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Learning and Equilibrium
Learning and Equilibrium Drew Fudenberg1 and David K. Levine2 1Department of Economics, Harvard University, Cambridge, Massachusetts; email: [email protected] 2Department of Economics, Washington University of St. Louis, St. Louis, Missouri; email: [email protected] Annu. Rev. Econ. 2009. 1:385–419 Key Words First published online as a Review in Advance on nonequilibrium dynamics, bounded rationality, Nash equilibrium, June 11, 2009 self-confirming equilibrium The Annual Review of Economics is online at by 140.247.212.190 on 09/04/09. For personal use only. econ.annualreviews.org Abstract This article’s doi: The theory of learning in games explores how, which, and what 10.1146/annurev.economics.050708.142930 kind of equilibria might arise as a consequence of a long-run non- Annu. Rev. Econ. 2009.1:385-420. Downloaded from arjournals.annualreviews.org Copyright © 2009 by Annual Reviews. equilibrium process of learning, adaptation, and/or imitation. If All rights reserved agents’ strategies are completely observed at the end of each round 1941-1383/09/0904-0385$20.00 (and agents are randomly matched with a series of anonymous opponents), fairly simple rules perform well in terms of the agent’s worst-case payoffs, and also guarantee that any steady state of the system must correspond to an equilibrium. If players do not ob- serve the strategies chosen by their opponents (as in extensive-form games), then learning is consistent with steady states that are not Nash equilibria because players can maintain incorrect beliefs about off-path play. Beliefs can also be incorrect because of cogni- tive limitations and systematic inferential errors. -
Minimax TD-Learning with Neural Nets in a Markov Game
Minimax TD-Learning with Neural Nets in a Markov Game Fredrik A. Dahl and Ole Martin Halck Norwegian Defence Research Establishment (FFI) P.O. Box 25, NO-2027 Kjeller, Norway {Fredrik-A.Dahl, Ole-Martin.Halck}@ffi.no Abstract. A minimax version of temporal difference learning (minimax TD- learning) is given, similar to minimax Q-learning. The algorithm is used to train a neural net to play Campaign, a two-player zero-sum game with imperfect information of the Markov game class. Two different evaluation criteria for evaluating game-playing agents are used, and their relation to game theory is shown. Also practical aspects of linear programming and fictitious play used for solving matrix games are discussed. 1 Introduction An important challenge to artificial intelligence (AI) in general, and machine learning in particular, is the development of agents that handle uncertainty in a rational way. This is particularly true when the uncertainty is connected with the behavior of other agents. Game theory is the branch of mathematics that deals with these problems, and indeed games have always been an important arena for testing and developing AI. However, almost all of this effort has gone into deterministic games like chess, go, othello and checkers. Although these are complex problem domains, uncertainty is not their major challenge. With the successful application of temporal difference learning, as defined by Sutton [1], to the dice game of backgammon by Tesauro [2], random games were included as a standard testing ground. But even backgammon features perfect information, which implies that both players always have the same information about the state of the game. -
Modeling Strategic Behavior1
Modeling Strategic Behavior1 A Graduate Introduction to Game Theory and Mechanism Design George J. Mailath Department of Economics, University of Pennsylvania Research School of Economics, Australian National University 1September 16, 2020, copyright by George J. Mailath. World Scientific Press published the November 02, 2018 version. Any significant correc- tions or changes are listed at the back. To Loretta Preface These notes are based on my lecture notes for Economics 703, a first-year graduate course that I have been teaching at the Eco- nomics Department, University of Pennsylvania, for many years. It is impossible to understand modern economics without knowl- edge of the basic tools of game theory and mechanism design. My goal in the course (and this book) is to teach those basic tools so that students can understand and appreciate the corpus of modern economic thought, and so contribute to it. A key theme in the course is the interplay between the formal development of the tools and their use in applications. At the same time, extensions of the results that are beyond the course, but im- portant for context are (briefly) discussed. While I provide more background verbally on many of the exam- ples, I assume that students have seen some undergraduate game theory (such as covered in Osborne, 2004, Tadelis, 2013, and Wat- son, 2013). In addition, some exposure to intermediate microeco- nomics and decision making under uncertainty is helpful. Since these are lecture notes for an introductory course, I have not tried to attribute every result or model described. The result is a somewhat random pattern of citations and references. -
Comparing Auction Designs Where Suppliers Have Uncertain Costs and Uncertain Pivotal Status
RAND Journal of Economics Vol. 00, No. 0, Winter 2018 pp. 1–33 Comparing auction designs where suppliers have uncertain costs and uncertain pivotal status ∗ Par¨ Holmberg and ∗∗ Frank A. Wolak We analyze how market design influences bidding in multiunit procurement auctions where suppliers have asymmetric information about production costs. Our analysis is particularly relevant to wholesale electricity markets, because it accounts for the risk that a supplier is pivotal; market demand is larger than the total production capacity of its competitors. With constant marginal costs, expected welfare improves if the auctioneer restricts offers to be flat. We identify circumstances where the competitiveness of market outcomes improves with increased market transparency. We also find that, for buyers, uniform pricing is preferable to discriminatory pricing when producers’ private signals are affiliated. 1. Introduction Multiunit auctions are used to trade commodities, securities, emission permits, and other divisible goods. This article focuses on electricity markets, where producers submit offers before the level of demand and amount of available production capacity are fully known. Due to demand shocks, unexpected outages, transmission-constraints, and intermittent output from renewable energy sources, it often arises that an electricity producer is pivotal, that is, that realized demand is larger than the realized total production capacity of its competitors. A producer that is certain to be pivotal possess a substantial ability to exercise market power because it can withhold output ∗ Research Institute of Industrial Economics (IFN), Associate Researcher of the Energy Policy Research Group (EPRG), University of Cambridge; [email protected]. ∗∗ Program on Energy and Sustainable Development (PESD) and Stanford University; [email protected]. -
Natural Games
Ntur J A a A A ,,,* , a Department of Biosciences, FI-00014 University of Helsinki, Finland b Institute of Biotechnology, FI-00014 University of Helsinki, Finland c Department of Physics, FI-00014 University of Helsinki, Finland ABSTRACT Behavior in the context of game theory is described as a natural process that follows the 2nd law of thermodynamics. The rate of entropy increase as the payoff function is derived from statistical physics of open systems. The thermodynamic formalism relates everything in terms of energy and describes various ways to consume free energy. This allows us to associate game theoretical models of behavior to physical reality. Ultimately behavior is viewed as a physical process where flows of energy naturally select ways to consume free energy as soon as possible. This natural process is, according to the profound thermodynamic principle, equivalent to entropy increase in the least time. However, the physical portrayal of behavior does not imply determinism. On the contrary, evolutionary equation for open systems reveals that when there are three or more degrees of freedom for behavior, the course of a game is inherently unpredictable in detail because each move affects motives of moves in the future. Eventually, when no moves are found to consume more free energy, the extensive- form game has arrived at a solution concept that satisfies the minimax theorem. The equilibrium is Lyapunov-stable against variation in behavior within strategies but will be perturbed by a new strategy that will draw even more surrounding resources to the game. Entropy as the payoff function also clarifies motives of collaboration and subjective nature of decision making. -
Beyond Truth-Telling: Preference Estimation with Centralized School Choice and College Admissions∗
Beyond Truth-Telling: Preference Estimation with Centralized School Choice and College Admissions∗ Gabrielle Fack Julien Grenet Yinghua He October 2018 (Forthcoming: The American Economic Review) Abstract We propose novel approaches to estimating student preferences with data from matching mechanisms, especially the Gale-Shapley Deferred Acceptance. Even if the mechanism is strategy-proof, assuming that students truthfully rank schools in applications may be restrictive. We show that when students are ranked strictly by some ex-ante known priority index (e.g., test scores), stability is a plausible and weaker assumption, implying that every student is matched with her favorite school/college among those she qualifies for ex post. The methods are illustrated in simulations and applied to school choice in Paris. We discuss when each approach is more appropriate in real-life settings. JEL Codes: C78, D47, D50, D61, I21 Keywords: Gale-Shapley Deferred Acceptance Mechanism, School Choice, College Admissions, Stable Matching, Student Preferences ∗Fack: Universit´eParis-Dauphine, PSL Research University, LEDa and Paris School of Economics, Paris, France (e-mail: [email protected]); Grenet: CNRS and Paris School of Economics, 48 boulevard Jourdan, 75014 Paris, France (e-mail: [email protected]); He: Rice University and Toulouse School of Economics, Baker Hall 246, Department of Economcis MS-22, Houston, TX 77251 (e-mail: [email protected]). For constructive comments, we thank Nikhil Agarwal, Peter Arcidi- acono, Eduardo Azevedo, -
Disambiguation of Ellsberg Equilibria in 2X2 Normal Form Games
A Service of Leibniz-Informationszentrum econstor Wirtschaft Leibniz Information Centre Make Your Publications Visible. zbw for Economics Decerf, Benoit; Riedel, Frank Working Paper Disambiguation of Ellsberg equilibria in 2x2 normal form games Center for Mathematical Economics Working Papers, No. 554 Provided in Cooperation with: Center for Mathematical Economics (IMW), Bielefeld University Suggested Citation: Decerf, Benoit; Riedel, Frank (2016) : Disambiguation of Ellsberg equilibria in 2x2 normal form games, Center for Mathematical Economics Working Papers, No. 554, Bielefeld University, Center for Mathematical Economics (IMW), Bielefeld, http://nbn-resolving.de/urn:nbn:de:0070-pub-29013618 This Version is available at: http://hdl.handle.net/10419/149011 Standard-Nutzungsbedingungen: Terms of use: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Documents in EconStor may be saved and copied for your Zwecken und zum Privatgebrauch gespeichert und kopiert werden. personal and scholarly purposes. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle You are not to copy documents for public or commercial Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich purposes, to exhibit the documents publicly, to make them machen, vertreiben oder anderweitig nutzen. publicly available on the internet, or to distribute or otherwise use the documents in public. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, If the documents have -
A Pigouvian Approach to Congestion in Matching Markets
DISCUSSION PAPER SERIES IZA DP No. 11967 A Pigouvian Approach to Congestion in Matching Markets Yinghua He Thierry Magnac NOVEMBER 2018 DISCUSSION PAPER SERIES IZA DP No. 11967 A Pigouvian Approach to Congestion in Matching Markets Yinghua He Rice University and Toulouse School of Economics Thierry Magnac Toulouse School of Economics and IZA NOVEMBER 2018 Any opinions expressed in this paper are those of the author(s) and not those of IZA. Research published in this series may include views on policy, but IZA takes no institutional policy positions. The IZA research network is committed to the IZA Guiding Principles of Research Integrity. The IZA Institute of Labor Economics is an independent economic research institute that conducts research in labor economics and offers evidence-based policy advice on labor market issues. Supported by the Deutsche Post Foundation, IZA runs the world’s largest network of economists, whose research aims to provide answers to the global labor market challenges of our time. Our key objective is to build bridges between academic research, policymakers and society. IZA Discussion Papers often represent preliminary work and are circulated to encourage discussion. Citation of such a paper should account for its provisional character. A revised version may be available directly from the author. IZA – Institute of Labor Economics Schaumburg-Lippe-Straße 5–9 Phone: +49-228-3894-0 53113 Bonn, Germany Email: [email protected] www.iza.org IZA DP No. 11967 NOVEMBER 2018 ABSTRACT A Pigouvian Approach to Congestion in Matching Markets* Recruiting agents, or “programs” costly screen “applicants” in matching processes, and congestion in a market increases with the number of applicants to be screened. -
Bayesian Games Professors Greenwald 2018-01-31
Bayesian Games Professors Greenwald 2018-01-31 We describe incomplete-information, or Bayesian, normal-form games (formally; no examples), and corresponding equilibrium concepts. 1 A Bayesian Model of Interaction A Bayesian, or incomplete information, game is a generalization of a complete-information game. Recall that in a complete-information game, the game is assumed to be common knowledge. In a Bayesian game, in addition to what is common knowledge, players may have private information. This private information is captured by the notion of an epistemic type, which describes a player’s knowledge. The Bayesian-game formalism makes two simplifying assumptions: • Any information that is privy to any of the players pertains only to utilities. In all realizations of a Bayesian game, the number of players and their actions are identical. • Players maintain beliefs about the game (i.e., about utilities) in the form of a probability distribution over types. Prior to receiving any private information, this probability distribution is common knowledge: i.e., it is a common prior. After receiving private information, players conditional on this information to update their beliefs. As a consequence of the com- mon prior assumption, any differences in beliefs can be attributed entirely to differences in information. Rational players are again assumed to maximize their utility. But further, they are assumed to update their beliefs when they obtain new information via Bayes’ rule. Thus, in a Bayesian game, in addition to players, actions, and utilities, there is a type space T = ∏i2[n] Ti, where Ti is the type space of player i.There is also a common prior F, which is a probability distribution over type profiles. -
Reinterpreting Mixed Strategy Equilibria: a Unification Of
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Research Papers in Economics Reinterpreting Mixed Strategy Equilibria: A Unification of the Classical and Bayesian Views∗ Philip J. Reny Arthur J. Robson Department of Economics Department of Economics University of Chicago Simon Fraser University September, 2003 Abstract We provide a new interpretation of mixed strategy equilibria that incor- porates both von Neumann and Morgenstern’s classical concealment role of mixing as well as the more recent Bayesian view originating with Harsanyi. For any two-person game, G, we consider an incomplete information game, , in which each player’s type is the probability he assigns to the event thatIG his mixed strategy in G is “found out” by his opponent. We show that, generically, any regular equilibrium of G can be approximated by an equilibrium of in which almost every type of each player is strictly opti- mizing. This leadsIG us to interpret i’s equilibrium mixed strategy in G as a combination of deliberate randomization by i together with uncertainty on j’s part about which randomization i will employ. We also show that such randomization is not unusual: For example, i’s randomization is nondegen- erate whenever the support of an equilibrium contains cyclic best replies. ∗We wish to thank Drew Fudenberg, Motty Perry and Hugo Sonnenschein for very helpful comments. We are especially grateful to Bob Aumann for a stimulating discussion of the litera- ture on mixed strategies that prompted us to fit our work into that literature and ultimately led us to the interpretation we provide here, and to Hari Govindan whose detailed suggestions per- mitted a substantial simplification of the proof of our main result. -
Outline for Static Games of Complete Information I
Outline for Static Games of Complete Information I. Definition of a game II. Examples III. Definition of Nash equilibrium IV. Examples, continued V. Iterated elimination of dominated strategies VI. Mixed strategies VII. Existence theorem on Nash equilibria VIII. The Hotelling model and extensions Copyright © 2004 by Lawrence M. Ausubel Definition: An n-player, static game of complete information consists of an n-tuple of strategy sets and an n-tuple of payoff functions, denoted by G = {S1, … , Sn; u1, … , un} Si, the strategy set of player i, is the set of all permissible moves for player i. We write si ∈ Si for one of player i’s strategies. ui, the payoff function of player i, is the utility, profit, etc. for player i, and depends on the strategies chosen by all the players: ui(s1, … , sn). Example: Prisoners’ Dilemma Prisoner II Remain Confess Silent Remain –1 , –1 –5 , 0 Prisoner Silent I Confess 0, –5 –4 ,–4 Example: Battle of the Sexes F Boxing Ballet Boxing 2, 1 0, 0 M Ballet 0, 0 1, 2 Definition: A Nash equilibrium of G (in pure strategies) consists of a strategy for every player with the property that no player can improve her payoff by unilaterally deviating: (s1*, … , sn*) with the property that, for every player i: ui (s1*, … , si–1*, si*, si+1*, … , sn*) ≥ ui (s1*, … , si–1*, si, si+1*, … , sn*) for all si ∈ Si. Equivalently, a Nash equilibrium is a mutual best response. That is, for every player i, si* is a solution to: ***** susssssiiiiin∈arg max{ (111 , ... ,−+ , , , ... , )} sSii∈ Example: Prisoners’ Dilemma Prisoner II -
Evolution in Bayesian Games I: Theory*
Evolution in Bayesian Games I: Theory* Jeffrey C. Ely Department of Economics Boston University 270 Bay State Road Boston, MA 02215 [email protected] William H. Sandholm Department of Economics University of Wisconsin 1180 Observatory Drive Madison, WI 53706 [email protected] www.ssc.wisc.edu/~whs June 18, 2004 * We thank Drew Fudenberg, Josef Hofbauer, Larry Samuelson, and Jörgen Weibull, two anonymous referees and an anonymous Associate Editor, and seminar audiences at the Stockholm School of Economics and at Wisconsin for their comments. Results in this paper were previously circulated under the title “Evolution with Diverse Preferences”. Financial support from NSF Grants SBR-9810787 and SES-0092145 is gratefully acknowledged. Abstract We introduce best response dynamics for settings where agents' preferences are diverse. Under these dynamics, which are defined on the space of Bayesian strategies, rest points and Bayesian Nash equilibria are identical. We prove the existence and uniqueness of solution trajectories to these dynamics, and provide methods of analyzing the dynamics based on aggregation. JEL Classification: C72, C73 1. Introduction We study best response dynamics for populations with diverse preferences. The state variables for these dynamics are Bayesian strategies: that is, maps from preferences to distributions over actions. We prove the existence, uniqueness, and continuity of solutions of these dynamics, and show that the rest points of the dynamics are the Bayesian equilibria of the underlying game. We then characterize the dynamic stability of Bayesian equilibria in terms of aggregate dynamics defined on the simplex, making it possible to evaluate stability using standard dynamical systems techniques. We offer three motivations for this study.