The Effective Minimax Value of Asynchronously Repeated Games*
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Game Theory 2: Extensive-Form Games and Subgame Perfection
Game Theory 2: Extensive-Form Games and Subgame Perfection 1 / 26 Dynamics in Games How should we think of strategic interactions that occur in sequence? Who moves when? And what can they do at different points in time? How do people react to different histories? 2 / 26 Modeling Games with Dynamics Players Player function I Who moves when Terminal histories I Possible paths through the game Preferences over terminal histories 3 / 26 Strategies A strategy is a complete contingent plan Player i's strategy specifies her action choice at each point at which she could be called on to make a choice 4 / 26 An Example: International Crises Two countries (A and B) are competing over a piece of land that B occupies Country A decides whether to make a demand If Country A makes a demand, B can either acquiesce or fight a war If A does not make a demand, B keeps land (game ends) A's best outcome is Demand followed by Acquiesce, worst outcome is Demand and War B's best outcome is No Demand and worst outcome is Demand and War 5 / 26 An Example: International Crises A can choose: Demand (D) or No Demand (ND) B can choose: Fight a war (W ) or Acquiesce (A) Preferences uA(D; A) = 3 > uA(ND; A) = uA(ND; W ) = 2 > uA(D; W ) = 1 uB(ND; A) = uB(ND; W ) = 3 > uB(D; A) = 2 > uB(D; W ) = 1 How can we represent this scenario as a game (in strategic form)? 6 / 26 International Crisis Game: NE Country B WA D 1; 1 3X; 2X Country A ND 2X; 3X 2; 3X I Is there something funny here? I Is there something funny here? I Specifically, (ND; W )? I Is there something funny here? -
Equilibrium Refinements
Equilibrium Refinements Mihai Manea MIT Sequential Equilibrium I In many games information is imperfect and the only subgame is the original game. subgame perfect equilibrium = Nash equilibrium I Play starting at an information set can be analyzed as a separate subgame if we specify players’ beliefs about at which node they are. I Based on the beliefs, we can test whether continuation strategies form a Nash equilibrium. I Sequential equilibrium (Kreps and Wilson 1982): way to derive plausible beliefs at every information set. Mihai Manea (MIT) Equilibrium Refinements April 13, 2016 2 / 38 An Example with Incomplete Information Spence’s (1973) job market signaling game I The worker knows her ability (productivity) and chooses a level of education. I Education is more costly for low ability types. I Firm observes the worker’s education, but not her ability. I The firm decides what wage to offer her. In the spirit of subgame perfection, the optimal wage should depend on the firm’s beliefs about the worker’s ability given the observed education. An equilibrium needs to specify contingent actions and beliefs. Beliefs should follow Bayes’ rule on the equilibrium path. What about off-path beliefs? Mihai Manea (MIT) Equilibrium Refinements April 13, 2016 3 / 38 An Example with Imperfect Information Courtesy of The MIT Press. Used with permission. Figure: (L; A) is a subgame perfect equilibrium. Is it plausible that 2 plays A? Mihai Manea (MIT) Equilibrium Refinements April 13, 2016 4 / 38 Assessments and Sequential Rationality Focus on extensive-form games of perfect recall with finitely many nodes. An assessment is a pair (σ; µ) I σ: (behavior) strategy profile I µ = (µ(h) 2 ∆(h))h2H: system of beliefs ui(σjh; µ(h)): i’s payoff when play begins at a node in h randomly selected according to µ(h), and subsequent play specified by σ. -
Subgame Perfect (-)Equilibrium in Perfect Information Games
Subgame perfect (-)equilibrium in perfect information games J´anosFlesch∗ April 15, 2016 Abstract We discuss recent results on the existence and characterization of subgame perfect ({)equilibrium in perfect information games. The game. We consider games with perfect information and deterministic transitions. Such games can be given by a directed tree.1 In this tree, each node is associated with a player, who controls this node. The outgoing arcs at this node represent the actions available to this player at this node. We assume that each node has at least one successor, rather than having terminal nodes.2 Play of the game starts at the root. At any node z that play visits, the player who controls z has to choose one of the actions at z, which brings play to a next node. This induces an infinite path in the tree from the root, which we call a play. Depending on this play, each player receives a payoff. Note that these payoffs are fairly general. This setup encompasses the case when the actions induce instantaneous rewards which are then aggregated into a payoff, possibly by taking the total discounted sum or the long-term average. It also includes payoff functions considered in the literature of computer science (reachability games, etc.). Subgame-perfect (-)equilibrium. We focus on pure strategies for the players. A central solution concept in such games is subgame-perfect equilibrium, which is a strategy profile that induces a Nash equilibrium in every subgame, i.e. when starting at any node in the tree, no player has an incentive to deviate individually from his continuation strategy. -
Subgame-Perfect Equilibria in Mean-Payoff Games
Subgame-perfect Equilibria in Mean-payoff Games Léonard Brice ! Université Gustave Eiffel, France Jean-François Raskin ! Université Libre de Bruxelles, Belgium Marie van den Bogaard ! Université Gustave Eiffel, France Abstract In this paper, we provide an effective characterization of all the subgame-perfect equilibria in infinite duration games played on finite graphs with mean-payoff objectives. To this end, we introduce the notion of requirement, and the notion of negotiation function. We establish that the plays that are supported by SPEs are exactly those that are consistent with the least fixed point of the negotiation function. Finally, we show that the negotiation function is piecewise linear, and can be analyzed using the linear algebraic tool box. As a corollary, we prove the decidability of the SPE constrained existence problem, whose status was left open in the literature. 2012 ACM Subject Classification Software and its engineering: Formal methods; Theory of compu- tation: Logic and verification; Theory of computation: Solution concepts in game theory. Keywords and phrases Games on graphs, subgame-perfect equilibria, mean-payoff objectives. Digital Object Identifier 10.4230/LIPIcs... 1 Introduction The notion of Nash equilibrium (NE) is one of the most important and most studied solution concepts in game theory. A profile of strategies is an NE when no rational player has an incentive to change their strategy unilaterally, i.e. while the other players keep their strategies. Thus an NE models a stable situation. Unfortunately, it is well known that, in sequential games, NEs suffer from the problem of non-credible threats, see e.g. [18]. In those games, some NE only exists when some players do not play rationally in subgames and so use non-credible threats to force the NE. -
Alpha-Beta Pruning
Alpha-Beta Pruning Carl Felstiner May 9, 2019 Abstract This paper serves as an introduction to the ways computers are built to play games. We implement the basic minimax algorithm and expand on it by finding ways to reduce the portion of the game tree that must be generated to find the best move. We tested our algorithms on ordinary Tic-Tac-Toe, Hex, and 3-D Tic-Tac-Toe. With our algorithms, we were able to find the best opening move in Tic-Tac-Toe by only generating 0.34% of the nodes in the game tree. We also explored some mathematical features of Hex and provided proofs of them. 1 Introduction Building computers to play board games has been a focus for mathematicians ever since computers were invented. The first computer to beat a human opponent in chess was built in 1956, and towards the late 1960s, computers were already beating chess players of low-medium skill.[1] Now, it is generally recognized that computers can beat even the most accomplished grandmaster. Computers build a tree of different possibilities for the game and then work backwards to find the move that will give the computer the best outcome. Al- though computers can evaluate board positions very quickly, in a game like chess where there are over 10120 possible board configurations it is impossible for a computer to search through the entire tree. The challenge for the computer is then to find ways to avoid searching in certain parts of the tree. Humans also look ahead a certain number of moves when playing a game, but experienced players already know of certain theories and strategies that tell them which parts of the tree to look. -
(501B) Problem Set 5. Bayesian Games Suggested Solutions by Tibor Heumann
Dirk Bergemann Department of Economics Yale University Microeconomic Theory (501b) Problem Set 5. Bayesian Games Suggested Solutions by Tibor Heumann 1. (Market for Lemons) Here I ask that you work out some of the details in perhaps the most famous of all information economics models. By contrast to discussion in class, we give a complete formulation of the game. A seller is privately informed of the value v of the good that she sells to a buyer. The buyer's prior belief on v is uniformly distributed on [x; y] with 3 0 < x < y: The good is worth 2 v to the buyer. (a) Suppose the buyer proposes a price p and the seller either accepts or rejects p: If she accepts, the seller gets payoff p−v; and the buyer gets 3 2 v − p: If she rejects, the seller gets v; and the buyer gets nothing. Find the optimal offer that the buyer can make as a function of x and y: (b) Show that if the buyer and the seller are symmetrically informed (i.e. either both know v or neither party knows v), then trade takes place with probability 1. (c) Consider a simultaneous acceptance game in the model with private information as in part a, where a price p is announced and then the buyer and the seller simultaneously accept or reject trade at price p: The payoffs are as in part a. Find the p that maximizes the probability of trade. [SOLUTION] (a) We look for the subgame perfect equilibrium, thus we solve by back- ward induction. -
14.12 Game Theory Lecture Notes∗ Lectures 7-9
14.12 Game Theory Lecture Notes∗ Lectures 7-9 Muhamet Yildiz Intheselecturesweanalyzedynamicgames(withcompleteinformation).Wefirst analyze the perfect information games, where each information set is singleton, and develop the notion of backwards induction. Then, considering more general dynamic games, we will introduce the concept of the subgame perfection. We explain these concepts on economic problems, most of which can be found in Gibbons. 1 Backwards induction The concept of backwards induction corresponds to the assumption that it is common knowledge that each player will act rationally at each node where he moves — even if his rationality would imply that such a node will not be reached.1 Mechanically, it is computed as follows. Consider a finite horizon perfect information game. Consider any node that comes just before terminal nodes, that is, after each move stemming from this node, the game ends. If the player who moves at this node acts rationally, he will choose the best move for himself. Hence, we select one of the moves that give this player the highest payoff. Assigning the payoff vector associated with this move to the node at hand, we delete all the moves stemming from this node so that we have a shorter game, where our node is a terminal node. Repeat this procedure until we reach the origin. ∗These notes do not include all the topics that will be covered in the class. See the slides for a more complete picture. 1 More precisely: at each node i the player is certain that all the players will act rationally at all nodes j that follow node i; and at each node i the player is certain that at each node j that follows node i the player who moves at j will be certain that all the players will act rationally at all nodes k that follow node j,...ad infinitum. -
Game Theory with Translucent Players
Game Theory with Translucent Players Joseph Y. Halpern and Rafael Pass∗ Cornell University Department of Computer Science Ithaca, NY, 14853, U.S.A. e-mail: fhalpern,[email protected] November 27, 2012 Abstract A traditional assumption in game theory is that players are opaque to one another—if a player changes strategies, then this change in strategies does not affect the choice of other players’ strategies. In many situations this is an unrealistic assumption. We develop a framework for reasoning about games where the players may be translucent to one another; in particular, a player may believe that if she were to change strategies, then the other player would also change strategies. Translucent players may achieve significantly more efficient outcomes than opaque ones. Our main result is a characterization of strategies consistent with appro- priate analogues of common belief of rationality. Common Counterfactual Belief of Rationality (CCBR) holds if (1) everyone is rational, (2) everyone counterfactually believes that everyone else is rational (i.e., all players i be- lieve that everyone else would still be rational even if i were to switch strate- gies), (3) everyone counterfactually believes that everyone else is rational, and counterfactually believes that everyone else is rational, and so on. CCBR characterizes the set of strategies surviving iterated removal of minimax dom- inated strategies: a strategy σi is minimax dominated for i if there exists a 0 0 0 strategy σ for i such that min 0 u (σ ; µ ) > max u (σ ; µ ). i µ−i i i −i µ−i i i −i ∗Halpern is supported in part by NSF grants IIS-0812045, IIS-0911036, and CCF-1214844, by AFOSR grant FA9550-08-1-0266, and by ARO grant W911NF-09-1-0281. -
Lecture Notes
Chapter 12 Repeated Games In real life, most games are played within a larger context, and actions in a given situation affect not only the present situation but also the future situations that may arise. When a player acts in a given situation, he takes into account not only the implications of his actions for the current situation but also their implications for the future. If the players arepatient andthe current actionshavesignificant implications for the future, then the considerations about the future may take over. This may lead to a rich set of behavior that may seem to be irrational when one considers the current situation alone. Such ideas are captured in the repeated games, in which a "stage game" is played repeatedly. The stage game is repeated regardless of what has been played in the previous games. This chapter explores the basic ideas in the theory of repeated games and applies them in a variety of economic problems. As it turns out, it is important whether the game is repeated finitely or infinitely many times. 12.1 Finitely-repeated games Let = 0 1 be the set of all possible dates. Consider a game in which at each { } players play a "stage game" , knowing what each player has played in the past. ∈ Assume that the payoff of each player in this larger game is the sum of the payoffsthat he obtains in the stage games. Denote the larger game by . Note that a player simply cares about the sum of his payoffs at the stage games. Most importantly, at the beginning of each repetition each player recalls what each player has 199 200 CHAPTER 12. -
Collusion and Cartels
Collusion and Cartels Allan Collard-Wexler Duke November 4, 2016 Defining Collusion: A secret agreement between two or more parties for a fraudulent, illegal, or deceitful purpose. These are “cooperative” outcomes in the sense that firms agree to act together. Why do firms want to achieve a cooperative outcome? They earn higher profits! Some famous recent examples of collusion (we’ll see more when we cover antitrust issues): Lysine (mid-1990’s) From Wikipedia: The lysine price-fixing conspiracy was an organized effort during the mid-1990s to raise the price of the animal feed additive lysine. It involved five companies that had commercialized high-tech fermentation technologies, including American company Archer Daniels Midland (ADM), Japanese companies Ajinomoto and Kyowa Hakko Kogyo, and Korean companies Sewon America Inc. and Cheil Jedang Ltd. A criminal investigation resulted in fines and three-year prison sentences for three executives of ADM who colluded with the other companies to fix prices. The foreign companies settled with the United States Department of Justice Antitrust Division in September through December 1996. Each firm and four executives from the Asian firms pled guilty as part of a plea bargain to aid in further investigation against ADM. The cartel had been able to raise lysine prices 70% within their first nine months of cooperation. The investigation yielded $105 million in criminal fines, a record antitrust penalty at the time, including a $70 million fine against ADM. ADM was fined an additional $30 million for its participation in a separate conspiracy in the citric acid market and paid a total fine of $100 million. -
570: Minimax Sample Complexity for Turn-Based Stochastic Game
Minimax Sample Complexity for Turn-based Stochastic Game Qiwen Cui1 Lin F. Yang2 1School of Mathematical Sciences, Peking University 2Electrical and Computer Engineering Department, University of California, Los Angeles Abstract guarantees are rather rare due to complex interaction be- tween agents that makes the problem considerably harder than single agent reinforcement learning. This is also known The empirical success of multi-agent reinforce- as non-stationarity in MARL, which means when multi- ment learning is encouraging, while few theoret- ple agents alter their strategies based on samples collected ical guarantees have been revealed. In this work, from previous strategy, the system becomes non-stationary we prove that the plug-in solver approach, proba- for each agent and the improvement can not be guaranteed. bly the most natural reinforcement learning algo- One fundamental question in MBRL is that how to design rithm, achieves minimax sample complexity for efficient algorithms to overcome non-stationarity. turn-based stochastic game (TBSG). Specifically, we perform planning in an empirical TBSG by Two-players turn-based stochastic game (TBSG) is a two- utilizing a ‘simulator’ that allows sampling from agents generalization of Markov decision process (MDP), arbitrary state-action pair. We show that the em- where two agents choose actions in turn and one agent wants pirical Nash equilibrium strategy is an approxi- to maximize the total reward while the other wants to min- mate Nash equilibrium strategy in the true TBSG imize it. As a zero-sum game, TBSG is known to have and give both problem-dependent and problem- Nash equilibrium strategy [Shapley, 1953], which means independent bound. -
Contemporaneous Perfect Epsilon-Equilibria
Games and Economic Behavior 53 (2005) 126–140 www.elsevier.com/locate/geb Contemporaneous perfect epsilon-equilibria George J. Mailath a, Andrew Postlewaite a,∗, Larry Samuelson b a University of Pennsylvania b University of Wisconsin Received 8 January 2003 Available online 18 July 2005 Abstract We examine contemporaneous perfect ε-equilibria, in which a player’s actions after every history, evaluated at the point of deviation from the equilibrium, must be within ε of a best response. This concept implies, but is stronger than, Radner’s ex ante perfect ε-equilibrium. A strategy profile is a contemporaneous perfect ε-equilibrium of a game if it is a subgame perfect equilibrium in a perturbed game with nearly the same payoffs, with the converse holding for pure equilibria. 2005 Elsevier Inc. All rights reserved. JEL classification: C70; C72; C73 Keywords: Epsilon equilibrium; Ex ante payoff; Multistage game; Subgame perfect equilibrium 1. Introduction Analyzing a game begins with the construction of a model specifying the strategies of the players and the resulting payoffs. For many games, one cannot be positive that the specified payoffs are precisely correct. For the model to be useful, one must hope that its equilibria are close to those of the real game whenever the payoff misspecification is small. To ensure that an equilibrium of the model is close to a Nash equilibrium of every possible game with nearly the same payoffs, the appropriate solution concept in the model * Corresponding author. E-mail addresses: [email protected] (G.J. Mailath), [email protected] (A. Postlewaite), [email protected] (L.