Formation of Coalition Structures As a Non-Cooperative Game
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Labsi Working Papers
UNIVERSITY OF SIENA S.N. O’ H iggins Arturo Palomba Patrizia Sbriglia Second Mover Advantage and Bertrand Dynamic Competition: An Experiment May 2010 LABSI WORKING PAPERS N. 28/2010 SECOND MOVER ADVANTAGE AND BERTRAND DYNAMIC COMPETITION: AN EXPERIMENT § S.N. O’Higgins University of Salerno [email protected] Arturo Palomba University of Naples II [email protected] Patrizia Sbriglia §§ University of Naples II [email protected] Abstract In this paper we provide an experimental test of a dynamic Bertrand duopolistic model, where firms move sequentially and their informational setting varies across different designs. Our experiment is composed of three treatments. In the first treatment, subjects receive information only on the costs and demand parameters and on the price’ choices of their opponent in the market in which they are positioned (matching is fixed); in the second and third treatments, subjects are also informed on the behaviour of players who are not directly operating in their market. Our aim is to study whether the individual behaviour and the process of equilibrium convergence are affected by the specific informational setting adopted. In all treatments we selected students who had previously studied market games and industrial organization, conjecturing that the specific participants’ expertise decreased the chances of imitation in treatment II and III. However, our results prove the opposite: the extra information provided in treatment II and III strongly affects the long run convergence to the market equilibrium. In fact, whilst in the first session, a high proportion of markets converge to the Nash-Bertrand symmetric solution, we observe that a high proportion of markets converge to more collusive outcomes in treatment II and more competitive outcomes in treatment III. -
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. -
Finitely Repeated Games
Repeated games 1: Finite repetition Universidad Carlos III de Madrid 1 Finitely repeated games • A finitely repeated game is a dynamic game in which a simultaneous game (the stage game) is played finitely many times, and the result of each stage is observed before the next one is played. • Example: Play the prisoners’ dilemma several times. The stage game is the simultaneous prisoners’ dilemma game. 2 Results • If the stage game (the simultaneous game) has only one NE the repeated game has only one SPNE: In the SPNE players’ play the strategies in the NE in each stage. • If the stage game has 2 or more NE, one can find a SPNE where, at some stage, players play a strategy that is not part of a NE of the stage game. 3 The prisoners’ dilemma repeated twice • Two players play the same simultaneous game twice, at ! = 1 and at ! = 2. • After the first time the game is played (after ! = 1) the result is observed before playing the second time. • The payoff in the repeated game is the sum of the payoffs in each stage (! = 1, ! = 2) • Which is the SPNE? Player 2 D C D 1 , 1 5 , 0 Player 1 C 0 , 5 4 , 4 4 The prisoners’ dilemma repeated twice Information sets? Strategies? 1 .1 5 for each player 2" for each player D C E.g.: (C, D, D, C, C) Subgames? 2.1 5 D C D C .2 1.3 1.5 1 1.4 D C D C D C D C 2.2 2.3 2 .4 2.5 D C D C D C D C D C D C D C D C 1+1 1+5 1+0 1+4 5+1 5+5 5+0 5+4 0+1 0+5 0+0 0+4 4+1 4+5 4+0 4+4 1+1 1+0 1+5 1+4 0+1 0+0 0+5 0+4 5+1 5+0 5+5 5+4 4+1 4+0 4+5 4+4 The prisoners’ dilemma repeated twice Let’s find the NE in the subgames. -
Chapter 16 Oligopoly and Game Theory Oligopoly Oligopoly
Chapter 16 “Game theory is the study of how people Oligopoly behave in strategic situations. By ‘strategic’ we mean a situation in which each person, when deciding what actions to take, must and consider how others might respond to that action.” Game Theory Oligopoly Oligopoly • “Oligopoly is a market structure in which only a few • “Figuring out the environment” when there are sellers offer similar or identical products.” rival firms in your market, means guessing (or • As we saw last time, oligopoly differs from the two ‘ideal’ inferring) what the rivals are doing and then cases, perfect competition and monopoly. choosing a “best response” • In the ‘ideal’ cases, the firm just has to figure out the environment (prices for the perfectly competitive firm, • This means that firms in oligopoly markets are demand curve for the monopolist) and select output to playing a ‘game’ against each other. maximize profits • To understand how they might act, we need to • An oligopolist, on the other hand, also has to figure out the understand how players play games. environment before computing the best output. • This is the role of Game Theory. Some Concepts We Will Use Strategies • Strategies • Strategies are the choices that a player is allowed • Payoffs to make. • Sequential Games •Examples: • Simultaneous Games – In game trees (sequential games), the players choose paths or branches from roots or nodes. • Best Responses – In matrix games players choose rows or columns • Equilibrium – In market games, players choose prices, or quantities, • Dominated strategies or R and D levels. • Dominant Strategies. – In Blackjack, players choose whether to stay or draw. -
Multi-Pose Interactive Linkage Design Teaser
EUROGRAPHICS 2019 / P. Alliez and F. Pellacini Volume 38 (2019), Number 2 (Guest Editors) Multi-Pose Interactive Linkage Design Teaser G. Nishida1 , A. Bousseau2 , D. G. Aliaga1 1Purdue University, USA 2Inria, France a) b) e) f) Fixed bodies Moving bodies c) d) Figure 1: Interactive design of a multi-pose and multi-body linkage. Our system relies on a simple 2D drawing interface to let the user design a multi-body object including (a) fixed bodies, (b) moving bodies (green and purple shapes), (c) multiple poses for each moving body to specify the desired motion, and (d) a desired region for the linkage mechanism. Then, our system automatically generates a 2.5D linkage mechanism that makes the moving bodies traverse all input poses in a desired order without any collision (e). The system also automatically generates linkage parts ready for 3D printing and assembly (f). Please refer to the accompanying video for a demonstration of the sketching interface and animations of the resulting mechanisms. Abstract We introduce an interactive tool for novice users to design mechanical objects made of 2.5D linkages. Users simply draw the shape of the object and a few key poses of its multiple moving parts. Our approach automatically generates a one-degree-of- freedom linkage that connects the fixed and moving parts, such that the moving parts traverse all input poses in order without any collision with the fixed and other moving parts. In addition, our approach avoids common linkage defects and favors compact linkages and smooth motion trajectories. Finally, our system automatically generates the 3D geometry of the object and its links, allowing the rapid creation of a physical mockup of the designed object. -
Computing in Mechanism Design∗
Computing in Mechanism Design¤ Tuomas Sandholm Computer Science Department Carnegie Mellon University Pittsburgh, PA 15213 1. Introduction Computational issues in mechanism design are important, but have received insufficient research interest until recently. Limited computing hinders mechanism design in several ways, and presents deep strategic interactions between com- puting and incentives. On the bright side, the vast increase in computing power has enabled better mechanisms. Perhaps most interestingly, limited computing of the agents can be used as a tool to implement mechanisms that would not be implementable among computationally unlimited agents. This chapter briefly reviews some of the key ideas, with the goal of alerting the reader to the importance of these issues and hopefully spurring future research. I will discuss computing by the center, such as an auction server or vote aggregator, in Section 2. Then, in Section 3, I will address the agents’ computing, be they human or software. 2. Computing by the center Computing by the center plays significant roles in mechanism design. In the following three subsections I will review three prominent directions. 2.1. Executing expressive mechanisms As algorithms have advanced drastically and computing power has increased, it has become feasible to field mecha- nisms that were previously impractical. The most famous example is a combinatorial auction (CA). In a CA, there are multiple distinguishable items for sale, and the bidders can submit bids on self-selected packages of the items. (Sometimes each bidder is also allowed to submit exclusivity constraints of different forms among his bids.) This increase in the expressiveness of the bids drastically reduces the strategic complexity that bidders face. -
Cooperation Spillovers in Coordination Games*
Cooperation Spillovers in Coordination Games* Timothy N. Casona, Anya Savikhina, and Roman M. Sheremetab aDepartment of Economics, Krannert School of Management, Purdue University, 403 W. State St., West Lafayette, IN 47906-2056, U.S.A. bArgyros School of Business and Economics, Chapman University, One University Drive, Orange, CA 92866, U.S.A. November 2009 Abstract Motivated by problems of coordination failure observed in weak-link games, we experimentally investigate behavioral spillovers for order-statistic coordination games. Subjects play the minimum- and median-effort coordination games simultaneously and sequentially. The results show the precedent for cooperative behavior spills over from the median game to the minimum game when the games are played sequentially. Moreover, spillover occurs even when group composition changes, although the effect is not as strong. We also find that the precedent for uncooperative behavior does not spill over from the minimum game to the median game. These findings suggest guidelines for increasing cooperative behavior within organizations. JEL Classifications: C72, C91 Keywords: coordination, order-statistic games, experiments, cooperation, minimum game, behavioral spillover Corresponding author: Timothy Cason, [email protected] * We thank Yan Chen, David Cooper, John Duffy, Vai-Lam Mui, seminar participants at Purdue University, and participants at Economic Science Association conferences for helpful comments. Any remaining errors are ours. 1. Introduction Coordination failure is often the reason for the inefficient performance of many groups, ranging from small firms to entire economies. When agents’ actions have strategic interdependence, even when they succeed in coordinating they may be “trapped” in an equilibrium that is objectively inferior to other equilibria. Coordination failure and inefficient coordination has been an important theme across a variety of fields in economics, ranging from development and macroeconomics to mechanism design for overcoming moral hazard in teams. -
Supermodular Mechanism Design
Theoretical Economics 5(2010),403–443 1555-7561/20100403 Supermodular mechanism design L M Department of Economics, University of Texas This paper introduces a mechanism design approach that allows dealing with the multiple equilibrium problem, using mechanisms that are robust to bounded ra- tionality. This approach is a tool for constructing supermodular mechanisms, i.e., mechanisms that induce games with strategic complementarities. In quasilinear environments, I prove that if a social choice function can be implemented by a mechanism that generates bounded strategic substitutes—as opposed to strate- gic complementarities—then this mechanism can be converted into a supermod- ular mechanism that implements the social choice function. If the social choice function also satisfies some efficiency criterion, then it admits a supermodular mechanism that balances the budget. Building on these results, I address the multiple equilibrium problem. I provide sufficient conditions for a social choice function to be implementable with a supermodular mechanism whose equilibria are contained in the smallest interval among all supermodular mechanisms. This is followed by conditions for supermodular implementability in unique equilib- rium. Finally, I provide a revelation principle for supermodular implementation in environments with general preferences. K.Implementation,mechanisms,multipleequilibriumproblem,learn- ing, strategic complementarities, supermodular games. JEL .C72,D78,D83. 1. I For the economist designing contracts, taxes, or other institutions, there is a dilemma between the simplicity of a mechanism and the multiple equilibrium problem. On the one hand, simple mechanisms often restrict attention to the “right” equilibria. In public good contexts, for example, most tax schemes overlook inefficient equilibrium situa- tions, provided that one equilibrium outcome is a social optimum. -
570: Minimax Sample Complexity for Turn-Based Stochastic Game
Minimax Sample Complexity for Turn-based Stochastic Game Qiwen Cui1 Lin F. Yang2 1School of Mathematical Sciences, Peking University 2Electrical and Computer Engineering Department, University of California, Los Angeles Abstract guarantees are rather rare due to complex interaction be- tween agents that makes the problem considerably harder than single agent reinforcement learning. This is also known The empirical success of multi-agent reinforce- as non-stationarity in MARL, which means when multi- ment learning is encouraging, while few theoret- ple agents alter their strategies based on samples collected ical guarantees have been revealed. In this work, from previous strategy, the system becomes non-stationary we prove that the plug-in solver approach, proba- for each agent and the improvement can not be guaranteed. bly the most natural reinforcement learning algo- One fundamental question in MBRL is that how to design rithm, achieves minimax sample complexity for efficient algorithms to overcome non-stationarity. turn-based stochastic game (TBSG). Specifically, we perform planning in an empirical TBSG by Two-players turn-based stochastic game (TBSG) is a two- utilizing a ‘simulator’ that allows sampling from agents generalization of Markov decision process (MDP), arbitrary state-action pair. We show that the em- where two agents choose actions in turn and one agent wants pirical Nash equilibrium strategy is an approxi- to maximize the total reward while the other wants to min- mate Nash equilibrium strategy in the true TBSG imize it. As a zero-sum game, TBSG is known to have and give both problem-dependent and problem- Nash equilibrium strategy [Shapley, 1953], which means independent bound. -
Chapter on Automated Mechanism Design
Chapter 6 Automated Mechanism Design Mechanism design has traditionally been a manual endeavor. The designer uses experience and intuition to hypothesize that a certain rule set is desirable in some ways, and then tries to prove that this is the case. Alternatively, the designer formulates the mechanism design problem mathemat- ically and characterizes desirable mechanisms analytically in that framework. These approaches have yielded a small number of canonical mechanisms over the last 40 years, the most significant of which we discussed in Chapter 4. Each of these mechanisms is designed for a class of settings and a specific objective. The upside of these mechanisms is that they do not rely on (even probabilistic) information about the agents' preferences (e.g. Vickrey-Clarke-Groves mechanisms), or they can be easily applied to any probability distribution over the preferences (e.g. the dAGVA mechanism, the Myerson auction, and the Maskin-Riley multi-unit auction). However, these general mechanisms also have significant downsides: ² The most famous and most broadly applicable general mechanisms, VCG and dAGVA, only maximize social welfare. If the designer is self-interested, as is the case in many electronic commerce settings, these mechanisms do not maximize the designer's objective. ² The general mechanisms that do focus on a self-interested designer are only applicable in very restricted settings. For example, Myerson's expected revenue maximizing auction is for selling a single item, and Maskin and Riley's expected revenue maximizing auction is for selling multiple identical units of an item. ² Even in the restricted settings in which these mechanisms apply, the mechanisms only allow for payment maximization. -
Finding Strategic Game Equivalent of an Extensive Form Game
Finding Strategic Game Equivalent of an Extensive Form Game • In an extensive form game, a strategy for a player should specify what action the player will choose at each information set. That is, a strategy is a complete plan for playing a game for a particular player. • Therefore to find the strategic game equivalent of an extensive form game we should follow these steps: 1. First we need to find all strategies for every player. To do that first we find all information sets for every player (including the singleton information sets). If there are many of them we may label them (such as Player 1’s 1st info. set, Player 1’s 2nd info. set, etc.) 2. A strategy should specify what action the player will take in every information set which belongs to him/her. We find all combinations of actions the player can take at these information sets. For example if a player has 3 information sets where • the first one has 2 actions, • the second one has 2 actions, and • the third one has 3 actions then there will be a total of 2 × 2 × 3 = 12 strategies for this player. 3. Once the strategies are obtained for every player the next step is finding the payoffs. For every strategy profile we find the payoff vector that would be obtained in case these strategies are played in the extensive form game. Example: Let’s find the strategic game equivalent of the following 3-level centipede game. (3,1)r (2,3)r (6,2)r (4,6)r (12,4)r (8,12)r S S S S S S r 1 2 1 2 1 2 (24,8) C C C C C C 1. -
How to Solve Strategic Games? Dominant Strategies
How to Solve Strategic Games? There are three main concepts to solve strategic games: 1. Dominant Strategies & Dominant Strategy Equilibrium 2. Dominated Strategies & Iterative Elimination of Dominated Strategies 3. Nash Equilibrium Dominant Strategies • Astrategyisadominant strategy for a player if it yields the best payoff (for that player) no matter what strategies the other players choose. • If all players have a dominant strategy, then it is natural for them to choose the dominant strategies and we reach a dominant strategy equilibrium. Example (Prisoner’s Dilemma): Prisoner 2 Confess Deny Prisoner 1 Confess -10, -10 -1, -25 Deny -25, -1 -3, -3 Confess is a dominant strategy for both players and therefore (Confess,Confess) is a dominant strategy equilibrium yielding the payoff vector (-10,-10). Example (Time vs. Newsweek): Newsweek AIDS BUDGET Time AIDS 35,35 70,30 BUDGET 30,70 15,15 The AIDS story is a dominant strategy for both Time and Newsweek. Therefore (AIDS,AIDS) is a dominant strategy equilibrium yielding both magazines a market share of 35 percent. Example: Player 2 XY A 5,2 4,2 Player 1 B 3,1 3,2 C 2,1 4,1 D 4,3 5,4 • Here Player 1 does not have a single strategy that “beats” every other strategy. Therefore she does not have a dominant strategy. • On the other hand Y is a dominant strategy for Player 2. Example (with 3 players): P3 A B P2 P2 LR LR U 3,2,1 2,1,1 U 1,1,2 2,0,1 P1 M 2,2,0 1,2,1 M 1,2,0 1,0,2 D 3,1,2 1,0,2 D 0,2,3 1,2,2 Here • U is a dominant strategy for Player 1, L is a dominant strategy for Player 2, B is a dominant strategy for Player 3, • and therefore (U;L;B) is a dominant strategy equilibrium yielding a payoff of (1,1,2).