The Nash equilibria of a two-person, non-zero-sum game are the solutions of a certain linear complementarity problem (LCP). In order to use this for solving a game in extensive form, the game must first be converted to a strategic description such as the normal form. The classical normal form, however, is often exponentially large in the size of the game tree. If the game has perfect recall, a linear-sized strategic description is the sequence form. For the resulting small LCP, we show that an equilibrium is found efficiently by Lemke's algorithm, a generalization of the Lemke-Howson method. Journal of Economic Literature Classification Number: C72.

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This paper defines the extensive-form correlated equilibrium (EFCE) for extensive games with perfect recall. The EFCE concept extends Aumann's strategic-form correlated equilibrium (CE). Before the game starts, a correlation device generates a move for each information set. This move is recommended to the player only when the player reaches the information set. In two-player perfect-recall extensive games without chance moves, the set of EFCE can be described by a polynomial number of consistency and incentive constraints. Assuming P is not equal to NP, this is not possible for the set of CE, or if the game has chance moves.Key words: correlated equilibrium; extensive game; polynomial-time computable MSC2000 subject classification: Primary: 91A18; secondary: 91A05, 91A28, 68Q17 OR/MS subject classification: Primary: noncooperative games; secondary: computational complexity History: Received March 22, 2006; revised September 19, 2007, and March 20, 2008. 1. Introduction. Aumann [1] defined the concept of correlated equilibrium (abbreviated as CE, also for the plural equilibria) for games in strategic form. Before the game starts, a device selects private signals from a joint probability distribution and sends them to the players. In the canonical representation of a CE, these signals are strategies that players are recommended to play. This paper proposes a new concept of correlated equilibrium for extensive games, called extensive-form correlated equilibrium or EFCE. Like in a CE (which is defined in terms of the strategic form), the recommendations to the players are moves that are generated before the game starts. However, each recommended move is assumed to be in a "sealed envelope" and is only revealed to a player when he reaches the information set where he can make that move.As recommendations become local in this way, players know less. Consequently, the set of EFCE outcomes is larger than the set of CE outcomes. However, an EFCE is more restrictive than an agent-form correlated equilibrium (AFCE). In the agent form of the game, moves are chosen by a separate agent for each information set of the player. In an EFCE, players remain in control of their future actions, which is important when they consider deviating from their recommended moves.The EFCE is a natural definition of correlated equilibrium for extensive games with perfect recall as defined by Kuhn [25]. Earlier extensions of Aumann's concept applied only to multistage games, including Bayesian games and stochastic games, which have a special time and information structure. These earlier approaches are discussed in §2.4.The main motivation for the EFCE concept is computational. The algorithmic input is some description of the extensive game with its game tree, information sets, moves, chance probabilities and payoffs. Polynomial (or linear or exponential) size and time always refer to the size of this description. The strategic form of the extensive game has typically exponential size. Hence, there are also exponentially many linear constraint...

This document is the author's final manuscript accepted version of the journal article, incorporating any revisions agreed during the peer review process. Some differences between this version and the published version may remain. You are advised to consult the publisher's version if you wish to cite from it. Leadership Games with Convex Strategy Sets AbstractA basic model of commitment is to convert a two-player game in strategic form to a "leadership game" with the same payoffs, where one player, the leader, commits to a strategy, to which the second player always chooses a best reply. This paper studies such leadership games for games with convex strategy sets. We apply them to mixed extensions of finite games, which we analyze completely, including nongeneric games. The main result is that leadership is advantageous in the sense that, as a set, the leader's payoffs in equilibrium are at least as high as his Nash and correlated equilibrium payoffs in the simultaneous game. We also consider leadership games with three or more players, where most conclusions no longer hold.

Interactions among agents can be conveniently described by game trees. In order to analyze a game, it is important to derive optimal (or equilibrium) strategies for the different players. The standard approach to finding such strategies in games with imperfect information is, in general, computationally intractable. The approach is to generate the normal form of the game (the matrix containing the payoff for each strategy combination), and then solve a linear program (LP) or a linear complementarity problem (LCP). The size of the normal form, however, is typically exponential in the size of the game tree, thus making this method impractical in all but the simplest cases. This paper describes a new representation of strategies which results in a practical linear formulation of the problem of two-player games with perfect recall (i.e., games where players never forget anything, which is a standard assumption). Standard LP or LCP solvers can then be applied to find optimal randomized strategies. The resulting algorithms are, in general, exponentially better than the standard ones, both in terms of time and in terms of space.