Coordination with Local Information∗
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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). -
Prophylaxy Copie.Pdf
Social interactions and the prophylaxis of SI epidemics on networks Géraldine Bouveret, Antoine Mandel To cite this version: Géraldine Bouveret, Antoine Mandel. Social interactions and the prophylaxis of SI epi- demics on networks. Journal of Mathematical Economics, Elsevier, 2021, 93, pp.102486. 10.1016/j.jmateco.2021.102486. halshs-03165772 HAL Id: halshs-03165772 https://halshs.archives-ouvertes.fr/halshs-03165772 Submitted on 17 Mar 2021 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Social interactions and the prophylaxis of SI epidemics on networkssa G´eraldineBouveretb Antoine Mandel c March 17, 2021 Abstract We investigate the containment of epidemic spreading in networks from a nor- mative point of view. We consider a susceptible/infected model in which agents can invest in order to reduce the contagiousness of network links. In this setting, we study the relationships between social efficiency, individual behaviours and network structure. First, we characterise individual and socially efficient behaviour using the notions of communicability and exponential centrality. Second, we show, by computing the Price of Anarchy, that the level of inefficiency can scale up to lin- early with the number of agents. -
(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. -
Statistical GGP Game Decomposition Aline Hufschmitt, Jean-Noël Vittaut, Nicolas Jouandeau
Statistical GGP Game Decomposition Aline Hufschmitt, Jean-Noël Vittaut, Nicolas Jouandeau To cite this version: Aline Hufschmitt, Jean-Noël Vittaut, Nicolas Jouandeau. Statistical GGP Game Decomposition. Computer Games - 7th Workshop, CGW 2018, Held in Conjunction with the 27th International Con- ference on Artificial Intelligence, IJCAI 2018, Jul 2018, Stockholm, Sweden. pp.79-97, 10.1007/978- 3-030-24337-1_4. hal-02182443 HAL Id: hal-02182443 https://hal.archives-ouvertes.fr/hal-02182443 Submitted on 15 Oct 2019 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. In Proceedings of the IJCAI-18 Workshop on Computer Games (CGW 2018) Statistical GGP Game Decomposition Aline Hufschmitt, Jean-No¨elVittaut, and Nicolas Jouandeau LIASD - University of Paris 8, France falinehuf,jnv,[email protected] Abstract. This paper presents a statistical approach for the decompo- sition of games in the General Game Playing framework. General game players can drastically decrease game search cost if they hold a decom- posed version of the game. Previous works on decomposition rely on syn- tactical structures, which can be missing from the game description, or on the disjunctive normal form of the rules, which is very costly to compute. -
Common Belief Foundations of Global Games∗
Common Belief Foundations of Global Games Stephen Morris Hyun Song Shin Muhamet Yildiz Princeton University Bank of International Settlements M.I.T. January 2015 Abstract Complete information games– i.e., games where there is common certainty of payoffs– often have multiple rationalizable actions. What happens if the common certainty of payoffs assumption is relaxed? Consider a two player game where a player’spayoff depends on a payoff parameter. Define a player’s"rank belief" as the probability he assigns to his payoff parameter being higher than his opponent’s. We show that there is a unique rationalizable action played when there is common certainty of rank beliefs, and we argue this is the driving force behind selection results in the global games literature. We consider what happens if players’payoff parameters are derived from common and idiosyncratic terms with fat tailed distributions, and we approach complete information by shrinking the distribution of both terms. There are two cases. If the common component has a thicker tails, then there is common certainty of rank beliefs, and thus uniqueness and equilibrium selection. If the idiosyncratic terms has thicker tails, then there is approximate common certainty of payoffs, and thus multiple rationalizable actions. 1 Introduction Complete information games often have many equilibria. Even when they have a single equilibrium, they often have many actions that are rationalizable, and are therefore consistent with common This paper incorporates material from a working paper of the same title circulated in 2009 (Morris and Shin (2009)). We are grateful for comments from seminar participants at Northwestern on this iteration of the project. -
Essays on Supermodular Games, Design, and Political Theory
Selection, Learning, and Nomination: Essays on Supermodular Games, Design, and Political Theory Thesis by Laurent Mathevet In Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy California Institute of Technology Pasadena, California 2008 (Defended May 15th, 2008) ii c 2008 Laurent Mathevet All Rights Reserved iii For my parents, Denise and Ren´eMathevet iv Acknowledgements Many people have helped me along the way. Christelle has been a source of happiness, and without her uncompromising support and faith in me, I would have been miserable. My parents, to whom this dissertation is dedicated, have shown continual and unconditional support despite the distance. They are a constant source of inspiration. I also wish to thank my advisors, Federico Echenique and Matthew Jackson, for their help and encouragement. Federico has spent numerous hours advising me, meeting with me nearly every week since my second year as a graduate student. He has endeavored to extract the most out of me, discarding unpromising thoughts and results, reading my drafts countless times and demanding (countless!) rewrites. Most importantly, he found the right words in those difficult times of a PhD graduate student. Matt has also been a wonderful advisor, available, demanding, and encouraging. I have benefited greatly from his comments, which are the kind that shapes the core of a research project. Matt has funded me for several years, which allowed me to concentrate on research. I also thank him for his invitation to spend a term at Stanford to further my research. I am indebted to Preston McAfee, whose personality, charisma, and sharpness have made meetings with him among the most fruitful and enjoyable. -
Game Theory for Signal Processing in Networks
Game Theory for Signal Processing in Networks Giacomo Bacci, Samson Lasaulce, Walid Saad, and Luca Sanguinetti Abstract In this tutorial, the basics of game theory are introduced along with an overview of its most recent and emerging applications in signal processing. One of the main features of this contribution is to gather in a single paper some fundamental game-theoretic notions and tools which, over the past few years, have become widely spread over a large number of papers. In particular, both strategic-form and coalition-form games are described in details while the key connections and differences betweenthemareoutlined.Moreover,aparticularattention is also devoted to clarify the connections between strategic-form games and distributed optimization and learning algorithms. Beyond an introduction to the basic concepts andmainsolutionapproaches,severalcarefullydesigned examples are provided to allow a better understanding of how to apply the described tools. I. INTRODUCTION game theory (GT) is a branch of mathematics that enables the modeling and analysis of the interactions between several decision-makers (called players) who can have conflicting or common objectives. A game is a situation in which the benefit or cost achieved by each player from an interactive situation depends, not only on its own decisions, but also on those taken by the other players. For example, the time a car driver needs to get back home generally depends not only on the route he/she chooses but also on the decisions taken by the other drivers. Therefore, in a game, the actions and objectives of the players are tightly coupled.Untilveryrecently,GThas been used only marginally in signal processing (SP), with notable examples being some applications in robust detection and estimation [1] as well as watermarking [2] (in which the watermarking problem is seen as a game between the data embedder and the attacker). -
Convergence to Approximate Nash Equilibria in Congestion Games†
Convergence to Approximate Nash Equilibria in Congestion Games† Steve Chien‡ Alistair Sinclair§ Abstract We study the ability of decentralized, local dynamics in non-cooperative games to rapidly reach an approximate Nash equilibrium. For symmetric congestion games in which the edge delays satisfy a “bounded jump” condition, we show that convergence to an ε-Nash equilibrium occurs within a number of steps that is polynomial in the number of players and ε−1. This appears to be the first such result for a class of games that includes examples for which finding an exact Nash equilibrium is PLS-complete, and in which shortest paths to an exact equilibrium are exponentially long. We show moreover that rapid convergence holds even under only the apparently minimal assumption that no player is excluded from moving for arbitrarily many steps. We also prove that, in a generalized setting where players have different “tolerances” εi that specify their thresholds in the approximate Nash equilibrium, the number of moves made by a player before equilibrium is reached depends only on his associated εi, and not on those of the other players. Finally, we show that polynomial time convergence still holds even when a bounded number of edges are allowed to have arbitrary delay functions. 1 Introduction The emerging field of algorithmic game theory has led to a fundamental re-examination, from a computational perspective, of the classical concept of Nash equilibrium [20]. Much of this activity has focused on understanding the structure of Nash equilibria (as expressed, notably, in the “price of anarchy,” see e.g. [22, 25, 24]) and the computational complexity of finding them (see, e.g., [10, 7, 4]). -
Bounded Rationality in Keynesian Beauty Contests: a Lesson for Central Bankers?
Vol. 14, 2020-16 | June 04, 2020 | http://dx.doi.org/10.5018/economics-ejournal.ja.2020-16 Bounded rationality in Keynesian beauty contests: a lesson for central bankers? Felix Mauersberger, Rosemarie Nagel, and Christoph Bühren Abstract The great recession (2008) triggered an apparent discrepancy between empirical findings and macroeconomic models based on rational expectations alone. This gap led to a series of recent developments of a behavioral microfoundation of macroeconomics combined with the underlying experimental and behavioral Beauty Contest (BC) literature, which the authors review in this paper. They introduce the reader to variations of the Keynesian Beauty Contest (Keynes, The general theory of employment, interest, and money, 1936), theoretically and experimentally, demonstrating systematic patterns of out-of-equilibrium behavior. This divergence of (benchmark) solutions and bounded rationality observed in human behavior has been resolved through stepwise reasoning, the so-called level k, or cognitive hierarchy models. Furthermore, the authors show how the generalized BC function with limited parameter specifications encompasses relevant micro and macro models. Therefore, the stepwise reasoning models emerge naturally as building blocks for new behavioral macroeconomic theories to understand puzzles like the lacking rise of inflation after the financial crisis, the efficacy of quantitative easing, the forward guidance puzzle, and the effectiveness of temporary fiscal expansion. (Published in Special Issue Bio-psycho-social foundations of macroeconomics) JEL E12 E13 E7 D80 D9 C91 Keywords Beauty Contest game; expectation formation; equilibration; level k reasoning; behavioral macroeconomics; game theory; experimental economics Authors Felix Mauersberger, University of Bonn, Germany Rosemarie Nagel, ICREA-UPF Barcelona GSE, Spain, [email protected] Christoph Bühren, Clausthal University of Technology, Germany Citation Felix Mauersberger, Rosemarie Nagel, and Christoph Bühren (2020). -
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.