Potential Games and Competition in the Supply of Natural Resources By
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DUOPOLY MICROECONOMICS Principles and Analysis Frank Cowell
Prerequisites Almost essential Monopoly Useful, but optional Game Theory: Strategy and Equilibrium DUOPOLY MICROECONOMICS Principles and Analysis Frank Cowell April 2018 Frank Cowell: Duopoly 1 Overview Duopoly Background How the basic elements of the Price firm and of game competition theory are used Quantity competition Assessment April 2018 Frank Cowell: Duopoly 2 Basic ingredients . Two firms: • issue of entry is not considered • but monopoly could be a special limiting case . Profit maximisation . Quantities or prices? • there’s nothing within the model to determine which “weapon” is used • it’s determined a priori • highlights artificiality of the approach . Simple market situation: • there is a known demand curve • single, homogeneous product April 2018 Frank Cowell: Duopoly 3 Reaction . We deal with “competition amongst the few” . Each actor has to take into account what others do . A simple way to do this: the reaction function . Based on the idea of “best response” • we can extend this idea • in the case where more than one possible reaction to a particular action • it is then known as a reaction correspondence . We will see how this works: • where reaction is in terms of prices • where reaction is in terms of quantities April 2018 Frank Cowell: Duopoly 4 Overview Duopoly Background Introduction to a simple simultaneous move Price price-setting problem competitionCompetition Quantity competition Assessment April 2018 Frank Cowell: Duopoly 5 Competing by price . Simplest version of model: • there is a market for a single, homogeneous good • firms announce prices • each firm does not know the other’s announcement when making its own . Total output is determined by demand • determinate market demand curve • known to the firms . -
CSC304 Lecture 4 Game Theory
CSC304 Lecture 4 Game Theory (Cost sharing & congestion games, Potential function, Braess’ paradox) CSC304 - Nisarg Shah 1 Recap • Nash equilibria (NE) ➢ No agent wants to change their strategy ➢ Guaranteed to exist if mixed strategies are allowed ➢ Could be multiple • Pure NE through best-response diagrams • Mixed NE through the indifference principle CSC304 - Nisarg Shah 2 Worst and Best Nash Equilibria • What can we say after we identify all Nash equilibria? ➢ Compute how “good” they are in the best/worst case • How do we measure “social good”? ➢ Game with only rewards? Higher total reward of players = more social good ➢ Game with only penalties? Lower total penalty to players = more social good ➢ Game with rewards and penalties? No clear consensus… CSC304 - Nisarg Shah 3 Price of Anarchy and Stability • Price of Anarchy (PoA) • Price of Stability (PoS) “Worst NE vs optimum” “Best NE vs optimum” Max total reward Max total reward Min total reward in any NE Max total reward in any NE or or Max total cost in any NE Min total cost in any NE Min total cost Min total cost PoA ≥ PoS ≥ 1 CSC304 - Nisarg Shah 4 Revisiting Stag-Hunt Hunter 2 Stag Hare Hunter 1 Stag (4 , 4) (0 , 2) Hare (2 , 0) (1 , 1) • Max total reward = 4 + 4 = 8 • Three equilibria ➢ (Stag, Stag) : Total reward = 8 ➢ (Hare, Hare) : Total reward = 2 1 2 1 2 ➢ ( Τ3 Stag – Τ3 Hare, Τ3 Stag – Τ3 Hare) 1 1 1 1 o Total reward = ∗ ∗ 8 + 1 − ∗ ∗ 2 ∈ (2,8) 3 3 3 3 • Price of stability? Price of anarchy? CSC304 - Nisarg Shah 5 Revisiting Prisoner’s Dilemma John Stay Silent Betray Sam -
Potential Games. Congestion Games. Price of Anarchy and Price of Stability
8803 Connections between Learning, Game Theory, and Optimization Maria-Florina Balcan Lecture 13: October 5, 2010 Reading: Algorithmic Game Theory book, Chapters 17, 18 and 19. Price of Anarchy and Price of Staility We assume a (finite) game with n players, where player i's set of possible strategies is Si. We let s = (s1; : : : ; sn) denote the (joint) vector of strategies selected by players in the space S = S1 × · · · × Sn of joint actions. The game assigns utilities ui : S ! R or costs ui : S ! R to any player i at any joint action s 2 S: any player maximizes his utility ui(s) or minimizes his cost ci(s). As we recall from the introductory lectures, any finite game has a mixed Nash equilibrium (NE), but a finite game may or may not have pure Nash equilibria. Today we focus on games with pure NE. Some NE are \better" than others, which we formalize via a social objective function f : S ! R. Two classic social objectives are: P sum social welfare f(s) = i ui(s) measures social welfare { we make sure that the av- erage satisfaction of the population is high maxmin social utility f(s) = mini ui(s) measures the satisfaction of the most unsatisfied player A social objective function quantifies the efficiency of each strategy profile. We can now measure how efficient a Nash equilibrium is in a specific game. Since a game may have many NE we have at least two natural measures, corresponding to the best and the worst NE. We first define the best possible solution in a game Definition 1. -
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 .................................. -
Microeconomics III Oligopoly — Preface to Game Theory
Microeconomics III Oligopoly — prefacetogametheory (Mar 11, 2012) School of Economics The Interdisciplinary Center (IDC), Herzliya Oligopoly is a market in which only a few firms compete with one another, • and entry of new firmsisimpeded. The situation is known as the Cournot model after Antoine Augustin • Cournot, a French economist, philosopher and mathematician (1801-1877). In the basic example, a single good is produced by two firms (the industry • is a “duopoly”). Cournot’s oligopoly model (1838) — A single good is produced by two firms (the industry is a “duopoly”). — The cost for firm =1 2 for producing units of the good is given by (“unit cost” is constant equal to 0). — If the firms’ total output is = 1 + 2 then the market price is = − if and zero otherwise (linear inverse demand function). We ≥ also assume that . The inverse demand function P A P=A-Q A Q To find the Nash equilibria of the Cournot’s game, we can use the proce- dures based on the firms’ best response functions. But first we need the firms payoffs(profits): 1 = 1 11 − =( )1 11 − − =( 1 2)1 11 − − − =( 1 2 1)1 − − − and similarly, 2 =( 1 2 2)2 − − − Firm 1’s profit as a function of its output (given firm 2’s output) Profit 1 q'2 q2 q2 A c q A c q' Output 1 1 2 1 2 2 2 To find firm 1’s best response to any given output 2 of firm 2, we need to study firm 1’s profit as a function of its output 1 for given values of 2. -
Online Learning and Game Theory
Online Learning and Game Theory Your guide: Avrim Blum Carnegie Mellon University [Algorithmic Economics Summer School 2012] Itinerary • Stop 1: Minimizing regret and combining advice. – Randomized Wtd Majority / Multiplicative Weights alg – Connections to minimax-optimality in zero-sum games • Stop 2: Bandits and Pricing – Online learning from limited feedback (bandit algs) – Use for online pricing/auction problems • Stop 3: Internal regret & Correlated equil – Internal regret minimization – Connections to correlated equilib. in general-sum games • Stop 4: Guiding dynamics to higher-quality equilibria – Helpful “nudging” of simple dynamics in potential games Stop 1: Minimizing regret and combining expert advice Consider the following setting… Each morning, you need to pick one of N possible routes to drive to work. Robots But traffic is different each day. R Us 32 min Not clear a priori which will be best. When you get there you find out how long your route took. (And maybe others too or maybe not.) Is there a strategy for picking routes so that in the long run, whatever the sequence of traffic patterns has been, you’ve done nearly as well as the best fixed route in hindsight? (In expectation, over internal randomness in the algorithm) Yes. “No-regret” algorithms for repeated decisions A bit more generally: Algorithm has N options. World chooses cost vector. Can view as matrix like this (maybe infinite # cols) World – life - fate Algorithm At each time step, algorithm picks row, life picks column. Alg pays cost for action chosen. Alg gets column as feedback (or just its own cost in the “bandit” model). Need to assume some bound on max cost. -
Quality and Welfare in a Mixed Duopoly with Regulated Prices: the Case of a Public and a Private Hospital
A Service of Leibniz-Informationszentrum econstor Wirtschaft Leibniz Information Centre Make Your Publications Visible. zbw for Economics Herr, Annika Working Paper Quality and welfare in a mixed duopoly with regulated prices: The case of a public and a private hospital DICE Discussion Paper, No. 07 Provided in Cooperation with: Düsseldorf Institute for Competition Economics (DICE) Suggested Citation: Herr, Annika (2010) : Quality and welfare in a mixed duopoly with regulated prices: The case of a public and a private hospital, DICE Discussion Paper, No. 07, ISBN 978-3-86304-006-2, Heinrich Heine University Düsseldorf, Düsseldorf Institute for Competition Economics (DICE), Düsseldorf This Version is available at: http://hdl.handle.net/10419/41424 Standard-Nutzungsbedingungen: Terms of use: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Documents in EconStor may be saved and copied for your Zwecken und zum Privatgebrauch gespeichert und kopiert werden. personal and scholarly purposes. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle You are not to copy documents for public or commercial Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich purposes, to exhibit the documents publicly, to make them machen, vertreiben oder anderweitig nutzen. publicly available on the internet, or to distribute or otherwise use the documents in public. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, If the documents have been made -
Potential Games with Continuous Player Sets
Potential Games with Continuous Player Sets William H. Sandholm* Department of Economics University of Wisconsin 1180 Observatory Drive Madison, WI 53706 [email protected] www.ssc.wisc.edu/~whs First Version: April 30, 1997 This Version: May 20, 2000 * This paper is based on Chapter 4 of my doctoral dissertation (Sandholm [29]). I thank an anonymous referee and Associate Editor, Josef Hofbauer, and seminar audiences at Caltech, Chicago, Columbia, Harvard (Kennedy), Michigan, Northwestern, Rochester, Washington University, and Wisconsin for their comments. I especially thank Eddie Dekel and Jeroen Swinkels for their advice and encouragement. Financial support from a State Farm Companies Foundation Dissertation Fellowship is gratefully acknowledged. Abstract We study potential games with continuous player sets, a class of games characterized by an externality symmetry condition. Examples of these games include random matching games with common payoffs and congestion games. We offer a simple description of equilibria which are locally stable under a broad class of evolutionary dynamics, and prove that behavior converges to Nash equilibrium from all initial conditions. W e consider a subclass of potential games in which evolution leads to efficient play. Finally, we show that the games studied here are the limits of convergent sequences of the finite player potential games studied by Monderer and Shapley [22]. JEL Classification Numbers: C72, C73, D62, R41. 1. Introduction Nash equilibrium is the cornerstone of non-cooperative game theory, providing a necessary condition for stable behavior among rational agents. Still, to justify the prediction of Nash equilibrium play, one must explain how players arrive at a Nash equilibrium; if equilibrium is not reached, the fact that it is self-sustaining becomes moot. -
Noisy Directional Learning and the Logit Equilibrium*
CSE: AR SJOE 011 Scand. J. of Economics 106(*), *–*, 2004 DOI: 10.1111/j.1467-9442.2004.000376.x Noisy Directional Learning and the Logit Equilibrium* Simon P. Anderson University of Virginia, Charlottesville, VA 22903-3328, USA [email protected] Jacob K. Goeree California Institute of Technology, Pasadena, CA 91125, USA [email protected] Charles A. Holt University of Virginia, Charlottesville, VA 22903-3328, USA [email protected] PROOF Abstract We specify a dynamic model in which agents adjust their decisions toward higher payoffs, subject to normal error. This process generates a probability distribution of players’ decisions that evolves over time according to the Fokker–Planck equation. The dynamic process is stable for all potential games, a class of payoff structures that includes several widely studied games. In equilibrium, the distributions that determine expected payoffs correspond to the distributions that arise from the logit function applied to those expected payoffs. This ‘‘logit equilibrium’’ forms a stochastic generalization of the Nash equilibrium and provides a possible explanation of anomalous laboratory data. ECTED Keywords: Bounded rationality; noisy directional learning; Fokker–Planck equation; potential games; logit equilibrium JEL classification: C62; C73 I. Introduction Small errors and shocks may have offsetting effects in some economic con- texts, in which case there is not much to be gained from an explicit analysis of stochasticNCORR elements. In other contexts, a small amount of randomness can * We gratefully acknowledge financial support from the National Science Foundation (SBR- 9818683U and SBR-0094800), the Alfred P. Sloan Foundation and the Dutch National Science Foundation (NWO-VICI 453.03.606). -
WHY COMPETITION in the POLITICS INDUSTRY IS FAILING AMERICA a Strategy for Reinvigorating Our Democracy
SEPTEMBER 2017 WHY COMPETITION IN THE POLITICS INDUSTRY IS FAILING AMERICA A strategy for reinvigorating our democracy Katherine M. Gehl and Michael E. Porter ABOUT THE AUTHORS Katherine M. Gehl, a business leader and former CEO with experience in government, began, in the last decade, to participate actively in politics—first in traditional partisan politics. As she deepened her understanding of how politics actually worked—and didn’t work—for the public interest, she realized that even the best candidates and elected officials were severely limited by a dysfunctional system, and that the political system was the single greatest challenge facing our country. She turned her focus to political system reform and innovation and has made this her mission. Michael E. Porter, an expert on competition and strategy in industries and nations, encountered politics in trying to advise governments and advocate sensible and proven reforms. As co-chair of the multiyear, non-partisan U.S. Competitiveness Project at Harvard Business School over the past five years, it became clear to him that the political system was actually the major constraint in America’s inability to restore economic prosperity and address many of the other problems our nation faces. Working with Katherine to understand the root causes of the failure of political competition, and what to do about it, has become an obsession. DISCLOSURE This work was funded by Harvard Business School, including the Institute for Strategy and Competitiveness and the Division of Research and Faculty Development. No external funding was received. Katherine and Michael are both involved in supporting the work they advocate in this report. -
Strong Nash Equilibria in Games with the Lexicographical Improvement
Strong Nash Equilibria in Games with the Lexicographical Improvement Property Tobias Harks, Max Klimm, and Rolf H. M¨ohring Preprint 15/07/2009 Institut f¨ur Mathematik, Technische Universit¨at Berlin harks,klimm,moehring @math.tu-berlin.de { } Abstract. We introduce a class of finite strategic games with the property that every deviation of a coalition of players that is profitable to each of its members strictly decreases the lexicographical order of a certain function defined on the set of strategy profiles. We call this property the Lexicographical Improvement Property (LIP) and show that it implies the existence of a generalized strong ordinal potential function. We use this characterization to derive existence, efficiency and fairness properties of strong Nash equilibria. We then study a class of games that generalizes congestion games with bottleneck objectives that we call bot- tleneck congestion games. We show that these games possess the LIP and thus the above mentioned properties. For bottleneck congestion games in networks, we identify cases in which the potential function associated with the LIP leads to polynomial time algorithms computing a strong Nash equilibrium. Finally, we investigate the LIP for infinite games. We show that the LIP does not imply the existence of a generalized strong ordinal potential, thus, the existence of SNE does not follow. Assuming that the function associated with the LIP is continuous, however, we prove existence of SNE. As a consequence, we prove that bottleneck congestion games with infinite strategy spaces and continuous cost functions possess a strong Nash equilibrium. 1 Introduction The theory of non-cooperative games is used to study situations that involve rational and selfish agents who are motivated by optimizing their own utilities rather than reaching some social optimum. -
Dynamics of Profit-Sharing Games
Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence Dynamics of Profit-Sharing Games John Augustine, Ning Chen, Edith Elkind, Angelo Fanelli, Nick Gravin, Dmitry Shiryaev School of Physical and Mathematical Sciences Nanyang Technological University, Singapore Abstract information about other players. Such agents may simply re- spond to their current environment without worrying about An important task in the analysis of multiagent sys- the subsequent reaction of other agents; such behavior is said tems is to understand how groups of selfish players to be myopic. Now, coalition formation by computationally can form coalitions, i.e., work together in teams. In limited agents has been studied by a number of researchers in this paper, we study the dynamics of coalition for- multi-agent systems, starting with the work of [Shehory and mation under bounded rationality. We consider set- Kraus, 1999] and [Sandholm and Lesser, 1997]. However, tings where each team’s profit is given by a concave myopic behavior in coalition formation received relatively lit- function, and propose three profit-sharing schemes, tle attention in the literature (for some exceptions, see [Dieck- each of which is based on the concept of marginal mann and Schwalbe, 2002; Chalkiadakis and Boutilier, 2004; utility. The agents are assumed to be myopic, i.e., Airiau and Sen, 2009]). In contrast, myopic dynamics of they keep changing teams as long as they can in- non-cooperative games is the subject of a growing body of crease their payoff by doing so. We study the prop- research (see, e.g. [Fabrikant et al., 2004; Awerbuch et al., erties (such as closeness to Nash equilibrium or to- 2008; Fanelli et al., 2008]).