Truthful Equilibria in Dynamic Bayesian Games 1 Introduction
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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 .................................. -
The Monty Hall Problem in the Game Theory Class
The Monty Hall Problem in the Game Theory Class Sasha Gnedin∗ October 29, 2018 1 Introduction Suppose you’re on a game show, and you’re given the choice of three doors: Behind one door is a car; behind the others, goats. You pick a door, say No. 1, and the host, who knows what’s behind the doors, opens another door, say No. 3, which has a goat. He then says to you, “Do you want to pick door No. 2?” Is it to your advantage to switch your choice? With these famous words the Parade Magazine columnist vos Savant opened an exciting chapter in mathematical didactics. The puzzle, which has be- come known as the Monty Hall Problem (MHP), has broken the records of popularity among the probability paradoxes. The book by Rosenhouse [9] and the Wikipedia entry on the MHP present the history and variations of the problem. arXiv:1107.0326v1 [math.HO] 1 Jul 2011 In the basic version of the MHP the rules of the game specify that the host must always reveal one of the unchosen doors to show that there is no prize there. Two remaining unrevealed doors may hide the prize, creating the illusion of symmetry and suggesting that the action does not matter. How- ever, the symmetry is fallacious, and switching is a better action, doubling the probability of winning. There are two main explanations of the paradox. One of them, simplistic, amounts to just counting the mutually exclusive cases: either you win with ∗[email protected] 1 switching or with holding the first choice. -
Incomplete Information I. Bayesian Games
Bayesian Games Debraj Ray, November 2006 Unlike the previous notes, the material here is perfectly standard and can be found in the usual textbooks: see, e.g., Fudenberg-Tirole. For the examples in these notes (except for the very last section), I draw heavily on Martin Osborne’s excellent recent text, An Introduction to Game Theory, Oxford University Press. Obviously, incomplete information games — in which one or more players are privy to infor- mation that others don’t have — has enormous applicability: credit markets / auctions / regulation of firms / insurance / bargaining /lemons / public goods provision / signaling / . the list goes on and on. 1. A Definition A Bayesian game consists of 1. A set of players N. 2. A set of states Ω, and a common prior µ on Ω. 3. For each player i a set of actions Ai and a set of signals or types Ti. (Can make actions sets depend on type realizations.) 4. For each player i, a mapping τi :Ω 7→ Ti. 5. For each player i, a vN-M payoff function fi : A × Ω 7→ R, where A is the product of the Ai’s. Remarks A. Typically, the mapping τi tells us player i’s type. The easiest case is just when Ω is the product of the Ti’s and τi is just the appropriate projection mapping. B. The prior on states need not be common, but one view is that there is no loss of generality in assuming it (simply put all differences in the prior into differences in signals and redefine the state space and si-mappings accordingly). -
(501B) Problem Set 5. Bayesian Games Suggested Solutions by Tibor Heumann
Dirk Bergemann Department of Economics Yale University Microeconomic Theory (501b) Problem Set 5. Bayesian Games Suggested Solutions by Tibor Heumann 1. (Market for Lemons) Here I ask that you work out some of the details in perhaps the most famous of all information economics models. By contrast to discussion in class, we give a complete formulation of the game. A seller is privately informed of the value v of the good that she sells to a buyer. The buyer's prior belief on v is uniformly distributed on [x; y] with 3 0 < x < y: The good is worth 2 v to the buyer. (a) Suppose the buyer proposes a price p and the seller either accepts or rejects p: If she accepts, the seller gets payoff p−v; and the buyer gets 3 2 v − p: If she rejects, the seller gets v; and the buyer gets nothing. Find the optimal offer that the buyer can make as a function of x and y: (b) Show that if the buyer and the seller are symmetrically informed (i.e. either both know v or neither party knows v), then trade takes place with probability 1. (c) Consider a simultaneous acceptance game in the model with private information as in part a, where a price p is announced and then the buyer and the seller simultaneously accept or reject trade at price p: The payoffs are as in part a. Find the p that maximizes the probability of trade. [SOLUTION] (a) We look for the subgame perfect equilibrium, thus we solve by back- ward induction. -
Matsui 2005 IER-23Wvu3z
INTERNATIONAL ECONOMIC REVIEW Vol. 46, No. 1, February 2005 ∗ A THEORY OF MONEY AND MARKETPLACES BY AKIHIKO MATSUI AND TAKASHI SHIMIZU1 University of Tokyo, Japan; Kansai University, Japan This article considers an infinitely repeated economy with divisible fiat money. The economy has many marketplaces that agents choose to visit. In each mar- ketplace, agents are randomly matched to trade goods. There exist a variety of stationary equilibria. In some equilibrium, each good is traded at a single price, whereas in another, every good is traded at two different prices. There is a contin- uum of such equilibria, which differ from each other in price and welfare levels. However, it is shown that only the efficient single-price equilibrium is evolution- arily stable. 1. INTRODUCTION In a transaction, one needs to have what his trade partner wants. However, it is hard for, say, an economist who wants to have her hair cut to find a hairdresser who wishes to learn economics. In order to mitigate this problem of a lack of double coincidence of wants, money is often used as a medium of exchange. If there is a generally acceptable good called money, then the economist can divide the trading process into two: first, she teaches economics students to obtain money, and then finds a hairdresser to exchange money for a haircut. The hairdresser accepts the money since he can use it to obtain what he wants. Money is accepted by many people as it is believed to be accepted by many. Focusing on this function of money, Kiyotaki and Wright (1989) formalized the process of monetary exchange. -
SUPPLEMENT to “VERY SIMPLE MARKOV-PERFECT INDUSTRY DYNAMICS: THEORY” (Econometrica, Vol
Econometrica Supplementary Material SUPPLEMENT TO “VERY SIMPLE MARKOV-PERFECT INDUSTRY DYNAMICS: THEORY” (Econometrica, Vol. 86, No. 2, March 2018, 721–735) JAAP H. ABBRING CentER, Department of Econometrics & OR, Tilburg University and CEPR JEFFREY R. CAMPBELL Economic Research, Federal Reserve Bank of Chicago and CentER, Tilburg University JAN TILLY Department of Economics, University of Pennsylvania NAN YANG Department of Strategy & Policy and Department of Marketing, National University of Singapore KEYWORDS: Demand uncertainty, dynamic oligopoly, firm entry and exit, sunk costs, toughness of competition. THIS SUPPLEMENT to Abbring, Campbell, Tilly, and Yang (2018) (hereafter referred to as the “main text”) (i) proves a theorem we rely upon for the characterization of equilibria and (ii) develops results for an alternative specification of the model. Section S1’s Theorem S1 establishes that a strategy profile is subgame perfect if no player can benefit from deviating from it in one stage of the game and following it faith- fully thereafter. Our proof very closely follows that of the analogous Theorem 4.2 in Fudenberg and Tirole (1991). That theorem only applies to games that are “continuous at infinity” (Fudenberg and Tirole, p. 110), which our game is not. In particular, we only bound payoffs from above (Assumption A1 in the main text) and not from below, be- cause we want the model to encompass econometric specifications like Abbring, Camp- bell, Tilly, and Yang’s (2017) that feature arbitrarily large cost shocks. Instead, our proof leverages the presence of a repeatedly-available outside option with a fixed and bounded payoff, exit. Section S2 presents the primitive assumptions and analysis for an alternative model of Markov-perfect industry dynamics in which one potential entrant makes its entry decision at the same time as incumbents choose between continuation and exit. -
Bayesian Games Professors Greenwald 2018-01-31
Bayesian Games Professors Greenwald 2018-01-31 We describe incomplete-information, or Bayesian, normal-form games (formally; no examples), and corresponding equilibrium concepts. 1 A Bayesian Model of Interaction A Bayesian, or incomplete information, game is a generalization of a complete-information game. Recall that in a complete-information game, the game is assumed to be common knowledge. In a Bayesian game, in addition to what is common knowledge, players may have private information. This private information is captured by the notion of an epistemic type, which describes a player’s knowledge. The Bayesian-game formalism makes two simplifying assumptions: • Any information that is privy to any of the players pertains only to utilities. In all realizations of a Bayesian game, the number of players and their actions are identical. • Players maintain beliefs about the game (i.e., about utilities) in the form of a probability distribution over types. Prior to receiving any private information, this probability distribution is common knowledge: i.e., it is a common prior. After receiving private information, players conditional on this information to update their beliefs. As a consequence of the com- mon prior assumption, any differences in beliefs can be attributed entirely to differences in information. Rational players are again assumed to maximize their utility. But further, they are assumed to update their beliefs when they obtain new information via Bayes’ rule. Thus, in a Bayesian game, in addition to players, actions, and utilities, there is a type space T = ∏i2[n] Ti, where Ti is the type space of player i.There is also a common prior F, which is a probability distribution over type profiles. -
Reinterpreting Mixed Strategy Equilibria: a Unification Of
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Research Papers in Economics Reinterpreting Mixed Strategy Equilibria: A Unification of the Classical and Bayesian Views∗ Philip J. Reny Arthur J. Robson Department of Economics Department of Economics University of Chicago Simon Fraser University September, 2003 Abstract We provide a new interpretation of mixed strategy equilibria that incor- porates both von Neumann and Morgenstern’s classical concealment role of mixing as well as the more recent Bayesian view originating with Harsanyi. For any two-person game, G, we consider an incomplete information game, , in which each player’s type is the probability he assigns to the event thatIG his mixed strategy in G is “found out” by his opponent. We show that, generically, any regular equilibrium of G can be approximated by an equilibrium of in which almost every type of each player is strictly opti- mizing. This leadsIG us to interpret i’s equilibrium mixed strategy in G as a combination of deliberate randomization by i together with uncertainty on j’s part about which randomization i will employ. We also show that such randomization is not unusual: For example, i’s randomization is nondegen- erate whenever the support of an equilibrium contains cyclic best replies. ∗We wish to thank Drew Fudenberg, Motty Perry and Hugo Sonnenschein for very helpful comments. We are especially grateful to Bob Aumann for a stimulating discussion of the litera- ture on mixed strategies that prompted us to fit our work into that literature and ultimately led us to the interpretation we provide here, and to Hari Govindan whose detailed suggestions per- mitted a substantial simplification of the proof of our main result. -
Very Simple Markov-Perfect Industry Dynamics: Theory
http://www.econometricsociety.org/ Econometrica, Vol. 86, No. 2 (March, 2018), 721–735 VERY SIMPLE MARKOV-PERFECT INDUSTRY DYNAMICS: THEORY JAAP H. ABBRING CentER, Department of Econometrics & OR, Tilburg University and CEPR JEFFREY R. CAMPBELL Economic Research, Federal Reserve Bank of Chicago and CentER, Tilburg University JAN TILLY Department of Economics, University of Pennsylvania NAN YANG Department of Strategy & Policy and Department of Marketing, National University of Singapore The copyright to this Article is held by the Econometric Society. It may be downloaded, printed and re- produced only for educational or research purposes, including use in course packs. No downloading or copying may be done for any commercial purpose without the explicit permission of the Econometric So- ciety. For such commercial purposes contact the Office of the Econometric Society (contact information may be found at the website http://www.econometricsociety.org or in the back cover of Econometrica). This statement must be included on all copies of this Article that are made available electronically or in any other format. Econometrica, Vol. 86, No. 2 (March, 2018), 721–735 NOTES AND COMMENTS VERY SIMPLE MARKOV-PERFECT INDUSTRY DYNAMICS: THEORY JAAP H. ABBRING CentER, Department of Econometrics & OR, Tilburg University and CEPR JEFFREY R. CAMPBELL Economic Research, Federal Reserve Bank of Chicago and CentER, Tilburg University JAN TILLY Department of Economics, University of Pennsylvania NAN YANG Department of Strategy & Policy and Department of Marketing, National University of Singapore This paper develops a simple model of firm entry, competition, and exit in oligopolis- tic markets. It features toughness of competition, sunk entry costs, and market-level de- mand and cost shocks, but assumes that firms’ expected payoffs are identical when en- try and survival decisions are made. -
Markov Distributional Equilibrium Dynamics in Games with Complementarities and No Aggregate Risk*
Markov distributional equilibrium dynamics in games with complementarities and no aggregate risk* Lukasz Balbus Pawe lDziewulski Kevin Reffettx Lukasz Wo´zny¶ June 2020 Abstract We present a new approach for studying equilibrium dynamics in a class of stochastic games with a continuum of players with private types and strategic com- plementarities. We introduce a suitable equilibrium concept, called Markov Sta- tionary Distributional Equilibrium (MSDE), prove its existence as well as provide constructive methods for characterizing and comparing equilibrium distributional transitional dynamics. In order to analyze equilibrium transitions for the distri- butions of private types, we provide an appropriate dynamic (exact) law of large numbers. We also show our models can be approximated as idealized limits of games with a large (but finite) number of players. We provide numerous applications of the results including to dynamic models of growth with status concerns, social distance, and paternalistic bequest with endogenous preference for consumption. Keywords: large games, distributional equilibria, supermodular games, compara- tive dynamics, non-aggregative games, law of large numbers, social interactions JEL classification: C62, C72, C73 *We want to thank Rabah Amir, Eric Balder, Michael Greinecker, Martin Jensen, Ali Khan, and Manuel Santos for valuable discussion during the writing of this paper, as well as participants of ANR Nuvo Tempo Workshop on Recursive Methods 2015 in Phoenix, Society for Advancement of Economic Theory 2015 Conference in Cambridge, European Workshop in General Equilibrium Theory 2017 in Salamanca, UECE Lisbon Meetings in Game Theory and Applications 2017, Lancaster Game Theory Conference 2018, Stony Brook Game Theory Conference 2019 for interesting comments on the earlier drafts of this paper. -
Mean Field Asymptotic of Markov Decision Evolutionary Games and Teams
Mean Field Asymptotic of Markov Decision Evolutionary Games and Teams Hamidou Tembine, Jean-Yves Le Boudec, Rachid El-Azouzi, Eitan Altman ¤† Abstract as the transition probabilities of a controlled Markov chain associated with each player. Each player wishes to maxi- We introduce Markov Decision Evolutionary Games with mize its expected fitness averaged over time. N players, in which each individual in a large population This model extends the basic evolutionary games by in- interacts with other randomly selected players. The states troducing a controlled state that characterizes each player. and actions of each player in an interaction together de- The stochastic dynamic games at each interaction replace termine the instantaneous payoff for all involved players. the matrix games, and the objective of maximizing the ex- They also determine the transition probabilities to move to pected long-term payoff over an infinite time horizon re- the next state. Each individual wishes to maximize the to- places the objective of maximizing the outcome of a matrix tal expected discounted payoff over an infinite horizon. We game. Instead of a choice of a (possibly mixed) action, a provide a rigorous derivation of the asymptotic behavior player is now faced with the choice of decision rules (called of this system as the size of the population grows to infin- strategies) that determine what actions should be chosen at ity. We show that under any Markov strategy, the random a given interaction for given present and past observations. process consisting of one specific player and the remaining This model with a finite number of players, called a population converges weakly to a jump process driven by mean field interaction model, is in general difficult to an- the solution of a system of differential equations. -
Coordination with Local Information
CORRECTED VERSION OF RECORD; SEE LAST PAGE OF ARTICLE OPERATIONS RESEARCH Vol. 64, No. 3, May–June 2016, pp. 622–637 ISSN 0030-364X (print) ISSN 1526-5463 (online) ó http://dx.doi.org/10.1287/opre.2015.1378 © 2016 INFORMS Coordination with Local Information Munther A. Dahleh Laboratory for Information and Decision Systems, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, [email protected] Alireza Tahbaz-Salehi Columbia Business School, Columbia University, New York, New York 10027, [email protected] John N. Tsitsiklis Laboratory for Information and Decision Systems, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, [email protected] Spyros I. Zoumpoulis Decision Sciences Area, INSEAD, 77300 Fontainebleau, France, [email protected] We study the role of local information channels in enabling coordination among strategic agents. Building on the standard finite-player global games framework, we show that the set of equilibria of a coordination game is highly sensitive to how information is locally shared among different agents. In particular, we show that the coordination game has multiple equilibria if there exists a collection of agents such that (i) they do not share a common signal with any agent outside of that collection and (ii) their information sets form an increasing sequence of nested sets. Our results thus extend the results on the uniqueness and multiplicity of equilibria beyond the well-known cases in which agents have access to purely private or public signals. We then provide a characterization of the set of equilibria as a function of the penetration of local information channels. We show that the set of equilibria shrinks as information becomes more decentralized.