification of VCGbased mechanisms in which the agents are given a chance to improve the output of the underlying algorithm. When the agents behave truthfully, the welfare obtained by the mechanism is at least as good as the one obtained by the algorithm’s output. We provide a strong rationale for truthtelling behavior. Our method satisfies individual rationality as well.
Playing Large Games using Simple Strategies
, 2003
"... We prove the existence of #Nash equilibrium strategies with support logarithmic in the number of pure strategies. We also show that the payo#s to all players in any (exact) Nash equilibrium can be #approximated by the payo#s to the players in some such logarithmic support #Nash equilibrium. These ..."
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Cited by 90 (1 self)
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We prove the existence of #Nash equilibrium strategies with support logarithmic in the number of pure strategies. We also show that the payo#s to all players in any (exact) Nash equilibrium can be #approximated by the payo#s to the players in some such logarithmic support #Nash equilibrium. These strategies are also uniform on a multiset of logarithmic size and therefore this leads to a quasipolynomial algorithm for computing an #Nash equilibrium. To our knowledge this is the first subexponential algorithm for finding an #Nash equilibrium. Our results hold for any multipleplayer game as long as the number of players is a constant (i.e., it is independent of the number of pure strategies). A similar argument also proves that for a fixed number of players m, the payo#s to all players in any mtuple of mixed strategies can be #approximated by the payo#s in some mtuple of constant support strategies.
Agentbased computational models and generative social science
 Complexity
, 1999
"... This article argues that the agentbased computational model permits a distinctive approach to social science for which the term “generative ” is suitable. In defending this terminology, features distinguishing the approach from both “inductive ” and “deductive ” science are given. Then, the followi ..."
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Cited by 64 (0 self)
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This article argues that the agentbased computational model permits a distinctive approach to social science for which the term “generative ” is suitable. In defending this terminology, features distinguishing the approach from both “inductive ” and “deductive ” science are given. Then, the following specific contributions to social science are discussed: The agentbased computational model is a new tool for empirical research. It offers a natural environment for the study of connectionist phenomena in social science. Agentbased modeling provides a powerful way to address certain enduring—and especially interdisciplinary—questions. It allows one to subject certain core theories—such as neoclassical microeconomics—to important types of stress (e.g., the effect of evolving preferences). It permits one to study how rules of individual behavior give rise—or “map up”—to macroscopic regularities and organizations. In turn, one can employ laboratory behavioral research findings to select among competing agentbased (“bottom up”) models. The agentbased approach may well have the important effect of decoupling individual rationality from macroscopic equilibrium and of separating decision science from social science more generally. Agentbased modeling offers powerful new forms of hybrid theoreticalcomputational work; these are particularly relevant to the study of nonequilibrium systems. The agentbased approach invites the interpretation of society as a distributed computational device, and in turn the interpretation of social dynamics as a type of computation. This interpretation raises important foundational issues in social science—some related to intractability, and some to undecidability proper. Finally, since “emergence” figures prominently in this literature, I take up the connection between agentbased modeling and classical emergentism, criticizing the latter and arguing that the two are incompatible. � 1999 John Wiley &
Argumentationbased negotiation
, 2004
"... Negotiation is essential in settings where autonomous agents have conflicting interests and a desire to cooperate. For this reason, mechanisms in which agents exchange potential agreements according to various rules of interaction have become very popular in recent years as evident, for example, in ..."
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Cited by 63 (15 self)
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Negotiation is essential in settings where autonomous agents have conflicting interests and a desire to cooperate. For this reason, mechanisms in which agents exchange potential agreements according to various rules of interaction have become very popular in recent years as evident, for example, in the auction and mechanism design community. However, a growing body of research is now emerging which points out limitations in such mechanisms and advocates the idea that agents can increase the likelihood and quality of an agreement by exchanging arguments which influence each others ’ states. This community further argues that argument exchange is sometimes essential when various assumptions about agent rationality cannot be satisfied. To this end, in this article, we identify the main research motivations and ambitions behind work in the field. We then provide a conceptual framework through which we outline the core elements and features required by agents engaged in argumentationbased negotiation, as well as the environment that hosts these agents. For each of these elements, we survey and evaluate existing proposed techniques in the literature and highlight the major challenges that need to be addressed if argumentbased negotiation research is to reach its full potential.
AnalogyBased Expectation Equilibrium
, 2001
"... It is assumed that players bundle nodes in which other players must move into analogy classes, and players only have expectations about the average behavior in every class. A solution concept is proposed for multistage games with perfect information: at every node players choose bestresponses to t ..."
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Cited by 62 (3 self)
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It is assumed that players bundle nodes in which other players must move into analogy classes, and players only have expectations about the average behavior in every class. A solution concept is proposed for multistage games with perfect information: at every node players choose bestresponses to their analogybased expectations, and expectations are correct on average over those various nodes pooled together into the same analogy classes. The approach is applied to a variety of games. It is shown that a player may beneÞt from having a coarse analogy partitioning. And for simple analogy partitioning, (1) initial cooperation followed by an end opportunistic behavior may emerge in the Þnitely repeated prisoner’s dilemma (or in the centipede game), (2) an agreement need not be reached immediately in bargaining games with complete information.
Costly valuation computation in auctions
 IN IN PROCEEDINGS OF THE EIGHTH CONFERENCE OF THEORETICAL ASPECTS OF KNOWLEDGE AND RATIONALITY (TARK VIII), SIENNA
, 2001
"... We investigate deliberation and bidding strategies of agents with unlimited but costly computation who are participating in auctions. The agents do not a priori know their valuations for the items begin auctioned. Instead they devote computational resources to compute their valuations. We present a ..."
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Cited by 52 (26 self)
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We investigate deliberation and bidding strategies of agents with unlimited but costly computation who are participating in auctions. The agents do not a priori know their valuations for the items begin auctioned. Instead they devote computational resources to compute their valuations. We present a normative model of bounded rationality where deliberation actions of agents are incorporated into strategies and equilibria are analyzed for standard auction protocols. We show that even in settings such as English auctions where information about other agents ’ valuations is revealed for free by the bidding process, agents may still compute on opponents’ valuation problems, incurring a cost, in order to determine how to bid. We compare the costly computation model of bounded rationality with a different model where computation is free but limited. For some auction mechanisms the equilibrium strategies are substantially different. It can be concluded that the model of bounded rationality impacts the agents’ equilibrium strategies and must be considered when designing mechanisms for computationally limited agents.
Multiagent reinforcement learning: a critical survey
, 2003
"... We survey the recent work in AI on multiagent reinforcement learning (that is, learning in stochastic games). We then argue that, while exciting, this work is flawed. The fundamental flaw is unclarity about the problem or problems being addressed. After tracing a representative sample of the recent ..."
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Cited by 52 (0 self)
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We survey the recent work in AI on multiagent reinforcement learning (that is, learning in stochastic games). We then argue that, while exciting, this work is flawed. The fundamental flaw is unclarity about the problem or problems being addressed. After tracing a representative sample of the recent literature, we identify four welldefined problems in multiagent reinforcement learning, single out the problem that in our view is most suitable for AI, and make some remarks about how we believe progress is tobemadeonthisproblem. 1
Bargaining with Limited Computation: Deliberation Equilibrium
 ARTIFICIAL INTELLIGENCE
, 2001
"... We develop a normative theory of interactionnegotiation in particularamong selfinterested computationally limited agents where computational actions are game theoretically treated as part of an agent's strategy. We focus on a 2agent setting where each agent has an intractable individual prob ..."
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Cited by 45 (19 self)
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We develop a normative theory of interactionnegotiation in particularamong selfinterested computationally limited agents where computational actions are game theoretically treated as part of an agent's strategy. We focus on a 2agent setting where each agent has an intractable individual problem, and there is a potential gain from pooling the problems, giving rise to an intractable joint problem. At any time, an agent can compute to improve its solution to its own problem, its opponent's problem, or the joint problem. At a deadline the agents then decide whether to implement the joint solution, and if so, how to divide its value (or cost). We present a fully normative model for controlling anytime algorithms where each agent has statistical performance profiles which are optimally conditioned on the problem instance as well as on the path of results of the algorithm run so far. Using this model, we introduce a solution concept, which we call deliberation equilibrium. It is the perfect Bayesian equilibrium of the game where deliberation actions are part of each agent's strategy. The equilibria differ based on whether the performance profiles are deterministic or stochastic, whether the deadline is known or not, and whether the proposer is known in advance or not. We present algorithms for finding the equilibria. Finally, we show that there exist instances of the deliberationbargaining problem where no pure strategy equilibria exist and also instances where the unique equilibrium outcome is not Pareto efficient.
Effects of interaction topology and activation regime in several multiagent systems
 In MABS
"... The effects of distinct agent interaction and activation structures are compared and contrasted in several multiagent models of social phenomena. Random graphs and lattices represent two limiting kinds of agent interaction networks studied, with socalled 'smallworld ' networks being an intermedia ..."
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Cited by 33 (0 self)
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The effects of distinct agent interaction and activation structures are compared and contrasted in several multiagent models of social phenomena. Random graphs and lattices represent two limiting kinds of agent interaction networks studied, with socalled 'smallworld ' networks being an intermediate form between these two extremes. A model of retirement behavior is studied with each network type, resulting in important differences in key model outputs. Then, in the context of a model of firm formation, in which multiagent structures (firms) are emergent, it is demonstrated that the medium of interaction—whether through individual agents or through firms—affects the qualitative character of the results. Finally, alternative agent activation 'schedules ' are studied. In particular, two activation modes are compared: (1) all agents being active exactly once each period, and (2) each agent having a random number of activations in every period with mean 1. In many circumstances these two regimes produce indistinguishable results at the aggregate level, but in certain cases the differences between them are significant. I
Instinctive and Cognitive Reasoning: A Study of Response Times
 Economic Journal
, 2007
"... Lecture audiences and students were asked to respond to virtual decision and game situations at gametheory.tau.ac.il. Several thousand observations were collected and the response time for each answer was recorded. There were significant differences in response time across responses. It is suggested ..."
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Cited by 31 (7 self)
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Lecture audiences and students were asked to respond to virtual decision and game situations at gametheory.tau.ac.il. Several thousand observations were collected and the response time for each answer was recorded. There were significant differences in response time across responses. It is suggested that choices made instinctively, that is, on the basis of an emotional response, require less response time than choices that require the use of cognitive reasoning. There is growing interest among economists in the bounds on the rationality of economic agents. Economists are increasingly abandoning the Ôeconomic manÕ paradigm and instead are using models that reflect what they consider to be more realistic descriptions of the way in which human beings make decisions. One can identify three approaches in the literature to Ôopening the black boxÕ of decision making. Bounded Rationality This approach is based on casual observations of the way in which people make decisions (and primarily of our own decisionmaking processes). These are used to construct abstract models which are intended to increase our understanding of the effect of certain decisionprocedural elements on the outcome of an economic interaction