Set-Valued Solution Concepts in Social Choice and Game Theory Axiomatic and Computational Aspects
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Lecture 1 Mathematical Preliminaries
Lecture 1 Mathematical Preliminaries A. Banerji July 26, 2016 (Delhi School of Economics) Introductory Math Econ July 26, 2016 1 / 27 Outline 1 Preliminaries Sets Logic Sets and Functions Linear Spaces (Delhi School of Economics) Introductory Math Econ July 26, 2016 2 / 27 Preliminaries Sets Basic Concepts Take as understood the notion of a set. Usually use upper case letters for sets and lower case ones for their elements. Notation. a 2 A, b 2= A. A ⊆ B if every element of A also belongs to B. A = B if A ⊆ B and B ⊆ A are both true. A [ B = fxjx 2 A or x 2 Bg. The ‘or’ is inclusive of ‘both’. A \ B = fxjx 2 A and x 2 Bg. What if A and B have no common elements? To retain meaning, we invent the concept of an empty set, ;. A [; = A, A \; = ;. For the latter, note that there’s no element in common between the sets A and ; because the latter does not have any elements. ; ⊆ A, for all A. Every element of ; belongs to A is vacuously true since ; has no elements. This brings us to some logic. (Delhi School of Economics) Introductory Math Econ July 26, 2016 3 / 27 Preliminaries Logic Logic Statements or propositions must be either true or false. P ) Q. If the statement P is true, then the statement Q is true. But if P is false, Q may be true or false. For example, on real numbers, let P = x > 0 and Q = x2 > 0. If P is true, so is Q, but Q may be true even if P is not, e.g. -
Evolutionary Game Theory: ESS, Convergence Stability, and NIS
Evolutionary Ecology Research, 2009, 11: 489–515 Evolutionary game theory: ESS, convergence stability, and NIS Joseph Apaloo1, Joel S. Brown2 and Thomas L. Vincent3 1Department of Mathematics, Statistics and Computer Science, St. Francis Xavier University, Antigonish, Nova Scotia, Canada, 2Department of Biological Sciences, University of Illinois, Chicago, Illinois, USA and 3Department of Aerospace and Mechanical Engineering, University of Arizona, Tucson, Arizona, USA ABSTRACT Question: How are the three main stability concepts from evolutionary game theory – evolutionarily stable strategy (ESS), convergence stability, and neighbourhood invader strategy (NIS) – related to each other? Do they form a basis for the many other definitions proposed in the literature? Mathematical methods: Ecological and evolutionary dynamics of population sizes and heritable strategies respectively, and adaptive and NIS landscapes. Results: Only six of the eight combinations of ESS, convergence stability, and NIS are possible. An ESS that is NIS must also be convergence stable; and a non-ESS, non-NIS cannot be convergence stable. A simple example shows how a single model can easily generate solutions with all six combinations of stability properties and explains in part the proliferation of jargon, terminology, and apparent complexity that has appeared in the literature. A tabulation of most of the evolutionary stability acronyms, definitions, and terminologies is provided for comparison. Key conclusions: The tabulated list of definitions related to evolutionary stability are variants or combinations of the three main stability concepts. Keywords: adaptive landscape, convergence stability, Darwinian dynamics, evolutionary game stabilities, evolutionarily stable strategy, neighbourhood invader strategy, strategy dynamics. INTRODUCTION Evolutionary game theory has and continues to make great strides. -
California Institute of Technology
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Caltech Authors - Main DIVISION OF THE HUM ANITIES AND SO CI AL SCIENCES CALIFORNIA INSTITUTE OF TECHNOLOGY PASADENA, CALIFORNIA 91125 IMPLEMENTATION THEORY Thomas R. Palfrey � < a: 0 1891 u. "')/,.. () SOCIAL SCIENCE WORKING PAPER 912 September 1995 Implementation Theory Thomas R. Palfrey Abstract This surveys the branch of implementation theory initiated by Maskin (1977). Results for both complete and incomplete information environments are covered. JEL classification numbers: 025, 026 Key words: Implementation Theory, Mechanism Design, Game Theory, Social Choice Implementation Theory* Thomas R. Palfrey 1 Introduction Implementation theory is an area of research in economic theory that rigorously investi gates the correspondence between normative goals and institutions designed to achieve {implement) those goals. More precisely, given a normative goal or welfare criterion for a particular class of allocation pro blems (or domain of environments) it formally char acterizes organizational mechanisms that will guarantee outcomes consistent with that goal, assuming the outcomes of any such mechanism arise from some specification of equilibrium behavior. The approaches to this problem to date lie in the general domain of game theory because, as a matter of definition in the implementation theory litera ture, an institution is modelled as a mechanism, which is essentially a non-cooperative game. Moreover, the specific models of equilibrium behavior -
8. Maxmin and Minmax Strategies
CMSC 474, Introduction to Game Theory 8. Maxmin and Minmax Strategies Mohammad T. Hajiaghayi University of Maryland Outline Chapter 2 discussed two solution concepts: Pareto optimality and Nash equilibrium Chapter 3 discusses several more: Maxmin and Minmax Dominant strategies Correlated equilibrium Trembling-hand perfect equilibrium e-Nash equilibrium Evolutionarily stable strategies Worst-Case Expected Utility For agent i, the worst-case expected utility of a strategy si is the minimum over all possible Husband Opera Football combinations of strategies for the other agents: Wife min u s ,s Opera 2, 1 0, 0 s-i i ( i -i ) Football 0, 0 1, 2 Example: Battle of the Sexes Wife’s strategy sw = {(p, Opera), (1 – p, Football)} Husband’s strategy sh = {(q, Opera), (1 – q, Football)} uw(p,q) = 2pq + (1 – p)(1 – q) = 3pq – p – q + 1 We can write uw(p,q) For any fixed p, uw(p,q) is linear in q instead of uw(sw , sh ) • e.g., if p = ½, then uw(½,q) = ½ q + ½ 0 ≤ q ≤ 1, so the min must be at q = 0 or q = 1 • e.g., minq (½ q + ½) is at q = 0 minq uw(p,q) = min (uw(p,0), uw(p,1)) = min (1 – p, 2p) Maxmin Strategies Also called maximin A maxmin strategy for agent i A strategy s1 that makes i’s worst-case expected utility as high as possible: argmaxmin ui (si,s-i ) si s-i This isn’t necessarily unique Often it is mixed Agent i’s maxmin value, or security level, is the maxmin strategy’s worst-case expected utility: maxmin ui (si,s-i ) si s-i For 2 players it simplifies to max min u1s1, s2 s1 s2 Example Wife’s and husband’s strategies -
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. -
14.12 Game Theory Lecture Notes∗ Lectures 7-9
14.12 Game Theory Lecture Notes∗ Lectures 7-9 Muhamet Yildiz Intheselecturesweanalyzedynamicgames(withcompleteinformation).Wefirst analyze the perfect information games, where each information set is singleton, and develop the notion of backwards induction. Then, considering more general dynamic games, we will introduce the concept of the subgame perfection. We explain these concepts on economic problems, most of which can be found in Gibbons. 1 Backwards induction The concept of backwards induction corresponds to the assumption that it is common knowledge that each player will act rationally at each node where he moves — even if his rationality would imply that such a node will not be reached.1 Mechanically, it is computed as follows. Consider a finite horizon perfect information game. Consider any node that comes just before terminal nodes, that is, after each move stemming from this node, the game ends. If the player who moves at this node acts rationally, he will choose the best move for himself. Hence, we select one of the moves that give this player the highest payoff. Assigning the payoff vector associated with this move to the node at hand, we delete all the moves stemming from this node so that we have a shorter game, where our node is a terminal node. Repeat this procedure until we reach the origin. ∗These notes do not include all the topics that will be covered in the class. See the slides for a more complete picture. 1 More precisely: at each node i the player is certain that all the players will act rationally at all nodes j that follow node i; and at each node i the player is certain that at each node j that follows node i the player who moves at j will be certain that all the players will act rationally at all nodes k that follow node j,...ad infinitum. -
Game Theory 2021
More Mechanism Design Game Theory 2021 Game Theory 2021 Ulle Endriss Institute for Logic, Language and Computation University of Amsterdam Ulle Endriss 1 More Mechanism Design Game Theory 2021 Plan for Today In this second lecture on mechanism design we are going to generalise beyond the basic scenario of auctions|as much as we can manage: • Revelation Principle: can focus on direct-revelation mechanisms • formal model of direct-revelation mechanisms with money • incentive compatibility of the Vickrey-Clarke-Groves mechanism • other properties of VCG for special case of combinatorial auctions • impossibility of achieving incentive compatibility more generally Much of this is also (somewhat differently) covered by Nisan (2007). N. Nisan. Introduction to Mechanism Design (for Computer Scientists). In N. Nisan et al. (eds.), Algorithmic Game Theory. Cambridge University Press, 2007. Ulle Endriss 2 More Mechanism Design Game Theory 2021 Reminder Last time we saw four auction mechanisms for selling a single item: English, Dutch, first-price sealed-bid, Vickrey. The Vickrey auction was particularly interesting: • each bidder submits a bid in a sealed envelope • the bidder with the highest bid wins, but pays the price of the second highest bid (unless it's below the reservation price) It is a direct-revelation mechanism (unlike English and Dutch auctions) and it is incentive-compatible, i.e., truth-telling is a dominant strategy (unlike for Dutch and FPSB auctions). Ulle Endriss 3 More Mechanism Design Game Theory 2021 The Revelation Principle Revelation Principle: Any outcome that is implementable in dominant strategies via some mechanism can also be implemented by means of a direct-revelation mechanism making truth-telling a dominant strategy. -
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. -
Contemporaneous Perfect Epsilon-Equilibria
Games and Economic Behavior 53 (2005) 126–140 www.elsevier.com/locate/geb Contemporaneous perfect epsilon-equilibria George J. Mailath a, Andrew Postlewaite a,∗, Larry Samuelson b a University of Pennsylvania b University of Wisconsin Received 8 January 2003 Available online 18 July 2005 Abstract We examine contemporaneous perfect ε-equilibria, in which a player’s actions after every history, evaluated at the point of deviation from the equilibrium, must be within ε of a best response. This concept implies, but is stronger than, Radner’s ex ante perfect ε-equilibrium. A strategy profile is a contemporaneous perfect ε-equilibrium of a game if it is a subgame perfect equilibrium in a perturbed game with nearly the same payoffs, with the converse holding for pure equilibria. 2005 Elsevier Inc. All rights reserved. JEL classification: C70; C72; C73 Keywords: Epsilon equilibrium; Ex ante payoff; Multistage game; Subgame perfect equilibrium 1. Introduction Analyzing a game begins with the construction of a model specifying the strategies of the players and the resulting payoffs. For many games, one cannot be positive that the specified payoffs are precisely correct. For the model to be useful, one must hope that its equilibria are close to those of the real game whenever the payoff misspecification is small. To ensure that an equilibrium of the model is close to a Nash equilibrium of every possible game with nearly the same payoffs, the appropriate solution concept in the model * Corresponding author. E-mail addresses: [email protected] (G.J. Mailath), [email protected] (A. Postlewaite), [email protected] (L. -
Designing Mechanisms to Make Welfare- Improving Strategies Focal∗
Designing Mechanisms to Make Welfare- Improving Strategies Focal∗ Daniel E. Fragiadakis Peter Troyan Texas A&M University University of Virginia Abstract Many institutions use matching algorithms to make assignments. Examples include the allocation of doctors, students and military cadets to hospitals, schools and branches, respec- tively. Most of the market design literature either imposes strong incentive constraints (such as strategyproofness) or builds mechanisms that, while more efficient than strongly incentive compatible alternatives, require agents to play potentially complex equilibria for the efficiency gains to be realized in practice. Understanding that the effectiveness of welfare-improving strategies relies on the ease with which real-world participants can find them, we carefully design an algorithm that we hypothesize will make such strategies focal. Using a lab experiment, we show that agents do indeed play the intended focal strategies, and doing so yields higher overall welfare than a common alternative mechanism that gives agents dominant strategies of stating their preferences truthfully. Furthermore, we test a mech- anism from the field that is similar to ours and find that while both yield comparable levels of average welfare, our algorithm performs significantly better on an individual level. Thus, we show that, if done carefully, this type of behavioral market design is a worthy endeavor that can most promisingly be pursued by a collaboration between institutions, matching theorists, and behavioral and experimental economists. JEL Classification: C78, C91, C92, D61, D63, Keywords: dictatorship, indifference, matching, welfare, efficiency, experiments ∗We are deeply indebted to our graduate school advisors Fuhito Kojima, Muriel Niederle and Al Roth for count- less conversations and encouragement while advising us on this project. -
Extensive Form Games
Extensive Form Games Jonathan Levin January 2002 1 Introduction The models we have described so far can capture a wide range of strategic environments, but they have an important limitation. In each game we have looked at, each player moves once and strategies are chosen simultaneously. This misses a common feature of many economic settings (and many classic “games” such as chess or poker). Consider just a few examples of economic environments where the timing of strategic decisions is important: • Compensation and Incentives. Firms first sign workers to a contract, then workers decide how to behave, and frequently firms later decide what bonuses to pay and/or which employees to promote. • Research and Development. Firms make choices about what tech- nologies to invest in given the set of available technologies and their forecasts about the future market. • Monetary Policy. A central bank makes decisions about the money supply in response to inflation and growth, which are themselves de- termined by individuals acting in response to monetary policy, and so on. • Entry into Markets. Prior to competing in a product market, firms must make decisions about whether to enter markets and what prod- ucts to introduce. These decisions may strongly influence later com- petition. These notes take up the problem of representing and analyzing these dy- namic strategic environments. 1 2TheExtensiveForm The extensive form of a game is a complete description of: 1. The set of players 2. Who moves when and what their choices are 3. What players know when they move 4. The players’ payoffs as a function of the choices that are made. -
Political Game Theory Nolan Mccarty Adam Meirowitz
Political Game Theory Nolan McCarty Adam Meirowitz To Liz, Janis, Lachlan, and Delaney. Contents Acknowledgements vii Chapter 1. Introduction 1 1. Organization of the Book 2 Chapter 2. The Theory of Choice 5 1. Finite Sets of Actions and Outcomes 6 2. Continuous Outcome Spaces* 10 3. Utility Theory 17 4. Utility representations on Continuous Outcome Spaces* 18 5. Spatial Preferences 19 6. Exercises 21 Chapter 3. Choice Under Uncertainty 23 1. TheFiniteCase 23 2. Risk Preferences 32 3. Learning 37 4. Critiques of Expected Utility Theory 41 5. Time Preferences 46 6. Exercises 50 Chapter 4. Social Choice Theory 53 1. The Open Search 53 2. Preference Aggregation Rules 55 3. Collective Choice 61 4. Manipulation of Choice Functions 66 5. Exercises 69 Chapter 5. Games in the Normal Form 71 1. The Normal Form 73 2. Solutions to Normal Form Games 76 3. Application: The Hotelling Model of Political Competition 83 4. Existence of Nash Equilibria 86 5. Pure Strategy Nash Equilibria in Non-Finite Games* 93 6. Application: Interest Group Contributions 95 7. Application: International Externalities 96 iii iv CONTENTS 8. Computing Equilibria with Constrained Optimization* 97 9. Proving the Existence of Nash Equilibria** 98 10. Strategic Complementarity 102 11. Supermodularity and Monotone Comparative Statics* 103 12. Refining Nash Equilibria 108 13. Application: Private Provision of Public Goods 109 14. Exercises 113 Chapter 6. Bayesian Games in the Normal Form 115 1. Formal Definitions 117 2. Application: Trade restrictions 119 3. Application: Jury Voting 121 4. Application: Jury Voting with a Continuum of Signals* 123 5. Application: Public Goods and Incomplete Information 126 6.