Misrepresentation and Stability in the Marriage Problem
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Game Theory 2: Extensive-Form Games and Subgame Perfection
Game Theory 2: Extensive-Form Games and Subgame Perfection 1 / 26 Dynamics in Games How should we think of strategic interactions that occur in sequence? Who moves when? And what can they do at different points in time? How do people react to different histories? 2 / 26 Modeling Games with Dynamics Players Player function I Who moves when Terminal histories I Possible paths through the game Preferences over terminal histories 3 / 26 Strategies A strategy is a complete contingent plan Player i's strategy specifies her action choice at each point at which she could be called on to make a choice 4 / 26 An Example: International Crises Two countries (A and B) are competing over a piece of land that B occupies Country A decides whether to make a demand If Country A makes a demand, B can either acquiesce or fight a war If A does not make a demand, B keeps land (game ends) A's best outcome is Demand followed by Acquiesce, worst outcome is Demand and War B's best outcome is No Demand and worst outcome is Demand and War 5 / 26 An Example: International Crises A can choose: Demand (D) or No Demand (ND) B can choose: Fight a war (W ) or Acquiesce (A) Preferences uA(D; A) = 3 > uA(ND; A) = uA(ND; W ) = 2 > uA(D; W ) = 1 uB(ND; A) = uB(ND; W ) = 3 > uB(D; A) = 2 > uB(D; W ) = 1 How can we represent this scenario as a game (in strategic form)? 6 / 26 International Crisis Game: NE Country B WA D 1; 1 3X; 2X Country A ND 2X; 3X 2; 3X I Is there something funny here? I Is there something funny here? I Specifically, (ND; W )? I Is there something funny here? -
Lecture 4 Rationalizability & Nash Equilibrium Road
Lecture 4 Rationalizability & Nash Equilibrium 14.12 Game Theory Muhamet Yildiz Road Map 1. Strategies – completed 2. Quiz 3. Dominance 4. Dominant-strategy equilibrium 5. Rationalizability 6. Nash Equilibrium 1 Strategy A strategy of a player is a complete contingent-plan, determining which action he will take at each information set he is to move (including the information sets that will not be reached according to this strategy). Matching pennies with perfect information 2’s Strategies: HH = Head if 1 plays Head, 1 Head if 1 plays Tail; HT = Head if 1 plays Head, Head Tail Tail if 1 plays Tail; 2 TH = Tail if 1 plays Head, 2 Head if 1 plays Tail; head tail head tail TT = Tail if 1 plays Head, Tail if 1 plays Tail. (-1,1) (1,-1) (1,-1) (-1,1) 2 Matching pennies with perfect information 2 1 HH HT TH TT Head Tail Matching pennies with Imperfect information 1 2 1 Head Tail Head Tail 2 Head (-1,1) (1,-1) head tail head tail Tail (1,-1) (-1,1) (-1,1) (1,-1) (1,-1) (-1,1) 3 A game with nature Left (5, 0) 1 Head 1/2 Right (2, 2) Nature (3, 3) 1/2 Left Tail 2 Right (0, -5) Mixed Strategy Definition: A mixed strategy of a player is a probability distribution over the set of his strategies. Pure strategies: Si = {si1,si2,…,sik} σ → A mixed strategy: i: S [0,1] s.t. σ σ σ i(si1) + i(si2) + … + i(sik) = 1. If the other players play s-i =(s1,…, si-1,si+1,…,sn), then σ the expected utility of playing i is σ σ σ i(si1)ui(si1,s-i) + i(si2)ui(si2,s-i) + … + i(sik)ui(sik,s-i). -
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 .................................. -
A New Approach to Stable Matching Problems
August1 989 Report No. STAN-CS-89- 1275 A New Approach to Stable Matching Problems bY Ashok Subramanian Department of Computer Science Stanford University Stanford, California 94305 3 DISTRIBUTION /AVAILABILITY OF REPORT Unrestricted: Distribution Unlimited GANIZATION 1 1 TITLE (Include Securrty Clamfrcat!on) A New Approach to Stable Matching Problems 12 PERSONAL AUTHOR(S) Ashok Subramanian 13a TYPE OF REPORT 13b TtME COVERED 14 DATE OF REPORT (Year, Month, Day) 15 PAGE COUNT FROM TO August 1989 34 16 SUPPLEMENTARY NOTATION 17 COSATI CODES 18 SUBJECT TERMS (Contrnue on reverse If necessary and jdentrfy by block number) FIELD GROUP SUB-GROUP 19 ABSTRACT (Continue on reverse if necessary and identrfy by block number) Abstract. We show that Stable Matching problems are the same as problems about stable config- urations of X-networks. Consequences include easy proofs of old theorems, a new simple algorithm for finding a stable matching, an understanding of the difference between Stable Marriage and Stable Roommates, NTcompleteness of Three-party Stable Marriage, CC-completeness of several Stable Matching problems, and a fast parallel reduction from the Stable Marriage problem to the ’ Assignment problem. 20 DISTRIBUTION /AVAILABILITY OF ABSTRACT 21 ABSTRACT SECURITY CLASSIFICATION q UNCLASSIFIED/UNLIMITED 0 SAME AS Rf’T 0 DTIC USERS 22a NAME OF RESPONSIBLE INDIVIDUAL 22b TELEPHONE (Include Area Code) 22c OFFICE SYMBOL Ernst Mavr DD Form 1473, JUN 86 Prevrous edrtions are obsolete SECURITY CLASSIFICATION OF TYS PAGt . 1 , S/N 0102-LF-014-6603 \ \ ““*5 - - A New Approach to Stable Matching Problems * Ashok Subramanian Department of Computer Science St anford University Stanford, CA 94305-2140 Abstract. -
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. -
How to Win at Tic-Tac-Toe
More Than Child’s Play How to Get N in a Row Games with Animals Hypercube Tic-Tac-Toe How to Win at Tic-Tac-Toe Norm Do Undoubtably, one of the most popular pencil and paper games in the world is tic-tac-toe, also commonly known as noughts and crosses. In this talk, you will learn how to beat your friends (at tic-tac-toe), discover why snaky is so shaky, and see the amazing tic-tac-toe playing chicken! March 2007 Norm Do How to Win at Tic-Tac-Toe Tic-Tac-Toe is popular: You’ve all played it while sitting at the back of a boring class. In fact, some of you are probably playing it right now! Tic-Tac-Toe is boring: People who are mildly clever should never lose. More Than Child’s Play How to Get N in a Row Some Facts About Tic-Tac-Toe Games with Animals Games to Beat your Friends With Hypercube Tic-Tac-Toe Some facts about tic-tac-toe Tic-Tac-Toe is old: It may have been played under the name of “terni lapilli” in Ancient Rome. Norm Do How to Win at Tic-Tac-Toe Tic-Tac-Toe is boring: People who are mildly clever should never lose. More Than Child’s Play How to Get N in a Row Some Facts About Tic-Tac-Toe Games with Animals Games to Beat your Friends With Hypercube Tic-Tac-Toe Some facts about tic-tac-toe Tic-Tac-Toe is old: It may have been played under the name of “terni lapilli” in Ancient Rome. -
Nash Equilibrium
Lecture 3: Nash equilibrium Nash equilibrium: The mathematician John Nash introduced the concept of an equi- librium for a game, and equilibrium is often called a Nash equilibrium. They provide a way to identify reasonable outcomes when an easy argument based on domination (like in the prisoner's dilemma, see lecture 2) is not available. We formulate the concept of an equilibrium for a two player game with respective 0 payoff matrices PR and PC . We write PR(s; s ) for the payoff for player R when R plays 0 s and C plays s, this is simply the (s; s ) entry the matrix PR. Definition 1. A pair of strategies (^sR; s^C ) is an Nash equilbrium for a two player game if no player can improve his payoff by changing his strategy from his equilibrium strategy to another strategy provided his opponent keeps his equilibrium strategy. In terms of the payoffs matrices this means that PR(sR; s^C ) ≤ P (^sR; s^C ) for all sR ; and PC (^sR; sC ) ≤ P (^sR; s^C ) for all sc : The idea at work in the definition of Nash equilibrium deserves a name: Definition 2. A strategy s^R is a best-response to a strategy sc if PR(sR; sC ) ≤ P (^sR; sC ) for all sR ; i.e. s^R is such that max PR(sR; sC ) = P (^sR; sC ) sR We can now reformulate the idea of a Nash equilibrium as The pair (^sR; s^C ) is a Nash equilibrium if and only ifs ^R is a best-response tos ^C and s^C is a best-response tos ^R. -
Lecture Notes
Chapter 12 Repeated Games In real life, most games are played within a larger context, and actions in a given situation affect not only the present situation but also the future situations that may arise. When a player acts in a given situation, he takes into account not only the implications of his actions for the current situation but also their implications for the future. If the players arepatient andthe current actionshavesignificant implications for the future, then the considerations about the future may take over. This may lead to a rich set of behavior that may seem to be irrational when one considers the current situation alone. Such ideas are captured in the repeated games, in which a "stage game" is played repeatedly. The stage game is repeated regardless of what has been played in the previous games. This chapter explores the basic ideas in the theory of repeated games and applies them in a variety of economic problems. As it turns out, it is important whether the game is repeated finitely or infinitely many times. 12.1 Finitely-repeated games Let = 0 1 be the set of all possible dates. Consider a game in which at each { } players play a "stage game" , knowing what each player has played in the past. ∈ Assume that the payoff of each player in this larger game is the sum of the payoffsthat he obtains in the stage games. Denote the larger game by . Note that a player simply cares about the sum of his payoffs at the stage games. Most importantly, at the beginning of each repetition each player recalls what each player has 199 200 CHAPTER 12. -
The Roommate Problem Revisited
Department of Economics- FEA/USP The Roommate Problem Revisited MARILDA SOTOMAYOR WORKING PAPER SERIES Nº 2016-04 DEPARTMENT OF ECONOMICS, FEA-USP WORKING PAPER Nº 2016-04 The Roommate Problem Revisited Marilda Sotomayor ([email protected]) Abstract: We approach the roommate problem by focusing on simple matchings, which are those individually rational matchings whose blocking pairs, if any, are formed with unmatched agents. We show that the core is non-empty if and only if no simple and unstable matching is Pareto optimal among all simple matchings. The economic intuition underlying this condition is that blocking can be done so that the transactions at any simple and unstable matching need not be undone, as agents reach the core. New properties of economic interest are proved. Keywords: Core; stable matching . JEL Codes: C78; D78. THE ROOMMATE PROBLEM REVISITED by MARILDA SOTOMAYOR1 Department of Economics Universidade de São Paulo, Cidade Universitária, Av. Prof. Luciano Gualberto 908 05508-900, São Paulo, SP, Brazil e-mail: [email protected] 7/2005 ABSTRACT We approach the roommate problem by focusing on simple matchings, which are those individually rational matchings whose blocking pairs, if any, are formed with unmatched agents. We show that the core is non-empty if and only if no simple and unstable matching is Pareto optimal among all simple matchings. The economic intuition underlying this condition is that blocking can be done so that the transactions at any simple and unstable matching need not be undone, as agents reach the core. New properties of economic interest are proved. Keywords: core, stable matching JEL numbers: C78, D78 1 This paper is partially supported by CNPq-Brazil. -
Fractional Hedonic Games
Fractional Hedonic Games HARIS AZIZ, Data61, CSIRO and UNSW Australia FLORIAN BRANDL, Technical University of Munich FELIX BRANDT, Technical University of Munich PAUL HARRENSTEIN, University of Oxford MARTIN OLSEN, Aarhus University DOMINIK PETERS, University of Oxford The work we present in this paper initiated the formal study of fractional hedonic games, coalition formation games in which the utility of a player is the average value he ascribes to the members of his coalition. Among other settings, this covers situations in which players only distinguish between friends and non-friends and desire to be in a coalition in which the fraction of friends is maximal. Fractional hedonic games thus not only constitute a natural class of succinctly representable coalition formation games, but also provide an interesting framework for network clustering. We propose a number of conditions under which the core of fractional hedonic games is non-empty and provide algorithms for computing a core stable outcome. By contrast, we show that the core may be empty in other cases, and that it is computationally hard in general to decide non-emptiness of the core. 1 INTRODUCTION Hedonic games present a natural and versatile framework to study the formal aspects of coalition formation which has received much attention from both an economic and an algorithmic perspective. This work was initiated by Drèze and Greenberg[1980], Banerjee et al . [2001], Cechlárová and Romero-Medina[2001], and Bogomolnaia and Jackson[2002] and has sparked a lot of follow- up work. A recent survey was provided by Aziz and Savani[2016]. In hedonic games, coalition formation is approached from a game-theoretic angle. -
What Is Local Optimality in Nonconvex-Nonconcave Minimax Optimization?
What is Local Optimality in Nonconvex-Nonconcave Minimax Optimization? Chi Jin Praneeth Netrapalli University of California, Berkeley Microsoft Research, India [email protected] [email protected] Michael I. Jordan University of California, Berkeley [email protected] August 18, 2020 Abstract Minimax optimization has found extensive application in modern machine learning, in settings such as generative adversarial networks (GANs), adversarial training and multi-agent reinforcement learning. As most of these applications involve continuous nonconvex-nonconcave formulations, a very basic question arises—“what is a proper definition of local optima?” Most previous work answers this question using classical notions of equilibria from simultaneous games, where the min-player and the max-player act simultaneously. In contrast, most applications in machine learning, including GANs and adversarial training, correspond to sequential games, where the order of which player acts first is crucial (since minimax is in general not equal to maximin due to the nonconvex-nonconcave nature of the problems). The main contribution of this paper is to propose a proper mathematical definition of local optimality for this sequential setting—local minimax—as well as to present its properties and existence results. Finally, we establish a strong connection to a basic local search algorithm—gradient descent ascent (GDA)—under mild conditions, all stable limit points of GDA are exactly local minimax points up to some degenerate points. 1 Introduction arXiv:1902.00618v3 [cs.LG] 15 Aug 2020 Minimax optimization refers to problems of two agents—one agent tries to minimize the payoff function f : X × Y ! R while the other agent tries to maximize it. -
15 Hedonic Games Haris Azizaand Rahul Savanib
Draft { December 5, 2014 15 Hedonic Games Haris Azizaand Rahul Savanib 15.1 Introduction Coalitions are a central part of economic, political, and social life, and coalition formation has been studied extensively within the mathematical social sciences. Agents (be they humans, robots, or software agents) have preferences over coalitions and, based on these preferences, it is natural to ask which coalitions are expected to form, and which coalition structures are better social outcomes. In this chapter, we consider coalition formation games with hedonic preferences, or simply hedonic games. The outcome of a coalition formation game is a partitioning of the agents into disjoint coalitions, which we will refer to synonymously as a partition or coalition structure. The defining feature of hedonic preferences is that every agent only cares about which agents are in its coalition, but does not care how agents in other coali- tions are grouped together (Dr`ezeand Greenberg, 1980). Thus, hedonic preferences completely ignore inter-coalitional dependencies. Despite their relative simplicity, hedonic games have been used to model many interesting settings, such as research team formation (Alcalde and Revilla, 2004), scheduling group activities (Darmann et al., 2012), formation of coalition governments (Le Breton et al., 2008), cluster- ings in social networks (see e.g., Aziz et al., 2014b; McSweeney et al., 2014; Olsen, 2009), and distributed task allocation for wireless agents (Saad et al., 2011). Before we give a formal definition of a hedonic game, we give a standard hedonic game from the literature that we will use as a running example (see e.g., Banerjee et al.