Game Theory Basics II: Extensive Form Games1
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Lecture 4 Rationalizability & Nash Equilibrium Road
Lecture 4 Rationalizability & Nash Equilibrium 14.12 Game Theory Muhamet Yildiz Road Map 1. Strategies – completed 2. Quiz 3. Dominance 4. Dominant-strategy equilibrium 5. Rationalizability 6. Nash Equilibrium 1 Strategy A strategy of a player is a complete contingent-plan, determining which action he will take at each information set he is to move (including the information sets that will not be reached according to this strategy). Matching pennies with perfect information 2’s Strategies: HH = Head if 1 plays Head, 1 Head if 1 plays Tail; HT = Head if 1 plays Head, Head Tail Tail if 1 plays Tail; 2 TH = Tail if 1 plays Head, 2 Head if 1 plays Tail; head tail head tail TT = Tail if 1 plays Head, Tail if 1 plays Tail. (-1,1) (1,-1) (1,-1) (-1,1) 2 Matching pennies with perfect information 2 1 HH HT TH TT Head Tail Matching pennies with Imperfect information 1 2 1 Head Tail Head Tail 2 Head (-1,1) (1,-1) head tail head tail Tail (1,-1) (-1,1) (-1,1) (1,-1) (1,-1) (-1,1) 3 A game with nature Left (5, 0) 1 Head 1/2 Right (2, 2) Nature (3, 3) 1/2 Left Tail 2 Right (0, -5) Mixed Strategy Definition: A mixed strategy of a player is a probability distribution over the set of his strategies. Pure strategies: Si = {si1,si2,…,sik} σ → A mixed strategy: i: S [0,1] s.t. σ σ σ i(si1) + i(si2) + … + i(sik) = 1. If the other players play s-i =(s1,…, si-1,si+1,…,sn), then σ the expected utility of playing i is σ σ σ i(si1)ui(si1,s-i) + i(si2)ui(si2,s-i) + … + i(sik)ui(sik,s-i). -
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 .................................. -
SEQUENTIAL GAMES with PERFECT INFORMATION Example
SEQUENTIAL GAMES WITH PERFECT INFORMATION Example 4.9 (page 105) Consider the sequential game given in Figure 4.9. We want to apply backward induction to the tree. 0 Vertex B is owned by player two, P2. The payoffs for P2 are 1 and 3, with 3 > 1, so the player picks R . Thus, the payoffs at B become (0, 3). 00 Next, vertex C is also owned by P2 with payoffs 1 and 0. Since 1 > 0, P2 picks L , and the payoffs are (4, 1). Player one, P1, owns A; the choice of L gives a payoff of 0 and R gives a payoff of 4; 4 > 0, so P1 chooses R. The final payoffs are (4, 1). 0 00 We claim that this strategy profile, { R } for P1 and { R ,L } is a Nash equilibrium. Notice that the 0 00 strategy profile gives a choice at each vertex. For the strategy { R ,L } fixed for P2, P1 has a maximal payoff by choosing { R }, ( 0 00 0 00 π1(R, { R ,L }) = 4 π1(R, { R ,L }) = 4 ≥ 0 00 π1(L, { R ,L }) = 0. 0 00 In the same way, for the strategy { R } fixed for P1, P2 has a maximal payoff by choosing { R ,L }, ( 00 0 00 π2(R, {∗,L }) = 1 π2(R, { R ,L }) = 1 ≥ 00 π2(R, {∗,R }) = 0, where ∗ means choose either L0 or R0. Since no change of choice by a player can increase that players own payoff, the strategy profile is called a Nash equilibrium. Notice that the above strategy profile is also a Nash equilibrium on each branch of the game tree, mainly starting at either B or starting at C. -
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). -
Economics 201B Economic Theory (Spring 2021) Strategic Games
Economics 201B Economic Theory (Spring 2021) Strategic Games Topics: terminology and notations (OR 1.7), games and solutions (OR 1.1-1.3), rationality and bounded rationality (OR 1.4-1.6), formalities (OR 2.1), best-response (OR 2.2), Nash equilibrium (OR 2.2), 2 2 examples × (OR 2.3), existence of Nash equilibrium (OR 2.4), mixed strategy Nash equilibrium (OR 3.1, 3.2), strictly competitive games (OR 2.5), evolution- ary stability (OR 3.4), rationalizability (OR 4.1), dominance (OR 4.2, 4.3), trembling hand perfection (OR 12.5). Terminology and notations (OR 1.7) Sets For R, ∈ ≥ ⇐⇒ ≥ for all . and ⇐⇒ ≥ for all and some . ⇐⇒ for all . Preferences is a binary relation on some set of alternatives R. % ⊆ From % we derive two other relations on : — strict performance relation and not  ⇐⇒ % % — indifference relation and ∼ ⇐⇒ % % Utility representation % is said to be — complete if , or . ∀ ∈ % % — transitive if , and then . ∀ ∈ % % % % can be presented by a utility function only if it is complete and transitive (rational). A function : R is a utility function representing if → % ∀ ∈ () () % ⇐⇒ ≥ % is said to be — continuous (preferences cannot jump...) if for any sequence of pairs () with ,and and , . { }∞=1 % → → % — (strictly) quasi-concave if for any the upper counter set ∈ { ∈ : is (strictly) convex. % } These guarantee the existence of continuous well-behaved utility function representation. Profiles Let be a the set of players. — () or simply () is a profile - a collection of values of some variable,∈ one for each player. — () or simply is the list of elements of the profile = ∈ { } − () for all players except . ∈ — ( ) is a list and an element ,whichistheprofile () . -
(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. -
Pure and Bayes-Nash Price of Anarchy for Generalized Second Price Auction
Pure and Bayes-Nash Price of Anarchy for Generalized Second Price Auction Renato Paes Leme Eva´ Tardos Department of Computer Science Department of Computer Science Cornell University, Ithaca, NY Cornell University, Ithaca, NY [email protected] [email protected] Abstract—The Generalized Second Price Auction has for advertisements and slots higher on the page are been the main mechanism used by search companies more valuable (clicked on by more users). The bids to auction positions for advertisements on search pages. are used to determine both the assignment of bidders In this paper we study the social welfare of the Nash equilibria of this game in various models. In the full to slots, and the fees charged. In the simplest model, information setting, socially optimal Nash equilibria are the bidders are assigned to slots in order of bids, and known to exist (i.e., the Price of Stability is 1). This paper the fee for each click is the bid occupying the next is the first to prove bounds on the price of anarchy, and slot. This auction is called the Generalized Second Price to give any bounds in the Bayesian setting. Auction (GSP). More generally, positions and payments Our main result is to show that the price of anarchy is small assuming that all bidders play un-dominated in the Generalized Second Price Auction depend also on strategies. In the full information setting we prove a bound the click-through rates associated with the bidders, the of 1.618 for the price of anarchy for pure Nash equilibria, probability that the advertisement will get clicked on by and a bound of 4 for mixed Nash equilibria. -
Extensive Form Games with Perfect Information
Notes Strategy and Politics: Extensive Form Games with Perfect Information Matt Golder Pennsylvania State University Sequential Decision-Making Notes The model of a strategic (normal form) game suppresses the sequential structure of decision-making. When applying the model to situations in which players move sequentially, we assume that each player chooses her plan of action once and for all. She is committed to this plan, which she cannot modify as events unfold. The model of an extensive form game, by contrast, describes the sequential structure of decision-making explicitly, allowing us to study situations in which each player is free to change her mind as events unfold. Perfect Information Notes Perfect information describes a situation in which players are always fully informed about all of the previous actions taken by all players. This assumption is used in all of the following lecture notes that use \perfect information" in the title. Later we will also study more general cases where players may only be imperfectly informed about previous actions when choosing an action. Extensive Form Games Notes To describe an extensive form game with perfect information we need to specify the set of players and their preferences, just as for a strategic game. In addition, we also need to specify the order of the players' moves and the actions each player may take at each point (or decision node). We do so by specifying the set of all sequences of actions that can possibly occur, together with the player who moves at each point in each sequence. We refer to each possible sequence of actions (a1; a2; : : : ak) as a terminal history and to the function that denotes the player who moves at each point in each terminal history as the player function. -
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. -
Games with Hidden Information
Games with Hidden Information R&N Chapter 6 R&N Section 17.6 • Assumptions so far: – Two-player game : Player A and B. – Perfect information : Both players see all the states and decisions. Each decision is made sequentially . – Zero-sum : Player’s A gain is exactly equal to player B’s loss. • We are going to eliminate these constraints. We will eliminate first the assumption of “perfect information” leading to far more realistic models. – Some more game-theoretic definitions Matrix games – Minimax results for perfect information games – Minimax results for hidden information games 1 Player A 1 L R Player B 2 3 L R L R Player A 4 +2 +5 +2 L Extensive form of game: Represent the -1 +4 game by a tree A pure strategy for a player 1 defines the move that the L R player would make for every possible state that the player 2 3 would see. L R L R 4 +2 +5 +2 L -1 +4 2 Pure strategies for A: 1 Strategy I: (1 L,4 L) Strategy II: (1 L,4 R) L R Strategy III: (1 R,4 L) Strategy IV: (1 R,4 R) 2 3 Pure strategies for B: L R L R Strategy I: (2 L,3 L) Strategy II: (2 L,3 R) 4 +2 +5 +2 Strategy III: (2 R,3 L) L R Strategy IV: (2 R,3 R) -1 +4 In general: If N states and B moves, how many pure strategies exist? Matrix form of games Pure strategies for A: Pure strategies for B: Strategy I: (1 L,4 L) Strategy I: (2 L,3 L) Strategy II: (1 L,4 R) Strategy II: (2 L,3 R) Strategy III: (1 R,4 L) Strategy III: (2 R,3 L) 1 Strategy IV: (1 R,4 R) Strategy IV: (2 R,3 R) R L I II III IV 2 3 L R I -1 -1 +2 +2 L R 4 II +4 +4 +2 +2 +2 +5 +1 L R III +5 +1 +5 +1 IV +5 +1 +5 +1 -1 +4 3 Pure strategies for Player B Player A’s payoff I II III IV if game is played I -1 -1 +2 +2 with strategy I by II +4 +4 +2 +2 Player A and strategy III by III +5 +1 +5 +1 Player B for Player A for Pure strategies IV +5 +1 +5 +1 • Matrix normal form of games: The table contains the payoffs for all the possible combinations of pure strategies for Player A and Player B • The table characterizes the game completely, there is no need for any additional information about rules, etc.