The Value of a Coordination Game∗ Preliminary and Incomplete

Total Page:16

File Type:pdf, Size:1020Kb

The Value of a Coordination Game∗ Preliminary and Incomplete The Value of a Coordination Game∗ Preliminary and Incomplete Willemien Ketsy Alvaro Sandroniz February 19, 2018 Abstract The value of a game is what a player can expect to receive a priori from playing the game. An important intuition is that the value of a game need not increase with (ex post) payoffs when the game has multiple Nash equilibrium. This is the case if an improvement in payoffs increases strategic uncertainty by reducing the payoff saliency of one of the equilibria. To model this, we build on insights from psychology on how players resolve strategic uncertainty. We assign a unique value to symmetric two-player, two-action coordination games and show that the value changes non-monotonically with payoffs. ∗We thank Larry Blume, Peter Hammond, Stephen Hansen, Wouter Kager, and David Schmeidler for helpful comments and stimulating discussions. yDepartment of Economics, University of Oxford. E-mail: [email protected]. zKellogg School of Management, Northwestern University. E-mail: [email protected] 1 1 Introduction Say that the value to an agent of an uncertain prospect is what the agent can expect a priori. That is, the value of a game to a player is the ex ante expected payoff from playing the game. The question of how the value depends on economic primitives is of obvious economic importance. For example, when designing institutions, it is important to know what payoffs players can expect to receive when they interact under different institutional constraints. Likewise, when advising a player whether or not to participate in a game (possibly at a cost), it is important to know the expected payoff to the player when he enters. In settings where economic primitives uniquely pin down behavior, defining the value of a game is simple. To give an example, in a standard principal-agent problem where the agent's wage is made contingent on the profit to incentivize effort, it is straightforward to determine what the contract is worth to both parties. But it is less clear how to define the value for games with multiple equilibria. For two-player zero-sum games, von Neumann and Morgenstern(1944) have shown that the value is uniquely determined even if the game has multiple equilibria, as all equilibria yield the same payoff. However, their classic result does not extend to general games. This leaves open the question how to evaluate the value of a game with multiple equilibria beyond two-player zero-sum games. In this paper, we study the value of (simultaneous-move, complete-information) coordi- nation games. Coordination games are simple in that they abstract away from incentive problems, yet they are nontrivial in that payoffs do not uniquely determine behavior. And while coordination games are admittedly stylized, they form the basis for understanding the general problem of how agents can coordinate their actions. This is important for, e.g., un- derstanding how bargainers decide on a split of a pie or how long-term cooperation can be sustained by mutually understood punishment strategies. Despite the simple nature of coordination games, understanding how their value depends on economic primitives is a vexing problem. In his seminal work, Thomas Schelling(1960, pp. 97{98) already observed that \It is noteworthy that traditional game theory does not assign a \value" to [coordination games]: how well people [coordinate] is something that, though hopefully amenable to systematic analysis, cannot be discovered by reasoning a priori. This corner of game theory is inherently dependent on empirical evidence." Since the publication of Schelling's work, a large experimental literature on coordination games has produced valuable insights on which a theory of the value of coordination games can be based. To illustrate, consider the following game: 2 IN I 10; 10 2; 6 N 6; 2 6; 6 Each player has two actions, Invest (denoted I) and Not Invest (N). The game has two strict Nash equilibria: one in which both players invest, and one in which both players do not invest. At first sight, it might seem obvious that both players will invest { after all, if both players invest, then they receive 10; if neither invests, they receive 6 {, so that the value of the game is 10 to both players. However, in the one shot game, only 60% of the players invests (Schmidt, Shupp, Walker, and Ostrom, 2003).1 A theory of the value of coordination games must therefore account for the possibility of coordination failure: players may fail to coordinate on the Nash equilibrium that would give them the highest payoff. A second striking feature is that players may not just fail to coordinate on the \best" Nash equilibrium; they may fail to coordinate at all. Indeed, while a significant proportion of subjects do not invest, some do, and many end up choosing a different action from their opponent's. A theory of the value of coordination games must therefore also account for the possibility of miscoordination, that is, non-Nash behavior associated with the failure to coordinate on a Nash equilibrium. A third striking feature is that increasing the (ex-post) payoffs in a game may not make players better off. Consider the following game: IN I 10; 10 2; 8 N 8; 2 8; 8 In this game, the ex-post payoffs are (weakly) higher than in the original game: the payoffs to investing are the same as before while the payoffs to not investing have increased. Yet, players are not necessary better off in this game: the proportion of subjects who invest falls to 40% (Schmidt, Shupp, Walker, and Ostrom, 2003). Intuitively, the increase in the payoffs makes it less attractive to invest. This increases strategic uncertainty by reducing the payoff saliency of the Pareto-dominant Nash equilibrium (i.e. (I; I)). An increase in the payoffs to not investing thus has an indirect strategic effect on the value by reducing the probability that 1Also see Van Huyck, Battalio, and Beil(1990), Cooper, DeJong, Forsythe, and Ross(1990, 1992), and Straub(1995). In general, there is significant coordination failure both when the game is played only once and when it is played repeatedly. 3 players coordinate on the Pareto-dominant Nash equilibrium. There is also a direct payoff effect on the value: an increase in the ex-post payoffs to not investing makes players better off if they do not invest. When the payoffs to not investing are small, then the indirect strategic effect dominates because players choose to invest with high probability. As the payoffs to not investing increase, the direct payoff effect becomes more important as the probability that players do not invest increases. Hence, any theory of the value of coordination game must allow for the value to be non-monotonic in ex-post payoffs. The value of the game thus critically depends on the strategic uncertainty that players face. A standard approach in game theory is to assume away strategic uncertainty by selecting a Nash equilibrium. But, as we argue below, Nash equilibrium or other standard approaches cannot account for the empirical regularities described above. We therefore depart from stan- dard game theory in that we explicitly model the process by which players reason about others to resolve any strategic uncertainty. As observed by Schelling(1960), when a player is uncertain about another player's action, \[the] objective is to make contact with the other player through some imaginative process of introspection" (p. 96). By modeling the process by which players reach such a \meeting of the minds," we obtain a unique prediction for any coordination game, despite the fact that these games have multiple Nash equilibria and that every action is rationalizable (Kets and Sandroni, 2015). Importantly, the outcome that is selected by the introspective process depends crucially on the interplay between strategic uncertainty and payoffs. This allows us to capture the empirical regularities described above. When the payoffs to investing are sufficiently high (relative to the payoffs to not investing), investing is uniquely salient. In that case, strategic uncertainty is negligible and the introspective process selects the pure Nash equilibrium in which both players invest. But, as the relative payoffs to not investing increase, players begin to face significant strategic uncertainty and the rate of miscoordination increases. This indirect strategic effect leads to a fall in the value. As the payoffs to not investing increase further, players begin to coordinate on the pure Nash equilibrium in which no player invests. This reduces the rate of miscoordination. But while the direct payoff effect of an increase in ex-post payoffs is obviously positive (and can be significant), coordination failure makes that the value of the game is lower than in the case where investing is uniquely payoff salient. While these predictions are intuitive, they are difficult to obtain using existing methods as we argue in Section 5.2. In short, existing models, including Nash equilibrium, equilibrium refinements, and behavioral models, either do not capture miscoordination or coordination failure, or fail to assign a unique value to the game. As a result, existing methods cannot account for the empirical regularities described above. Coordination problems are a central feature of economic environments with nontrivial 4 strategic uncertainty and have been studied in a range of applications, including economies with technological complementarities (Bryant, 1983), technology adoption (Katz and Shapiro, 1986), search and matching (Diamond, 1982), currency crises (Obstfeld, 1996), and bank runs (Diamond and Dybvig, 1983). A central theme in the literature is that even when there is an equilibrium that all players prefer, they may fail to coordinate on it. This is especially the case when the payoff difference between the Pareto-dominant Nash equilibrium and other equilibria is limited.
Recommended publications
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • 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”.
    [Show full text]
  • A Spatial Simulation of the Battle of the Sexes
    A Spatial Simulation of the Battle of the Sexes Master's thesis 27th August 2012 Student: Ivar H.J. Postma Primary supervisor: Dr. M.H.F. Wilkinson Secondary supervisor: Dr. H. Bekker A Spatial Simulation of the Battle of the Sexes Version 1.1 Ivar H. J. Postma Master’s thesis Computing Science School for Computing and Cognition Faculty of Mathematics and Natural Sciences University of Groningen The problem is all inside your head she said to me The answer is easy if you take it logically – Paul Simon, from: ‘50 Ways to Leave Your Lover’ vi Abstract In many species of animal, females require males to invest a substantial amount of time or effort in a relationship before mating. In evolutionary biology, the Battle of the Sexes is a game-theoretical model that tries to illustrate how this coy behaviour by females can survive over time. In its classical form of the game, the success of a specific mating strategy depends on the ratio of strategies of the opposite sex. This assumes that strategies are uniformly distributed. In other evolutionary games it has been shown that introducing a spatial dimension changes the out- come of the game. In a spatial game, the success of a strategy depends on the location. In a spatial version of the Battle of the Sexes it can be shown that the equilibrium that is present in the non-spatial game does not necessarily emerge when individuals only interact locally. A simu- lation also shows that the population oscillates around an equilibrium which was predicted when the non-spatial model was first coined and which was previously illustrated using a system of ordinary differential equations.
    [Show full text]
  • PSCI552 - Formal Theory (Graduate) Lecture Notes
    PSCI552 - Formal Theory (Graduate) Lecture Notes Dr. Jason S. Davis Spring 2020 Contents 1 Preliminaries 4 1.1 What is the role of theory in the social sciences? . .4 1.2 So why formalize that theory? And why take this course? . .4 2 Proofs 5 2.1 Introduction . .5 2.2 Logic . .5 2.3 What are we proving? . .6 2.4 Different proof strategies . .7 2.4.1 Direct . .7 2.4.2 Indirect/contradiction . .7 2.4.3 Induction . .8 3 Decision Theory 9 3.1 Preference Relations and Rationality . .9 3.2 Imposing Structure on Preferences . 11 3.3 Utility Functions . 13 3.4 Uncertainty, Expected Utility . 14 3.5 The Many Faces of Decreasing Returns . 15 3.5.1 Miscellaneous stuff . 17 3.6 Optimization . 17 3.7 Comparative Statics . 18 4 Game Theory 22 4.1 Preliminaries . 22 4.2 Static games of complete information . 23 4.3 The Concept of the Solution Concept . 24 4.3.1 Quick side note: Weak Dominance . 27 4.3.2 Quick side note: Weak Dominance . 27 4.4 Evaluating Outcomes/Normative Theory . 27 4.5 Best Response Correspondences . 28 4.6 Nash Equilibrium . 28 4.7 Mixed Strategies . 31 4.8 Extensive Form Games . 36 4.8.1 Introduction . 36 4.8.2 Imperfect Information . 39 4.8.3 Subgame Perfection . 40 4.8.4 Example Questions . 43 4.9 Static Games of Incomplete Information . 45 4.9.1 Learning . 45 4.9.2 Bayesian Nash Equilibrium . 46 4.10 Dynamic Games with Incomplete Information . 50 4.10.1 Perfect Bayesian Nash Equilibrium .
    [Show full text]
  • An Adaptive Learning Model in Coordination Games
    Games 2013, 4, 648-669; doi:10.3390/g4040648 OPEN ACCESS games ISSN 2073-4336 www.mdpi.com/journal/games Article An Adaptive Learning Model in Coordination Games Naoki Funai Department of Economics, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK; E-Mails: [email protected] or [email protected]; Tel.: +44-744-780-5832 Received: 15 September 2013; in revised form: 4 November 2013 / Accepted: 7 November 2013 / Published: 15 November 2013 Abstract: In this paper, we provide a theoretical prediction of the way in which adaptive players behave in the long run in normal form games with strict Nash equilibria. In the model, each player assigns subjective payoff assessments to his own actions, where the assessment of each action is a weighted average of its past payoffs, and chooses the action which has the highest assessment. After receiving a payoff, each player updates the assessment of his chosen action in an adaptive manner. We show almost sure convergence to a Nash equilibrium under one of the following conditions: (i) that, at any non-Nash equilibrium action profile, there exists a player who receives a payoff, which is less than his maximin payoff; (ii) that all non-Nash equilibrium action profiles give the same payoff. In particular, the convergence is shown in the following games: the battle of the sexes game, the stag hunt game and the first order statistic game. In the game of chicken and market entry games, players may end up playing the action profile, which consists of each player’s unique maximin action.
    [Show full text]
  • MS&E 246: Lecture 4 Mixed Strategies
    MS&E 246: Lecture 4 Mixed strategies Ramesh Johari January 18, 2007 Outline • Mixed strategies • Mixed strategy Nash equilibrium • Existence of Nash equilibrium • Examples • Discussion of Nash equilibrium Mixed strategies Notation: Given a set X, we let Δ(X) denote the set of all probability distributions on X. Given a strategy space Si for player i, the mixed strategies for player i are Δ(Si). Idea: a player can randomize over pure strategies. Mixed strategies How do we interpret mixed strategies? Note that players only play once; so mixed strategies reflect uncertainty about what the other player might play. Payoffs Suppose for each player i, pi is a mixed strategy for player i; i.e., it is a distribution on Si. We extend Πi by taking the expectation: Πi(p1,...,pN )= ··· p1(s1) ···pN (sN )Πi(s1,...,sN ) s1 S1 sN SN X∈ X∈ Mixed strategy Nash equilibrium Given a game (N, S1, …, SN, Π1, …, ΠN): Create a new game with N players, strategy spaces Δ(S1), …, Δ(SN), and expected payoffs Π1, …, ΠN. A mixed strategy Nash equilibrium is a Nash equilibrium of this new game. Mixed strategy Nash equilibrium Informally: All players can randomize over available strategies. In a mixed NE, player i’s mixed strategy must maximize his expected payoff, given all other player’s mixed strategies. Mixed strategy Nash equilibrium Key observations: (1) All our definitions -- dominated strategies, iterated strict dominance, rationalizability -- extend to mixed strategies. Note: any dominant strategy must be a pure strategy. Mixed strategy Nash equilibrium (2) We can extend the definition of best response set identically: Ri(p-i) is the set of mixed strategies for player i that maximize the expected payoff Πi (pi, p-i).
    [Show full text]
  • The Mathematics of Game Shows
    The Mathematics of Game Shows Frank Thorne May 1, 2018 These are the course notes for a class on The Mathematics of Game Shows which I taught at the University of South Carolina (through their Honors College) in Fall 2016, and again in Spring 2018. They are in the middle of revisions, being made as I teach the class a second time. Click here for the course website and syllabus: Link: The Mathematics of Game Shows { Course Website and Syllabus I welcome feedback from anyone who reads this (please e-mail me at thorne[at]math. sc.edu). The notes contain clickable internet links to clips from various game shows, hosted on the video sharing site Youtube (www.youtube.com). These materials are (presumably) all copyrighted, and as such they are subject to deletion. I have no control over this. Sorry! If you encounter a dead link I recommend searching Youtube for similar videos. The Price Is Right videos in particular appear to be ubiquitous. I would like to thank Bill Butterworth, Paul Dreyer, and all of my students for helpful feedback. I hope you enjoy reading these notes as much as I enjoyed writing them! Contents 1 Introduction 4 2 Probability 7 2.1 Sample Spaces and Events . .7 2.2 The Addition and Multiplication Rules . 14 2.3 Permutations and Factorials . 23 2.4 Exercises . 28 3 Expectation 33 3.1 Definitions and Examples . 33 3.2 Linearity of expectation . 44 3.3 Some classical examples . 47 1 3.4 Exercises . 52 3.5 Appendix: The Expected Value of Let 'em Roll .
    [Show full text]
  • MIT 14.16 S16 Strategy and Information Lecture Slides
    Strategy & Information Mihai Manea MIT What is Game Theory? Game Theory is the formal study of strategic interaction. In a strategic setting the actions of several agents are interdependent. Each agent’s outcome depends not only on his actions, but also on the actions of other agents. How to predict opponents’ play and respond optimally? Everything is a game. I poker, chess, soccer, driving, dating, stock market I advertising, setting prices, entering new markets, building a reputation I bargaining, partnerships, job market search and screening I designing contracts, auctions, insurance, environmental regulations I international relations, trade agreements, electoral campaigns Most modern economic research includes game theoretical elements. Eleven game theorists have won the economics Nobel Prize so far. Mihai Manea (MIT) Strategy & Information February 10, 2016 2 / 57 Brief History I Cournot (1838): quantity setting duopoly I Zermelo (1913): backward induction I von Neumann (1928), Borel (1938), von Neumann and Morgenstern (1944): zero-sum games I Flood and Dresher (1950): experiments I Nash (1950): equilibrium I Selten (1965): dynamic games I Harsanyi (1967): incomplete information I Akerlof (1970), Spence (1973): first applications I 1980s boom, continuing nowadays: repeated games, bargaining, reputation, equilibrium refinements, industrial organization, contract theory, mechanism/market design I 1990s: parallel development of behavioral economics I more recently: applications to computer science, political science, psychology, evolutionary
    [Show full text]