Coordination Games
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The Determinants of Efficient Behavior in Coordination Games
The Determinants of Efficient Behavior in Coordination Games Pedro Dal B´o Guillaume R. Fr´echette Jeongbin Kim* Brown University & NBER New York University Caltech May 20, 2020 Abstract We study the determinants of efficient behavior in stag hunt games (2x2 symmetric coordination games with Pareto ranked equilibria) using both data from eight previous experiments on stag hunt games and data from a new experiment which allows for a more systematic variation of parameters. In line with the previous experimental literature, we find that subjects do not necessarily play the efficient action (stag). While the rate of stag is greater when stag is risk dominant, we find that the equilibrium selection criterion of risk dominance is neither a necessary nor sufficient condition for a majority of subjects to choose the efficient action. We do find that an increase in the size of the basin of attraction of stag results in an increase in efficient behavior. We show that the results are robust to controlling for risk preferences. JEL codes: C9, D7. Keywords: Coordination games, strategic uncertainty, equilibrium selection. *We thank all the authors who made available the data that we use from previous articles, Hui Wen Ng for her assistance running experiments and Anna Aizer for useful comments. 1 1 Introduction The study of coordination games has a long history as many situations of interest present a coordination component: for example, the choice of technologies that require a threshold number of users to be sustainable, currency attacks, bank runs, asset price bubbles, cooper- ation in repeated games, etc. In such examples, agents may face strategic uncertainty; that is, they may be uncertain about how the other agents will respond to the multiplicity of equilibria, even when they have complete information about the environment. -
Economics 211: Dynamic Games by David K
Economics 211: Dynamic Games by David K. Levine Last modified: January 6, 1999 © This document is copyrighted by the author. You may freely reproduce and distribute it electronically or in print, provided it is distributed in its entirety, including this copyright notice. Basic Concepts of Game Theory and Equilibrium Course Slides Source: \Docs\Annual\99\class\grad\basnote7.doc A Finite Game an N player game iN= 1K PS() are probability measure on S finite strategy spaces σ ∈≡Σ iiPS() i are mixed strategies ∈≡×N sSi=1 Si are the strategy profiles σ∈ΣΣ ≡×N i=1 i ∈≡× other useful notation s−−iijijS ≠S σ ∈≡×ΣΣ −−iijij ≠ usi () payoff or utility N uuss()σσ≡ ∑ ()∏ ( ) is expected iijjsS∈ j=1 utility 2 Dominant Strategies σ σ i weakly (strongly) dominates 'i if σσ≥> usiii(,−− )()( u i' i,) s i with at least one strict Nash Equilibrium players can anticipate on another’s strategies σ is a Nash equilibrium profile if for each iN∈1,K uu()σ = maxσ (σ' ,)σ− iiii'i Theorem: a Nash equilibrium exists in a finite game this is more or less why Kakutani’s fixed point theorem was invented σ σ Bi () is the set of best responses of i to −i , and is UHC convex valued This theorem fails in pure strategies: consider matching pennies 3 Some Classic Simultaneous Move Games Coordination Game RL U1,10,0 D0,01,1 three equilibria (U,R) (D,L) (.5U,.5L) too many equilibria?? Coordination Game RL U 2,2 -10,0 D 0,-10 1,1 risk dominance: indifference between U,D −−=− 21011ppp222()() == 13pp22 11,/ 11 13 if U,R opponent must play equilibrium w/ 11/13 if D,L opponent must -
Collusion Constrained Equilibrium
Theoretical Economics 13 (2018), 307–340 1555-7561/20180307 Collusion constrained equilibrium Rohan Dutta Department of Economics, McGill University David K. Levine Department of Economics, European University Institute and Department of Economics, Washington University in Saint Louis Salvatore Modica Department of Economics, Università di Palermo We study collusion within groups in noncooperative games. The primitives are the preferences of the players, their assignment to nonoverlapping groups, and the goals of the groups. Our notion of collusion is that a group coordinates the play of its members among different incentive compatible plans to best achieve its goals. Unfortunately, equilibria that meet this requirement need not exist. We instead introduce the weaker notion of collusion constrained equilibrium. This al- lows groups to put positive probability on alternatives that are suboptimal for the group in certain razor’s edge cases where the set of incentive compatible plans changes discontinuously. These collusion constrained equilibria exist and are a subset of the correlated equilibria of the underlying game. We examine four per- turbations of the underlying game. In each case,we show that equilibria in which groups choose the best alternative exist and that limits of these equilibria lead to collusion constrained equilibria. We also show that for a sufficiently broad class of perturbations, every collusion constrained equilibrium arises as such a limit. We give an application to a voter participation game that shows how collusion constraints may be socially costly. Keywords. Collusion, organization, group. JEL classification. C72, D70. 1. Introduction As the literature on collective action (for example, Olson 1965) emphasizes, groups often behave collusively while the preferences of individual group members limit the possi- Rohan Dutta: [email protected] David K. -
Using Risk Dominance Strategy in Poker
Journal of Information Hiding and Multimedia Signal Processing ©2014 ISSN 2073-4212 Ubiquitous International Volume 5, Number 3, July 2014 Using Risk Dominance Strategy in Poker J.J. Zhang Department of ShenZhen Graduate School Harbin Institute of Technology Shenzhen Applied Technology Engineering Laboratory for Internet Multimedia Application HIT Part of XiLi University Town, NanShan, 518055, ShenZhen, GuangDong [email protected] X. Wang Department of ShenZhen Graduate School Harbin Institute of Technology Public Service Platform of Mobile Internet Application Security Industry HIT Part of XiLi University Town, NanShan, 518055, ShenZhen, GuangDong [email protected] L. Yao, J.P. Li and X.D. Shen Department of ShenZhen Graduate School Harbin Institute of Technology HIT Part of XiLi University Town, NanShan, 518055, ShenZhen, GuangDong [email protected]; [email protected]; [email protected] Received March, 2014; revised April, 2014 Abstract. Risk dominance strategy is a complementary part of game theory decision strategy besides payoff dominance. It is widely used in decision of economic behavior and other game conditions with risk characters. In research of imperfect information games, the rationality of risk dominance strategy has been proved while it is also wildly adopted by human players. In this paper, a decision model guided by risk dominance is introduced. The novel model provides poker agents of rational strategies which is relative but not equals simply decision of “bluffing or no". Neural networks and specified probability tables are applied to improve its performance. In our experiments, agent with the novel model shows an improved performance when playing against our former version of poker agent named HITSZ CS 13 which participated Annual Computer Poker Competition of 2013. -
Game Theory Chris Georges Some Examples of 2X2 Games Here Are
Game Theory Chris Georges Some Examples of 2x2 Games Here are some widely used stylized 2x2 normal form games (two players, two strategies each). The ranking of the payoffs is what distinguishes the games, rather than the actual values of these payoffs. I will use Watson’s numbers here (see e.g., p. 31). Coordination game: two friends have agreed to meet on campus, but didn’t resolve where. They had narrowed it down to two possible choices: library or pub. Here are two versions. Player 2 Library Pub Player 1 Library 1,1 0,0 Pub 0,0 1,1 Player 2 Library Pub Player 1 Library 2,2 0,0 Pub 0,0 1,1 Battle of the Sexes: This is an asymmetric coordination game. A couple is trying to agree on what to do this evening. They have narrowed the choice down to two concerts: Nicki Minaj or Justin Bieber. Each prefers one over the other, but prefers either to doing nothing. If they disagree, they do nothing. Possible metaphor for bargaining (strategies are high wage, low wage: if disagreement we get a strike (0,0)) or agreement between two firms on a technology standard. Notice that this is a coordination game in which the two players are of different types (have different preferences). Player 2 NM JB Player 1 NM 2,1 0,0 JB 0,0 1,2 Matching Pennies: coordination is good for one player and bad for the other. Player 1 wins if the strategies match, player 2 wins if they don’t match. -
Evolutionary Game Theory: Why Equilibrium and Which Equilibrium
Evolutionary Game Theory: Why Equilibrium and Which Equilibrium Hamid Sabourianand Wei-Torng Juangy November 5, 2007 1 Introduction Two questions central to the foundations of game theory are (Samuelson 2002): (i) Why equilibrium? Should we expect Nash equilibrium play (players choosing best response strategies to the choices of others)? (ii) If so, which equilibrium? Which of the many Nash equilibria that arise in most games should we expect? To address the …rst question the classical game theory approach employs assumptions that agents are rational and they all have common knowledge of such rationality and beliefs. For a variety of reasons there has been wide dissatisfaction with such an approach. One major concern has to do with the plausibility and necessity of rationality and the common knowledge of rationality and beliefs. Are players fully rational, having unbounded com- puting ability and never making mistakes? Clearly, they are not. Moreover, the assumption of common (knowledge of) beliefs begs another question as to how players come to have common (knowledge of) beliefs. As we shall see in later discussion, under a very broad range of situations, a system may reach or converge to an equilibrium even when players are not fully rational (boundedly rational) and are not well-informed. As a result, it may be, as School of Economics, Mathematics and Statistics, Birkbeck College Univerity of Lon- don and Faculty of Economics, University of Cambridge yInstitute of Economics, Academia Sinica, Taipei 115, Taiwan 1 Samuelson (1997) points out, that an equilibrium appears not because agents are rational, “but rather agents appear rational because an equilibrium has been reached.” The classical approach has also not been able to provide a satisfactory so- lution to the second question on selection among multiple equilibria that com- monly arise. -
Coordination Games on Dynamical Networks
Games 2010, 1, 242-261; doi:10.3390/g1030242 OPEN ACCESS games ISSN 2073-4336 www.mdpi.com/journal/games Article Coordination Games on Dynamical Networks Marco Tomassini and Enea Pestelacci ? Information Systems Institute, Faculty of Business and Economics, University of Lausanne, CH-1015 Lausanne, Switzerland; E-Mail: [email protected] ? Author to whom correspondence should be addressed; E-Mail: [email protected]; Tel.: +41-21-692-3583; Fax: +41-21-692-3585. Received: 8 June 2010; in revised form: 7 July 2010 / Accepted: 28 July 2010 / Published: 29 July 2010 Abstract: We propose a model in which agents of a population interacting according to a network of contacts play games of coordination with each other and can also dynamically break and redirect links to neighbors if they are unsatisfied. As a result, there is co-evolution of strategies in the population and of the graph that represents the network of contacts. We apply the model to the class of pure and general coordination games. For pure coordination games, the networks co-evolve towards the polarization of different strategies. In the case of general coordination games our results show that the possibility of refusing neighbors and choosing different partners increases the success rate of the Pareto-dominant equilibrium. Keywords: evolutionary game theory; coordination games; games on dynamical networks; co-evolution 1. Introduction The purpose of Game Theory [1] is to describe situations in which two or more agents or entities may pursue different views about what is to be considered best by each of them. In other words, Game Theory, or at least the non-cooperative part of it, strives to describe what the agents’ rational decisions should be in such conflicting situations. -
Restoring Fun to Game Theory
Restoring Fun to Game Theory Avinash Dixit Abstract: The author suggests methods for teaching game theory at an introduc- tory level, using interactive games to be played in the classroom or in computer clusters, clips from movies to be screened and discussed, and excerpts from novels and historical books to be read and discussed. JEL codes: A22, C70 Game theory starts with an unfair advantage over most other scientific subjects—it is applicable to numerous interesting and thought-provoking aspects of decision- making in economics, business, politics, social interactions, and indeed to much of everyday life, making it automatically appealing to students. However, too many teachers and textbook authors lose this advantage by treating the subject in such an abstract and formal way that the students’ eyes glaze over. Even the interests of the abstract theorists will be better served if introductory courses are motivated using examples and classroom games that engage the students’ interest and encourage them to go on to more advanced courses. This will create larger audiences for the abstract game theorists; then they can teach students the mathematics and the rigor that are surely important aspects of the subject at the higher levels. Game theory has become a part of the basic framework of economics, along with, or even replacing in many contexts, the traditional supply-demand frame- work in partial and general equilibrium. Therefore economics students should be introduced to game theory right at the beginning of their studies. Teachers of eco- nomics usually introduce game theory by using economics applications; Cournot duopoly is the favorite vehicle. -
Minimax Regret and Deviations Form Nash Equilibrium
Minmax regret and deviations from Nash Equilibrium Michal Lewandowski∗ December 10, 2019 Abstract We build upon Goeree and Holt [American Economic Review, 91 (5) (2001), 1402-1422] and show that the departures from Nash Equilibrium predictions observed in their experiment on static games of complete in- formation can be explained by minimizing the maximum regret. Keywords: minmax regret, Nash equilibrium JEL Classification Numbers: C7 1 Introduction 1.1 Motivating examples Game theory predictions are sometimes counter-intuitive. Below, we give three examples. First, consider the traveler's dilemma game of Basu (1994). An airline loses two identical suitcases that belong to two different travelers. The airline worker talks to the travelers separately asking them to report the value of their case between $2 and $100. If both tell the same amount, each gets this amount. If one amount is smaller, then each of them will get this amount with either a bonus or a malus: the traveler who chose the smaller amount will get $2 extra; the other traveler will have to pay $2 penalty. Intuitively, reporting a value that is a little bit below $100 seems a good strategy in this game. This is so, because reporting high value gives you a chance of receiving large payoff without risking much { compared to reporting lower values the maximum possible loss is $4, i.e. the difference between getting the bonus and getting the malus. Bidding a little below instead of exactly $100 ∗Warsaw School of Economics, [email protected] Minmax regret strategies Michal Lewandowski Figure 1: Asymmetric matching pennies LR T 7; 0 0; 1 B 0; 1 1; 0 Figure 2: Choose an effort games Game A Game B LR LR T 2; 2 −3; 1 T 5; 5 0; 1 B 1; −3 1; 1 B 1; 0 1; 1 avoids choosing a strictly dominant action. -
Chapter 10 Game Theory: Inside Oligopoly
Managerial Economics & Business Strategy Chapter 10 Game Theory: Inside Oligopoly McGraw-Hill/Irwin Copyright © 2010 by the McGraw-Hill Companies, Inc. All rights reserved. Overview I. Introduction to Game Theory II. Simultaneous-Move, One-Shot Games III. Infinitely Repeated Games IV. Finitely Repeated Games V. Multistage Games 10-2 Game Environments Players’ planned decisions are called strategies. Payoffs to players are the profits or losses resulting from strategies. Order of play is important: – Simultaneous-move game: each player makes decisions with knowledge of other players’ decisions. – Sequential-move game: one player observes its rival’s move prior to selecting a strategy. Frequency of rival interaction – One-shot game: game is played once. – Repeated game: game is played more than once; either a finite or infinite number of interactions. 10-3 Simultaneous-Move, One-Shot Games: Normal Form Game A Normal Form Game consists of: – Set of players i ∈ {1, 2, … n} where n is a finite number. – Each players strategy set or feasible actions consist of a finite number of strategies. • Player 1’s strategies are S 1 = {a, b, c, …}. • Player 2’s strategies are S2 = {A, B, C, …}. – Payoffs. • Player 1’s payoff: π1(a,B) = 11. • Player 2’s payoff: π2(b,C) = 12. 10-4 A Normal Form Game Player 2 Strategy ABC a 12 ,11 11 ,12 14 ,13 b 11 ,10 10 ,11 12 ,12 Player 1 Player c 10 ,15 10 ,13 13 ,14 10-5 Normal Form Game: Scenario Analysis Suppose 1 thinks 2 will choose “A”. Player 2 Strategy ABC a 12 ,11 11 ,12 14 ,13 b 11 ,10 10 ,11 12 ,12 Player 1 Player c 10 ,15 10 ,13 13 ,14 10-6 Normal Form Game: Scenario Analysis Then 1 should choose “a”. -
Prisoners' Other Dilemma
DISCUSSION PAPER SERIES No. 3856 PRISONERS’ OTHER DILEMMA Matthias Blonski and Giancarlo Spagnolo INDUSTRIAL ORGANIZATION ABCD www.cepr.org Available online at: www.cepr.org/pubs/dps/DP3856.asp www.ssrn.com/xxx/xxx/xxx ISSN 0265-8003 PRISONERS’ OTHER DILEMMA Matthias Blonski, Universität Mannheim Giancarlo Spagnolo, Universität Mannheim and CEPR Discussion Paper No. 3856 April 2003 Centre for Economic Policy Research 90–98 Goswell Rd, London EC1V 7RR, UK Tel: (44 20) 7878 2900, Fax: (44 20) 7878 2999 Email: [email protected], Website: www.cepr.org This Discussion Paper is issued under the auspices of the Centre’s research programme in INDUSTRIAL ORGANIZATION. Any opinions expressed here are those of the author(s) and not those of the Centre for Economic Policy Research. Research disseminated by CEPR may include views on policy, but the Centre itself takes no institutional policy positions. The Centre for Economic Policy Research was established in 1983 as a private educational charity, to promote independent analysis and public discussion of open economies and the relations among them. It is pluralist and non-partisan, bringing economic research to bear on the analysis of medium- and long-run policy questions. Institutional (core) finance for the Centre has been provided through major grants from the Economic and Social Research Council, under which an ESRC Resource Centre operates within CEPR; the Esmée Fairbairn Charitable Trust; and the Bank of England. These organizations do not give prior review to the Centre’s publications, nor do they necessarily endorse the views expressed therein. These Discussion Papers often represent preliminary or incomplete work, circulated to encourage discussion and comment. -
Evolutionary Game Theory: a Renaissance
games Review Evolutionary Game Theory: A Renaissance Jonathan Newton ID Institute of Economic Research, Kyoto University, Kyoto 606-8501, Japan; [email protected] Received: 23 April 2018; Accepted: 15 May 2018; Published: 24 May 2018 Abstract: Economic agents are not always rational or farsighted and can make decisions according to simple behavioral rules that vary according to situation and can be studied using the tools of evolutionary game theory. Furthermore, such behavioral rules are themselves subject to evolutionary forces. Paying particular attention to the work of young researchers, this essay surveys the progress made over the last decade towards understanding these phenomena, and discusses open research topics of importance to economics and the broader social sciences. Keywords: evolution; game theory; dynamics; agency; assortativity; culture; distributed systems JEL Classification: C73 Table of contents 1 Introduction p. 2 Shadow of Nash Equilibrium Renaissance Structure of the Survey · · 2 Agency—Who Makes Decisions? p. 3 Methodology Implications Evolution of Collective Agency Links between Individual & · · · Collective Agency 3 Assortativity—With Whom Does Interaction Occur? p. 10 Assortativity & Preferences Evolution of Assortativity Generalized Matching Conditional · · · Dissociation Network Formation · 4 Evolution of Behavior p. 17 Traits Conventions—Culture in Society Culture in Individuals Culture in Individuals · · · & Society 5 Economic Applications p. 29 Macroeconomics, Market Selection & Finance Industrial Organization · 6 The Evolutionary Nash Program p. 30 Recontracting & Nash Demand Games TU Matching NTU Matching Bargaining Solutions & · · · Coordination Games 7 Behavioral Dynamics p. 36 Reinforcement Learning Imitation Best Experienced Payoff Dynamics Best & Better Response · · · Continuous Strategy Sets Completely Uncoupled · · 8 General Methodology p. 44 Perturbed Dynamics Further Stability Results Further Convergence Results Distributed · · · control Software and Simulations · 9 Empirics p.