Equilibrium Computation in Normal Form Games
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CSC304 Lecture 4 Game Theory
CSC304 Lecture 4 Game Theory (Cost sharing & congestion games, Potential function, Braess’ paradox) CSC304 - Nisarg Shah 1 Recap • Nash equilibria (NE) ➢ No agent wants to change their strategy ➢ Guaranteed to exist if mixed strategies are allowed ➢ Could be multiple • Pure NE through best-response diagrams • Mixed NE through the indifference principle CSC304 - Nisarg Shah 2 Worst and Best Nash Equilibria • What can we say after we identify all Nash equilibria? ➢ Compute how “good” they are in the best/worst case • How do we measure “social good”? ➢ Game with only rewards? Higher total reward of players = more social good ➢ Game with only penalties? Lower total penalty to players = more social good ➢ Game with rewards and penalties? No clear consensus… CSC304 - Nisarg Shah 3 Price of Anarchy and Stability • Price of Anarchy (PoA) • Price of Stability (PoS) “Worst NE vs optimum” “Best NE vs optimum” Max total reward Max total reward Min total reward in any NE Max total reward in any NE or or Max total cost in any NE Min total cost in any NE Min total cost Min total cost PoA ≥ PoS ≥ 1 CSC304 - Nisarg Shah 4 Revisiting Stag-Hunt Hunter 2 Stag Hare Hunter 1 Stag (4 , 4) (0 , 2) Hare (2 , 0) (1 , 1) • Max total reward = 4 + 4 = 8 • Three equilibria ➢ (Stag, Stag) : Total reward = 8 ➢ (Hare, Hare) : Total reward = 2 1 2 1 2 ➢ ( Τ3 Stag – Τ3 Hare, Τ3 Stag – Τ3 Hare) 1 1 1 1 o Total reward = ∗ ∗ 8 + 1 − ∗ ∗ 2 ∈ (2,8) 3 3 3 3 • Price of stability? Price of anarchy? CSC304 - Nisarg Shah 5 Revisiting Prisoner’s Dilemma John Stay Silent Betray Sam -
Potential Games. Congestion Games. Price of Anarchy and Price of Stability
8803 Connections between Learning, Game Theory, and Optimization Maria-Florina Balcan Lecture 13: October 5, 2010 Reading: Algorithmic Game Theory book, Chapters 17, 18 and 19. Price of Anarchy and Price of Staility We assume a (finite) game with n players, where player i's set of possible strategies is Si. We let s = (s1; : : : ; sn) denote the (joint) vector of strategies selected by players in the space S = S1 × · · · × Sn of joint actions. The game assigns utilities ui : S ! R or costs ui : S ! R to any player i at any joint action s 2 S: any player maximizes his utility ui(s) or minimizes his cost ci(s). As we recall from the introductory lectures, any finite game has a mixed Nash equilibrium (NE), but a finite game may or may not have pure Nash equilibria. Today we focus on games with pure NE. Some NE are \better" than others, which we formalize via a social objective function f : S ! R. Two classic social objectives are: P sum social welfare f(s) = i ui(s) measures social welfare { we make sure that the av- erage satisfaction of the population is high maxmin social utility f(s) = mini ui(s) measures the satisfaction of the most unsatisfied player A social objective function quantifies the efficiency of each strategy profile. We can now measure how efficient a Nash equilibrium is in a specific game. Since a game may have many NE we have at least two natural measures, corresponding to the best and the worst NE. We first define the best possible solution in a game Definition 1. -
Lecture Notes
GRADUATE GAME THEORY LECTURE NOTES BY OMER TAMUZ California Institute of Technology 2018 Acknowledgments These lecture notes are partially adapted from Osborne and Rubinstein [29], Maschler, Solan and Zamir [23], lecture notes by Federico Echenique, and slides by Daron Acemoglu and Asu Ozdaglar. I am indebted to Seo Young (Silvia) Kim and Zhuofang Li for their help in finding and correcting many errors. Any comments or suggestions are welcome. 2 Contents 1 Extensive form games with perfect information 7 1.1 Tic-Tac-Toe ........................................ 7 1.2 The Sweet Fifteen Game ................................ 7 1.3 Chess ............................................ 7 1.4 Definition of extensive form games with perfect information ........... 10 1.5 The ultimatum game .................................. 10 1.6 Equilibria ......................................... 11 1.7 The centipede game ................................... 11 1.8 Subgames and subgame perfect equilibria ...................... 13 1.9 The dollar auction .................................... 14 1.10 Backward induction, Kuhn’s Theorem and a proof of Zermelo’s Theorem ... 15 2 Strategic form games 17 2.1 Definition ......................................... 17 2.2 Nash equilibria ...................................... 17 2.3 Classical examples .................................... 17 2.4 Dominated strategies .................................. 22 2.5 Repeated elimination of dominated strategies ................... 22 2.6 Dominant strategies .................................. -
Chapter 2 Equilibrium
Chapter 2 Equilibrium The theory of equilibrium attempts to predict what happens in a game when players be- have strategically. This is a central concept to this text as, in mechanism design, we are optimizing over games to find the games with good equilibria. Here, we review the most fundamental notions of equilibrium. They will all be static notions in that players are as- sumed to understand the game and will play once in the game. While such foreknowledge is certainly questionable, some justification can be derived from imagining the game in a dynamic setting where players can learn from past play. Readers should look elsewhere for formal justifications. This chapter reviews equilibrium in both complete and incomplete information games. As games of incomplete information are the most central to mechanism design, special at- tention will be paid to them. In particular, we will characterize equilibrium when the private information of each agent is single-dimensional and corresponds, for instance, to a value for receiving a good or service. We will show that auctions with the same equilibrium outcome have the same expected revenue. Using this so-called revenue equivalence we will describe how to solve for the equilibrium strategies of standard auctions in symmetric environments. Emphasis is placed on demonstrating the central theories of equilibrium and not on providing the most comprehensive or general results. For that readers are recommended to consult a game theory textbook. 2.1 Complete Information Games In games of compete information all players are assumed to know precisely the payoff struc- ture of all other players for all possible outcomes of the game. -
Potential Games and Competition in the Supply of Natural Resources By
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by ASU Digital Repository Potential Games and Competition in the Supply of Natural Resources by Robert H. Mamada A Dissertation Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy Approved March 2017 by the Graduate Supervisory Committee: Carlos Castillo-Chavez, Co-Chair Charles Perrings, Co-Chair Adam Lampert ARIZONA STATE UNIVERSITY May 2017 ABSTRACT This dissertation discusses the Cournot competition and competitions in the exploita- tion of common pool resources and its extension to the tragedy of the commons. I address these models by using potential games and inquire how these models reflect the real competitions for provisions of environmental resources. The Cournot models are dependent upon how many firms there are so that the resultant Cournot-Nash equilibrium is dependent upon the number of firms in oligopoly. But many studies do not take into account how the resultant Cournot-Nash equilibrium is sensitive to the change of the number of firms. Potential games can find out the outcome when the number of firms changes in addition to providing the \traditional" Cournot-Nash equilibrium when the number of firms is fixed. Hence, I use potential games to fill the gaps that exist in the studies of competitions in oligopoly and common pool resources and extend our knowledge in these topics. In specific, one of the rational conclusions from the Cournot model is that a firm’s best policy is to split into separate firms. In real life, we usually witness the other way around; i.e., several firms attempt to merge and enjoy the monopoly profit by restricting the amount of output and raising the price. -
Game-Based Verification and Synthesis
Downloaded from orbit.dtu.dk on: Sep 30, 2021 Game-based verification and synthesis Vester, Steen Publication date: 2016 Document Version Publisher's PDF, also known as Version of record Link back to DTU Orbit Citation (APA): Vester, S. (2016). Game-based verification and synthesis. Technical University of Denmark. DTU Compute PHD-2016 No. 414 General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. Users may download and print one copy of any publication from the public portal for the purpose of private study or research. You may not further distribute the material or use it for any profit-making activity or commercial gain You may freely distribute the URL identifying the publication in the public portal If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim. Ph.D. Thesis Doctor of Philosophy Game-based verification and synthesis Steen Vester Kongens Lyngby, Denmark 2016 PHD-2016-414 ISSN: 0909-3192 DTU Compute Department of Applied Mathematics and Computer Science Technical University of Denmark Richard Petersens Plads Building 324 2800 Kongens Lyngby, Denmark Phone +45 4525 3031 [email protected] www.compute.dtu.dk Summary Infinite-duration games provide a convenient way to model distributed, reactive and open systems in which several entities and an uncontrollable environment interact. -
Uniqueness and Stability in Symmetric Games: Theory and Applications
Uniqueness and stability in symmetric games: Theory and Applications Andreas M. Hefti∗ November 2013 Abstract This article develops a comparably simple approach towards uniqueness of pure- strategy equilibria in symmetric games with potentially many players by separating be- tween multiple symmetric equilibria and asymmetric equilibria. Our separation approach is useful in applications for investigating, for example, how different parameter constel- lations may affect the scope for multiple symmetric or asymmetric equilibria, or how the equilibrium set of higher-dimensional symmetric games depends on the nature of the strategies. Moreover, our approach is technically appealing as it reduces the complexity of the uniqueness-problem to a two-player game, boundary conditions are less critical compared to other standard procedures, and best-replies need not be everywhere differ- entiable. The article documents the usefulness of the separation approach with several examples, including applications to asymmetric games and to a two-dimensional price- advertising game, and discusses the relationship between stability and multiplicity of symmetric equilibria. Keywords: Symmetric Games, Uniqueness, Symmetric equilibrium, Stability, Indus- trial Organization JEL Classification: C62, C65, C72, D43, L13 ∗Author affiliation: University of Zurich, Department of Economics, Bluemlisalpstr. 10, CH-8006 Zurich. E-mail: [email protected], Phone: +41787354964. Part of the research was accomplished during a research stay at the Department of Economics, Harvard University, Littauer Center, 02138 Cambridge, USA. 1 1 Introduction Whether or not there is a unique (Nash) equilibrium is an interesting and important question in many game-theoretic settings. Many applications concentrate on games with identical players, as the equilibrium outcome of an ex-ante symmetric setting frequently is of self-interest, or comparably easy to handle analytically, especially in presence of more than two players. -
Online Learning and Game Theory
Online Learning and Game Theory Your guide: Avrim Blum Carnegie Mellon University [Algorithmic Economics Summer School 2012] Itinerary • Stop 1: Minimizing regret and combining advice. – Randomized Wtd Majority / Multiplicative Weights alg – Connections to minimax-optimality in zero-sum games • Stop 2: Bandits and Pricing – Online learning from limited feedback (bandit algs) – Use for online pricing/auction problems • Stop 3: Internal regret & Correlated equil – Internal regret minimization – Connections to correlated equilib. in general-sum games • Stop 4: Guiding dynamics to higher-quality equilibria – Helpful “nudging” of simple dynamics in potential games Stop 1: Minimizing regret and combining expert advice Consider the following setting… Each morning, you need to pick one of N possible routes to drive to work. Robots But traffic is different each day. R Us 32 min Not clear a priori which will be best. When you get there you find out how long your route took. (And maybe others too or maybe not.) Is there a strategy for picking routes so that in the long run, whatever the sequence of traffic patterns has been, you’ve done nearly as well as the best fixed route in hindsight? (In expectation, over internal randomness in the algorithm) Yes. “No-regret” algorithms for repeated decisions A bit more generally: Algorithm has N options. World chooses cost vector. Can view as matrix like this (maybe infinite # cols) World – life - fate Algorithm At each time step, algorithm picks row, life picks column. Alg pays cost for action chosen. Alg gets column as feedback (or just its own cost in the “bandit” model). Need to assume some bound on max cost. -
Potential Games with Continuous Player Sets
Potential Games with Continuous Player Sets William H. Sandholm* Department of Economics University of Wisconsin 1180 Observatory Drive Madison, WI 53706 [email protected] www.ssc.wisc.edu/~whs First Version: April 30, 1997 This Version: May 20, 2000 * This paper is based on Chapter 4 of my doctoral dissertation (Sandholm [29]). I thank an anonymous referee and Associate Editor, Josef Hofbauer, and seminar audiences at Caltech, Chicago, Columbia, Harvard (Kennedy), Michigan, Northwestern, Rochester, Washington University, and Wisconsin for their comments. I especially thank Eddie Dekel and Jeroen Swinkels for their advice and encouragement. Financial support from a State Farm Companies Foundation Dissertation Fellowship is gratefully acknowledged. Abstract We study potential games with continuous player sets, a class of games characterized by an externality symmetry condition. Examples of these games include random matching games with common payoffs and congestion games. We offer a simple description of equilibria which are locally stable under a broad class of evolutionary dynamics, and prove that behavior converges to Nash equilibrium from all initial conditions. W e consider a subclass of potential games in which evolution leads to efficient play. Finally, we show that the games studied here are the limits of convergent sequences of the finite player potential games studied by Monderer and Shapley [22]. JEL Classification Numbers: C72, C73, D62, R41. 1. Introduction Nash equilibrium is the cornerstone of non-cooperative game theory, providing a necessary condition for stable behavior among rational agents. Still, to justify the prediction of Nash equilibrium play, one must explain how players arrive at a Nash equilibrium; if equilibrium is not reached, the fact that it is self-sustaining becomes moot. -
Survey of the Computational Complexity of Finding a Nash Equilibrium
Survey of the Computational Complexity of Finding a Nash Equilibrium Valerie Ishida 22 April 2009 1 Abstract This survey reviews the complexity classes of interest for describing the problem of finding a Nash Equilibrium, the reductions that led to the conclusion that finding a Nash Equilibrium of a game in the normal form is PPAD-complete, and the reduction from a succinct game with a nice expected utility function to finding a Nash Equilibrium in a normal form game with two players. 2 Introduction 2.1 Motivation For many years it was unknown whether a Nash Equilibrium of a normal form game could be found in polynomial time. The motivation for being able to due so is to find game solutions that people are satisfied with. Consider an entertaining game that some friends are playing. If there is a set of strategies that constitutes and equilibrium each person will probably be satisfied. Likewise if financial games such as auctions could be solved in polynomial time in a way that all of the participates were satisfied that they could not do any better given the situation, then that would be great. There has been much progress made in the last fifty years toward finding the complexity of this problem. It turns out to be a hard problem unless PPAD = FP. This survey reviews the complexity classes of interest for describing the prob- lem of finding a Nash Equilibrium, the reductions that led to the conclusion that finding a Nash Equilibrium of a game in the normal form is PPAD-complete, and the reduction from a succinct game with a nice expected utility function to finding a Nash Equilibrium in a normal form game with two players. -
Equilibrium Computation in Normal Form Games
Tutorial Overview Game Theory Refresher Solution Concepts Computational Formulations Equilibrium Computation in Normal Form Games Costis Daskalakis & Kevin Leyton-Brown Part 1(a) Equilibrium Computation in Normal Form Games Costis Daskalakis & Kevin Leyton-Brown, Slide 1 Tutorial Overview Game Theory Refresher Solution Concepts Computational Formulations Overview 1 Plan of this Tutorial 2 Getting Our Bearings: A Quick Game Theory Refresher 3 Solution Concepts 4 Computational Formulations Equilibrium Computation in Normal Form Games Costis Daskalakis & Kevin Leyton-Brown, Slide 2 Tutorial Overview Game Theory Refresher Solution Concepts Computational Formulations Plan of this Tutorial This tutorial provides a broad introduction to the recent literature on the computation of equilibria of simultaneous-move games, weaving together both theoretical and applied viewpoints. It aims to explain recent results on: the complexity of equilibrium computation; representation and reasoning methods for compactly represented games. It also aims to be accessible to those having little experience with game theory. Our focus: the computational problem of identifying a Nash equilibrium in different game models. We will also more briefly consider -equilibria, correlated equilibria, pure-strategy Nash equilibria, and equilibria of two-player zero-sum games. Equilibrium Computation in Normal Form Games Costis Daskalakis & Kevin Leyton-Brown, Slide 3 Tutorial Overview Game Theory Refresher Solution Concepts Computational Formulations Part 1: Normal-Form Games -
Noisy Directional Learning and the Logit Equilibrium*
CSE: AR SJOE 011 Scand. J. of Economics 106(*), *–*, 2004 DOI: 10.1111/j.1467-9442.2004.000376.x Noisy Directional Learning and the Logit Equilibrium* Simon P. Anderson University of Virginia, Charlottesville, VA 22903-3328, USA [email protected] Jacob K. Goeree California Institute of Technology, Pasadena, CA 91125, USA [email protected] Charles A. Holt University of Virginia, Charlottesville, VA 22903-3328, USA [email protected] PROOF Abstract We specify a dynamic model in which agents adjust their decisions toward higher payoffs, subject to normal error. This process generates a probability distribution of players’ decisions that evolves over time according to the Fokker–Planck equation. The dynamic process is stable for all potential games, a class of payoff structures that includes several widely studied games. In equilibrium, the distributions that determine expected payoffs correspond to the distributions that arise from the logit function applied to those expected payoffs. This ‘‘logit equilibrium’’ forms a stochastic generalization of the Nash equilibrium and provides a possible explanation of anomalous laboratory data. ECTED Keywords: Bounded rationality; noisy directional learning; Fokker–Planck equation; potential games; logit equilibrium JEL classification: C62; C73 I. Introduction Small errors and shocks may have offsetting effects in some economic con- texts, in which case there is not much to be gained from an explicit analysis of stochasticNCORR elements. In other contexts, a small amount of randomness can * We gratefully acknowledge financial support from the National Science Foundation (SBR- 9818683U and SBR-0094800), the Alfred P. Sloan Foundation and the Dutch National Science Foundation (NWO-VICI 453.03.606).