Dynamics and Coalitions in Sequential Games
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1 Sequential Games
1 Sequential Games We call games where players take turns moving “sequential games”. Sequential games consist of the same elements as normal form games –there are players, rules, outcomes, and payo¤s. However, sequential games have the added element that history of play is now important as players can make decisions conditional on what other players have done. Thus, if two people are playing a game of Chess the second mover is able to observe the …rst mover’s initial move prior to making his initial move. While it is possible to represent sequential games using the strategic (or matrix) form representation of the game it is more instructive at …rst to represent sequential games using a game tree. In addition to the players, actions, outcomes, and payo¤s, the game tree will provide a history of play or a path of play. A very basic example of a sequential game is the Entrant-Incumbent game. The game is described as follows: Consider a game where there is an entrant and an incumbent. The entrant moves …rst and the incumbent observes the entrant’sdecision. The entrant can choose to either enter the market or remain out of the market. If the entrant remains out of the market then the game ends and the entrant receives a payo¤ of 0 while the incumbent receives a payo¤ of 2. If the entrant chooses to enter the market then the incumbent gets to make a choice. The incumbent chooses between …ghting entry or accommodating entry. If the incumbent …ghts the entrant receives a payo¤ of 3 while the incumbent receives a payo¤ of 1. -
Notes on Sequential and Repeated Games
Notes on sequential and repeated games 1 Sequential Move Games Thus far we have examined games in which players make moves simultaneously (or without observing what the other player has done). Using the normal (strategic) form representation of a game we can identify sets of strategies that are best responses to each other (Nash Equilibria). We now focus on sequential games of complete information. We can still use the normal form representation to identify NE but sequential games are richer than that because some players observe other players’decisions before they take action. The fact that some actions are observable may cause some NE of the normal form representation to be inconsistent with what one might think a player would do. Here’sa simple game between an Entrant and an Incumbent. The Entrant moves …rst and the Incumbent observes the Entrant’s action and then gets to make a choice. The Entrant has to decide whether or not he will enter a market or not. Thus, the Entrant’s two strategies are “Enter” or “Stay Out”. If the Entrant chooses “Stay Out” then the game ends. The payo¤s for the Entrant and Incumbent will be 0 and 2 respectively. If the Entrant chooses “Enter” then the Incumbent gets to choose whether or not he will “Fight”or “Accommodate”entry. If the Incumbent chooses “Fight”then the Entrant receives 3 and the Incumbent receives 1. If the Incumbent chooses “Accommodate”then the Entrant receives 2 and the Incumbent receives 1. This game in normal form is Incumbent Fight if Enter Accommodate if Enter . -
SEQUENTIAL GAMES with PERFECT INFORMATION Example
SEQUENTIAL GAMES WITH PERFECT INFORMATION Example 4.9 (page 105) Consider the sequential game given in Figure 4.9. We want to apply backward induction to the tree. 0 Vertex B is owned by player two, P2. The payoffs for P2 are 1 and 3, with 3 > 1, so the player picks R . Thus, the payoffs at B become (0, 3). 00 Next, vertex C is also owned by P2 with payoffs 1 and 0. Since 1 > 0, P2 picks L , and the payoffs are (4, 1). Player one, P1, owns A; the choice of L gives a payoff of 0 and R gives a payoff of 4; 4 > 0, so P1 chooses R. The final payoffs are (4, 1). 0 00 We claim that this strategy profile, { R } for P1 and { R ,L } is a Nash equilibrium. Notice that the 0 00 strategy profile gives a choice at each vertex. For the strategy { R ,L } fixed for P2, P1 has a maximal payoff by choosing { R }, ( 0 00 0 00 π1(R, { R ,L }) = 4 π1(R, { R ,L }) = 4 ≥ 0 00 π1(L, { R ,L }) = 0. 0 00 In the same way, for the strategy { R } fixed for P1, P2 has a maximal payoff by choosing { R ,L }, ( 00 0 00 π2(R, {∗,L }) = 1 π2(R, { R ,L }) = 1 ≥ 00 π2(R, {∗,R }) = 0, where ∗ means choose either L0 or R0. Since no change of choice by a player can increase that players own payoff, the strategy profile is called a Nash equilibrium. Notice that the above strategy profile is also a Nash equilibrium on each branch of the game tree, mainly starting at either B or starting at C. -
Implementation Theory*
Chapter 5 IMPLEMENTATION THEORY* ERIC MASKIN Institute for Advanced Study, Princeton, NJ, USA TOMAS SJOSTROM Department of Economics, Pennsylvania State University, University Park, PA, USA Contents Abstract 238 Keywords 238 1. Introduction 239 2. Definitions 245 3. Nash implementation 247 3.1. Definitions 248 3.2. Monotonicity and no veto power 248 3.3. Necessary and sufficient conditions 250 3.4. Weak implementation 254 3.5. Strategy-proofness and rich domains of preferences 254 3.6. Unrestricted domain of strict preferences 256 3.7. Economic environments 257 3.8. Two agent implementation 259 4. Implementation with complete information: further topics 260 4.1. Refinements of Nash equilibrium 260 4.2. Virtual implementation 264 4.3. Mixed strategies 265 4.4. Extensive form mechanisms 267 4.5. Renegotiation 269 4.6. The planner as a player 275 5. Bayesian implementation 276 5.1. Definitions 276 5.2. Closure 277 5.3. Incentive compatibility 278 5.4. Bayesian monotonicity 279 * We are grateful to Sandeep Baliga, Luis Corch6n, Matt Jackson, Byungchae Rhee, Ariel Rubinstein, Ilya Segal, Hannu Vartiainen, Masahiro Watabe, and two referees, for helpful comments. Handbook of Social Choice and Welfare, Volume 1, Edited by K.J Arrow, A.K. Sen and K. Suzumura ( 2002 Elsevier Science B. V All rights reserved 238 E. Maskin and T: Sj'str6m 5.5. Non-parametric, robust and fault tolerant implementation 281 6. Concluding remarks 281 References 282 Abstract The implementation problem is the problem of designing a mechanism (game form) such that the equilibrium outcomes satisfy a criterion of social optimality embodied in a social choice rule. -
Israel-Palestine Through the Lens of Game Theory
University of Massachusetts Amherst ScholarWorks@UMass Amherst Economics Department Working Paper Series Economics 2021 Land for peace? Israel-Palestine through the lens of game theory Amal Ahmad Department of Economics, University of Massachusetts Amherst Follow this and additional works at: https://scholarworks.umass.edu/econ_workingpaper Part of the Economics Commons Recommended Citation Ahmad, Amal, "Land for peace? Israel-Palestine through the lens of game theory" (2021). Economics Department Working Paper Series. 301. https://doi.org/10.7275/21792057 This Article is brought to you for free and open access by the Economics at ScholarWorks@UMass Amherst. It has been accepted for inclusion in Economics Department Working Paper Series by an authorized administrator of ScholarWorks@UMass Amherst. For more information, please contact [email protected]. Land for peace? Israel-Palestine through the lens of game theory Amal Ahmad∗ February 2021 Abstract Why have Israel and the Palestinians failed to implement a \land for peace" solution, along the lines of the Oslo Accords? This paper studies the applica- tion of game theory to this question. I show that existing models of the conflict largely rely on unrealistic assumptions about what the main actors are trying to achieve. Specifically, they assume that Israel is strategically interested in withdrawing from the occupied territories pending resolvable security concerns but that it is obstructed from doing so by violent Palestinians with other objec- tives. I use historical analysis along with bargaining theory to shed doubt on this assumption, and to argue that the persistence of conflict has been aligned with, not contrary to, the interests of the militarily powerful party, Israel. -
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. -
Implementation and Strong Nash Equilibrium
LIBRARY OF THE MASSACHUSETTS INSTITUTE OF TECHNOLOGY Digitized by the Internet Archive in 2011 with funding from Boston Library Consortium Member Libraries http://www.archive.org/details/implementationstOOmask : } ^mm working paper department of economics IMPLEMENTATION AND STRONG NASH EQUILIBRIUM Eric Maskin Number 216 January 1978 massachusetts institute of technology 50 memorial drive Cambridge, mass. 021 39 IMPLEMENTATION AND STRONG NASH EQUILIBRIUM Eric Maskin Number 216 January 1978 I am grateful for the financial support of the National Science Foundation. A social choice correspondence (SCC) is a mapping which assoc- iates each possible profile of individuals' preferences with a set of feasible alternatives (the set of f-optima) . To implement *n SCC, f, is to construct a game form g such that, for all preference profiles the equilibrium set of g (with respect to some solution concept) coincides with the f-optimal set. In a recent study [1], I examined the general question of implementing social choice correspondences when Nash equilibrium is the solution concept. Nash equilibrium, of course, is a strictly noncooperative notion, and so it is natural to consider the extent to which the results carry over when coalitions can form. The cooperative counterpart of Nash is the strong equilibrium due to Aumann. Whereas Nash defines equilibrium in terms of deviations only by single individuals, Aumann 's equilibrium incorporates deviations by every conceivable coalition. This paper considers implementation for strong equilibrium. The results of my previous paper were positive. If an SCC sat- isfies a monotonicity property and a much weaker requirement called no veto power, it can be implemented by Nash equilibrium. -
On Some Winning Strategies for the Iterated Prisoner's Dilemma Or Mr
Chapter 1 On some winning strategies for the Iterated Prisoner’s Dilemma or Mr. Nice Guy and the Cosa Nostra Wolfgang Slany Wolfgang Kienreich Technical University Know-Center Graz, Austria Graz, Austria [email protected] [email protected] We submitted two kinds of strategies to the iterated prisoner’s dilemma (IPD) competitions organized by Graham Kendall, Paul Darwen and Xin Yao in 2004 and 20051. Our strategies performed exceedingly well in both years. One type is an intelligent and optimistic enhanced version of the well known TitForTat strategy which we named OmegaTitForTat. It recognizes common behaviour patterns and detects and recovers from repairable mutual defect deadlock situations, otherwise behaving much like TitForTat. OmegaTitForTat was placed as the first or second individual strategy in both competitions in the leagues in which it took part. The second type consists of a set of strategies working together as a team. The call for participation of the competitions explicitly stated that cooperative strategies would be allowed to participate. This allowed a form of implicit communication which is not in keeping with the original IPD idea, but represents a natural extension to the study of cooperative behaviour in reality as it is aimed at through the study of the simple, yet 1 See http://www.prisoners-dilemma.com/ for more details. 1 2 Book Title insightful, iterated prisoner’s dilemma model. Indeed, one’s behaviour towards another person in reality is very often influenced by one’s relation to the other person. In particular, we submitted three sets of strategies that work together as groups. -
Order and Containment in Concurrent System Design Copyright Fall 2000
Order and Containment in Concurrent System Design Copyright Fall 2000 by John Sidney Davis II 1 Abstract Order and Containment in Concurrent System Design by John Sidney Davis II Doctor of Philosophy in Engineering-Electrical Engineering and Computer Science University of California at Berkeley Professor Edward A. Lee, Chair This dissertation considers the dif®culty of modeling and designing complex, concurrent systems. The term concurrent is used here to mean a system consisting of a network of commu- nicating components. The term complex is used here to mean a system consisting of components with different models of computation such that the communication between different components has different semantics according to the respective interacting models of computation. Modeling and designing a concurrent system requires a clear understanding of the types of relationships that exist between the components found within a concurrent system. Two partic- ularly important types of relationships found in concurrent systems are the order relation and the containment relation. The order relation represents the relative timing of component actions within a concurrent system. The containment relation facilitates human understanding of a system by ab- stracting a system's components into layers of visibility. The consequence of improper management of the order and containment relationships in a complex, concurrent system is deadlock. Deadlock is an undesirable halting of a system's execution and is the most challenging type of concurrent system error to debug. The contents of this disserta- tion show that no methodology is currently available that can concisely, accurately and graphically 2 model both the order and containment relations found in complex, concurrent systems. -
Managing Coopetition: Is Transparency a Fallacy Or a Mandatory Condition? a Case Study of a Coopetition Project with an Opportunistic Partner
XXVIe Conférence Internationale de Management Stratégique Managing coopetition: Is transparency a fallacy or a mandatory condition? A case study of a coopetition project with an opportunistic partner Sea Matilda Bez Université de Montpellier (MOMA) et Université Paris-Dauphine (PSL) [email protected] Frédéric Le Roy Université de Montpellier (MOMA) et Montpellier Business School [email protected] Stéphanie Dameron Université Paris-Dauphine (PSL) [email protected] Abstract : We investigate why and how knowledge transparency between competitors are needed for radical innovation. Previous scholars considered that coopetition (i.e. collaboration between competing firms) could not be a suitable strategy. Knowledge transparency invites opportunism. Thus, even for a radical innovation purpose, being highly transparent with a competitor is a fallacy. Our research goes beyond this specific approach of coopetition and reveals that firms can preserve transparency even if the partners behave opportunistically. We develop counter-intuitive propositions based on a conceptual discussion followed by insight from an extreme case study of a pharmaceutical company opening its innovation project to a competitor who behaves opportunistically (i.e. the Plavix). Our study mainly highlights that firms need to fight against the intuitive reaction of reducing transparency when the partner behaves opportunistically. Reducing the transparency inevitably leads to “shooting itself in the foot” because the reduction of transparency of one partner leads to the destruction of any possibility to create a radical innovation. Moreover, our research represents interesting guidelines for top managers by : (1) confirming that coopetition strategies are relevant in addressing the challenges of radical innovation, and (2) highlighting a specific organization design to manage transparency in a coopetitive project. -
Strong Nash Equilibria and Mixed Strategies
Strong Nash equilibria and mixed strategies Eleonora Braggiona, Nicola Gattib, Roberto Lucchettia, Tuomas Sandholmc aDipartimento di Matematica, Politecnico di Milano, piazza Leonardo da Vinci 32, 20133 Milano, Italy bDipartimento di Elettronica, Informazione e Bioningegneria, Politecnico di Milano, piazza Leonardo da Vinci 32, 20133 Milano, Italy cComputer Science Department, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA Abstract In this paper we consider strong Nash equilibria, in mixed strategies, for finite games. Any strong Nash equilibrium outcome is Pareto efficient for each coalition. First, we analyze the two–player setting. Our main result, in its simplest form, states that if a game has a strong Nash equilibrium with full support (that is, both players randomize among all pure strategies), then the game is strictly competitive. This means that all the outcomes of the game are Pareto efficient and lie on a straight line with negative slope. In order to get our result we use the indifference principle fulfilled by any Nash equilibrium, and the classical KKT conditions (in the vector setting), that are necessary conditions for Pareto efficiency. Our characterization enables us to design a strong–Nash– equilibrium–finding algorithm with complexity in Smoothed–P. So, this problem—that Conitzer and Sandholm [Conitzer, V., Sandholm, T., 2008. New complexity results about Nash equilibria. Games Econ. Behav. 63, 621–641] proved to be computationally hard in the worst case—is generically easy. Hence, although the worst case complexity of finding a strong Nash equilibrium is harder than that of finding a Nash equilibrium, once small perturbations are applied, finding a strong Nash is easier than finding a Nash equilibrium. -
On the Verification and Computation of Strong Nash Equilibrium
On the Verification and Computation of Strong Nash Equilibrium Nicola Gatti Marco Rocco Tuomas Sandholm Politecnico di Milano Politecnico di Milano Carnegie Mellon Piazza Leonardo da Vinci 32 Piazza Leonardo da Vinci 32 Computer Science Dep. Milano, Italy Milano, Italy 5000 Forbes Avenue [email protected] [email protected] Pittsburgh, PA 15213, USA [email protected] ABSTRACT The NE concept is inadequate when agents can a priori Computing equilibria of games is a central task in computer communicate, being in a position to form coalitions and de- science. A large number of results are known for Nash equi- viate multilaterally in a coordinated way. The strong Nash librium (NE). However, these can be adopted only when equilibrium (SNE) concept strengthens the NE concept by coalitions are not an issue. When instead agents can form requiring the strategy profile to be resilient also to multilat- coalitions, NE is inadequate and an appropriate solution eral deviations, including the grand coalition [2]. However, concept is strong Nash equilibrium (SNE). Few computa- differently from NE, an SNE may not exist. It is known tional results are known about SNE. In this paper, we first that searching for an SNE is NP–hard, but, except for the study the problem of verifying whether a strategy profile is case of two–agent symmetric games, it is not known whether an SNE, showing that the problem is in P. We then design the problem is in NP [8]. Furthermore, to the best of our a spatial branch–and–bound algorithm to find an SNE, and knowledge, there is no algorithm to find an SNE in general we experimentally evaluate the algorithm.