Abstract. Experimentalists frequently claim that human subjects in the laboratory violate game-theoretic predictions. It is here argued that this claim is usually premature. The paper elaborates on this theme by way of raising some conceptual and methodological issues in connection with the very definition of a game and of players ’ preferences, in particular with respect to potential context dependence, interpersonal preference dependence, backward induction and incomplete information.

When giving an nalysis of knowledge in multiagent systems, one needs a framework in which higher-order information and its dynamics can both be represented. A recent tradition stoxting in origina work by Plaza treats all of knowledge, higher-order knowledge, and its dynamics on the sae foot. Our work is in that tradition. It also fits in approaches that not only dynaize the epistemics, but also epistemize the dynamics: the ac- tions that (groups of) agents perform oxe epistemic actions. Different agents may have different information about which action is taking place, including higher-order information. We demonstrate that such information changes require subtle descriptions. Our contribution is to provide a complete axiomatization for n action language of vn Ditmoxsch, where an action is interpreted as a relation between epistemic states (pointed models) and sets of epistemic states. The applicability of the framework is found in every context where multiagent strategic decision making is at stake, and aready demonstrated in gae-like scenoxios such as Cluedo and coxd games.

‘‘Human experience, which is constantly contradicting theory, is the great test of truth.’’ �Dr. Johnson, quoted in James Boswell, The Life of Samuel Johnson L.L.D. This paper reports experiments designed to study strategic sophistication, the extent to which behavior in games reflects attempts to predict others ’ decisions, taking their incentives into account. We study subjects ’ initial responses to normal-form games with various patterns of iterated dominance and unique pure-strategy equilibria without dominance, using a computer interface that allowed them to search for hidden payoff information, while recording their searches. Monitoring subjects ’ information searches along with their decisions allows us to better understand how their decisions are determined, and subjects ’ deviations from the search patterns suggested by equilibrium analysis help to predict their deviations from equilibrium decisions.

This paper presents experimental results from an analysis of two similar games, the repeated ultimatum game and the repeated best-shot game. The experiment examines whether the amount and content of information given to players affects the evolution of play in the two games. In one experimental treatment, subjects in both games observe not only their own actions and payoffs, but also those of one randomly chosen pair of players in the just-completed round of play. In the other treatment, subjects in both games observe only their own actions and payoffs. We present evidence suggesting that observation of other players ' actions and payoffs may a ect the evolution of play relative to the case of no observation.

Starting from an example of the Allies ’ decision to feint at Calais and attack Normandy on D-Day, this paper models misrepresentation of intentions to competitors or enemies. Allowing for the possibility of bounded strategic rationality and rational players ’ responses to it yields a sensible account of lying via costless, noiseless messages. In some leading cases, the model has generically unique pure-strategy sequential equilibria, in which rational players exploit boundedly rational players, but are not themselves fooled. In others, the model has generically essentially unique mixed-strategy sequential equilibria, in which rational players’ strategies protect all players from exploitation. (JEL C72, D72, D80) Lord, what fools these mortals be! —Puck, A Midsummer Night’s Dream, Act 3 You may fool all the people some of the time; you can even fool some of the people all the time; but you can’t fool all of the people all the time.

We consider an implementation problem faced by a planner who manages a roadway network. The problem entails both hidden information and hidden actions. We solve the planner's problem by introducing a new class of mechanisms and a new notion of implementation. The mechanisms, called price schemes, attach transfers to the available routes; they do not involve direct revelation. The method of implementation is evolutionary, requiring that players who follow any reasonable myopic adjustment process eventually learn to behave as the planner desires. We show that efficient behavior can be guaranteed using simple, decentralized price schemes.

The Common Prior Assumption (CPA) is central to the economics of information and the foundations of game theory. Recent contributions (Dekel and Gul, 1997, Gul, 1996, Lipman, 1995) have questioned its meaningfulness in situations of incomplete information where there is no ex ante stage and the primitives of the model are the individuals ’ belief hierarchies. We address this conceptual issue by providing characterizations of two local versions of the CPA which are in terms of the primitives and, therefore, do not involve a counterfactual and problematic ex ante stage. The characterizations involve three notions: Comprehensive Agreement, no error of beliefs and common belief in no error. Comprehensive Agreement is defined as the absence of “agreement to disagree ” about any aspect of beliefs; it is a generalization of Aumann’s (1976) notion of agreement. The entire analysis is carried out locally, that is, with reference to the “true state ” (which represents the actual profile of belief hierarchies) and does not rely on the Truth Axiom for individual beliefs. The results are also applied to the problem of generalizing the notion of Bayesian updating to single-person, intertemporal situations without perfect recall and without given information partitions. We are grateful to Bart Lipman and two referees for helpful and constructive comments. Seminar participants at Harvard, Penn, Princeton, USC and Yale provided useful comments. We also greatly benefited from discussions with participants at the SITE Workshop on the Epistemic Foundations of Game Theory (Stanford), in particular Steve Morris.

We adopt the decision-theoretic principle of expected utility maximization as a paradigm for designing autonomous rational agents, and present a framework that uses this paradigm to determine the choice of coordinated action. We endow an agent with a specialized representation that captures the agent's knowledge about the environment and about the other agents, including its knowledge about their states of knowledge, which can include what they know about the other agents, and so on. This reciprocity leads to a recursive nesting of models. Our framework puts forth a representation for the recursive models and, under the assumption that the nesting of models is finite, uses dynamic programming to solve this representation for the agent's rational choice of action. Using a decision-theoretic approach, our work addresses concerns of agent decision-making about coordinated action in unpredictable situations, without imposing upon agents pre-designed prescriptions, or protocols, ...

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We introduce a Game Logic with Preferences (GLP), which makes it possible to reason about how information or assumptions about the preferences of other players can be used by agents in order to realize their own preferences. GLP can be applied to the analysis of social protocols such as voting or fair division problems; we illustrate this use of GLP with a number of worked examples. We then prove that the model checking problem for GLP is tractable, and describe an implemented model checker for the logic -- by using the model checker, it is possible to automate the analysis and verification of social protocols.