Static games of complete information
1 / 34

Static Games of Complete Information - PowerPoint PPT Presentation

  • Uploaded on

Static Games of Complete Information. Games and best responses. Bob is a florist One bunch of flowers sells for $10, but to sell 100 bunches market price should be zero Thus, each extra bunch reduces market price by a dime Cost is $2 a bunch. Games and best responses. $ 10. Price. $ 2.

I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
Download Presentation

PowerPoint Slideshow about ' Static Games of Complete Information' - cricket

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript

Games and best responses
Games and best responses

  • Bob is a florist

  • One bunch of flowers sells for $10, but to sell 100 bunches market price should be zero

  • Thus, each extra bunch reduces market price by a dime

  • Cost is $2 a bunch

Games and best responses1
Games and best responses


$ 10


$ 2




Games and best responses2
Games and best responses

  • What is Bob’s optimal quantity as a monopolist?

  • It turns out to be 40 units

  • Suppose now Ann is contemplating entry

  • How will the total quantity be allocated between the two?

Games and best responses3
Games and best responses

  • It depends on how they expect each other to act

  • Suppose Ann is a sophisticated thinker and responds optimally to Bob

  • Ann’s best response to Bob is (80-QB)/2

  • What should Bob do?

  • Note:

    Bob wants the total quantity to be the midpoint between Ann’s quantity (QA) and the break-even point (80)

Games and best responses4
Games and best responses

  • Demand curve facing Bob is

  • To maximize profits, Bob wants the total quantity to be the midpoint between Ann’s quantity (QA) and the break-even point (80)

$ 10


$ 2







Games and best responses5
Games and best responses

  • Four scenarios for Bob’s reasoning

What if bob s quantity is a given
What if Bob’s quantity is a given?

  • How many bunches should Bob bring, knowing how Ann would react to him?

  • Now, total quantity is (80-QB)/2+QB= (80+QB)/2

  • Thus, mkt price is $10-$0.10{(80+QB)/2}

  • Bob’s optimal quantity is 40; and so Ann’s quantity is 20

  • A significant first-mover advantage!

  • Why does Bob produce monopoly quantity?

Strategic form games
Strategic-Form Games

  • Elements of strategic-form games:

    - finite set of players, i I ={1,2,…,I}

    - pure-strategy space Si for each player i

    - payoff functions ui(s) for each player i and for each strategy profile s=(s1, s2,…, sI)

  • Each player goal is to maximize his own payoff, and NOT to ‘beat’ other players

  • A Zero-Sum game is where =0

  • Most applications in the Social Sciences are non zero-sum games

Mixed strategies

  • A mixed-strategy σiis a probability distribution over pure-strategies

  • σi(si) is probability that σi assigns to si

  • Space of player i’s mixed-strategies is ∑i

  • Space of mixed-strategy profiles is ∑= with elements σ

  • Each player’s randomization is independent of those of other players

  • Player i’s payoff to profile σ is

Computing payoff from a mixed strategy
Computing payoff from a mixed-strategy

  • Player 1 plays along rows; 2 along columns

  • Let σ1={⅓, ⅓, ⅓} andσ2={0, ½, ½ }.

  • What is player 1’s payoff?

Dominated strategies
Dominated Strategies

  • Is there an obvious prediction about how the previous game will be played?

  • Iterated Strict Dominance predicts (U, L)

  • Let “–i” denote all players other than i

  • Then strategies of other players s-i S-i and a strategy profile is (si , s-i )

  • Defn: Pure strategy si is strictly dominated for player i if there exists ∑i such that,

    for all s-i S-i

Some implications of dominance
Some implications of dominance

  • A mixed-strategy that assigns positive probability to a dominated pure strategy, is dominated

  • A mixed-strategy my be dominated even though it assigns positive probability only to undominated pure strategies.

  • Examples: -Prisoner’s Dilemma

    -Second-Price Auction

Nash equilibrium
Nash Equilibrium

  • Defn: A mixed-strategy profile σ* is a Nash Equilibrium if, for all players i,

    for all for all s-i S-i

  • Common examples in economics:

    - Cournot quantity-setting game

    - Bertrand price-setting game


  • Rationality

    • Players aim to maximize their payoffs

    • Players are perfect calculators

  • Common Knowledge

    • Each player knows the rules of the game

    • Each player knows that each player knows the rules

    • Each player knows that each player knows that each player knows the rules

    • Each player knows that each player knows that each player knows that each player knows the rules

    • Each player knows that each player knows that each player knows that each player knows that each player knows the rules

    • Etc. Etc. Etc.

  • Properties and implications
    Properties and implications

    • Property 1: A player must be indifferent between all pure strategies in the support of a mixed-strategy

    • Property 2: One only needs to check for pure-strategy deviations

    • Property 3: If a single strategy survives iterated deletion of strictly dominated strategies it is a Nash Equilibrium

    • Question: Why play mixed strategies when all pure strategies in the support have same payoff?

    Further comments on n e
    Further comments on N.E.

    • In many games pure strategy NE do not exist

      -example is game of “Matching Pennies”

    • Sometimes there are multiple Nash Equilibria

      - “Battle of the Sexes”; “Chicken”

    • Schelling’s theory of “focal points”

    • Pre-play communication

    Baseball anyone
    Baseball anyone?

    • 1986 baseball National League championship series

    • The New York Mets won a crucial game against he Houston Astros

    • Len Dykstra hit Dave Smith’s second pitch for a two-run home run

    • Later the two players talked about this critical play

    Analysis of a home run
    Analysis of a home run

    • Dykstra said, “He threw me a fastball on the first pitch and I fouled it off. I had a gut feeling then that he’d throw me a forkball next, and he did. I got a pitch I saw real well, and I hit it real well”.

    • Smith said, “What it boils down to is that, it was a bad pitch selection…if I had to do it over again, it would be [another] fastball”.

    • Would Dykstra not have been prepared for a fastball?

    • Again, randomization is the only way to go

    But how do you randomize
    But how do you randomize?

    • In a game of tennis, suppose receiver’s forehand is stronger than backhand

    • Consider following probabilities of successfully returning serve

    Server’s Aim

    Receiver’s Move

    Reducing receiver s effectiveness by randomizing
    Reducing receiver’s effectiveness by randomizing

    • Suppose server tosses a coin before each serve

    • Aims to forehand or backhand according to coin turning heads/tails

    • When receiver moves to forehand, his successful return rate is 55%

    • When receiver moves to backhand, his successful return rate is 45%

    • Given server’s randomization, receiver should move to forehand

    • The server has already an improved outcome compared to serving the same way all the time!!

    What is server s best mix
    What is server’s best mix?

    • Consider following graph


    Anticipate Forehand


    Anticipate Backhand


    Percentage successful returns






    Percentage of times server aims serve to forehand

    The mixing probabilities
    The mixing probabilities

    • The 40:60 mixture of forehands to backhands is the equilibrium

    • This mixture is the only one that cannot be exploited by the receiver to his own advantage

    • With this mixture the receiver does equally well with either of his choices

    • Both ensure the receiver a success rate of 48%

    Nash bargaining
    Nash bargaining

    • Two players, N=1, 2, bargain over the partition of a cake

    • The set of Agreements, A, has elements

      A={(a1, a2)єR2: ai≥0 for i=1, 2}

    • The event of Disagreement is D

    • Players have utilities ui, i=1, 2, s.t. ui: A {D}→R

    • Let, S={(u1(a), u2(a)) for a є A}, and d=(u1(D), u2(D))

    • A Bargaining ProblemB is the set of pairs <S, d>, and the Bargaining Solution is a function f :B → R2 that assigns to each <S, d> єB an element of S

    Nash s axioms
    Nash’s axioms

    1. INV (invariance to equiv utility representations):

    If <S/, d/> is obtained from <S, d> by si ,

    then, fi(S/, d/)=

    2. SYM (symmetry)

    If <S, d> is symmetric, then f1(S, d)= f2(S, d)

    3. PAR (Pareto efficiency)

    Suppose <S, d> is a bargaining problem, with si , ti єS , and ti >si , for i=1,2, then f(S, d)≠s

    4. IIA (independence of irrelevant alternatives)

    If <S, d> and <T, d> are bargaining problems with S T and f(T, d) єS, then f(S, d) = f(T, d).

    Nash bargaining solution
    Nash bargaining solution

    Theorem (Nash 1950)

    There is a unique bargaining solution fN:B → R2 satisfying the above axioms given by

    fN(S, d)=

    Application splitting a dollar
    Application: Splitting a dollar

    • Set of agreements is

      A={(a1, a2)єR2: a1+ a2≤1 and ai≥0 for i=1, 2}

    • Players are risk-averse, i.e. uiare concave

    • Disagreement point is d=(u1(0), u2(0))=(0, 0)

    • So <S, d> is a bargaining problem

      Result 1:With equal risk-aversion, players are symmetric & SYM, PAR give (u(1/2), u(1/2))

    Role of risk aversion
    Role of risk-aversion

    • Let player 2 be more risk-averse than player 1

    • Let player 2’s utility be v2 =hu2,where h is concave, and v1 = u1

    • Let <S/, d/> be bargaining problem with utilities vi

    • The optimizing program for <S, d> gives solution zu where zu solves:

    • The optimizing program for <S/, d/> gives solution zv where zv solves:

    Role of risk aversion1
    Role of risk-aversion

    • The first program gives,

    • The second program gives,

    • Result 2:If player 2 becomes more risk-averse, then Player 1’s share of the dollar in the Nash solution increases.

    Existence of ne
    Existence of NE

    Theorem (Nash 1950)

    Every finite strategic-form game has a mixed-strategy equilibrium.

    Sketch of Proof:

    1. Use players’ Reaction Correspondences r(σ)

    2. Realize that a NE is a Fixed Point of r(σ)

    3. Show that conditions for Kakutani’s Fixed Point Theorem are satisfied in this case

    Existence of ne cont
    Existence of NE (cont)

    Theorem (Shizuo Kakutani 1941)

    If :

    • ∑ is compact and convex

    • A correspondence r(σ) :∑→∑ is non-empty and convex

    • r(σ) has a closed graph

      Then r(σ) has a fixed point, that is, there exists σ*

      such that, r(σ*)= σ*

    Nash equilibrium is a fixed point
    Nash equilibrium is a fixed point

    • For any strategy profile σ, player i’s reaction correspondence ri maps σ to the mixed strategy that maximizes his payoff, given his opponents play σ-i . Thus, ri (σ)= ri (σi ,σ-i ) = ui(σ/i , σ-i )

    • Each player i=1,2,..n, has a reaction function

    • Let us form the n-tuple, r(σ)=(r1 (σ), r2 (σ),…, rn(σ)), where r(σ)є∑

    • Suppose there exists σ* є∑such that,for all players, σ*i =ri(σ*)= ri(σ*i , σ*-i), then σ* is a Nash equilibrium

    • But σ*i =ri(σ*) for all i,means that σ*=r(σ*), i.e. σ* is a fixed point of the reaction correspondence r(.)

    Applying kakutani
    Applying Kakutani

    • ∑ is compact and convex:

      1. Mixed strategies convexify the strategy space, and so each ∑i is a convex space of dimension (#Si-1)

      2. Theorem (Heine-Borel):

      Closed & bounded subsets in Rn are compact

    • r(σ) is non-empty:

      1. Theorem (Weierstrass):

      A continuous real-valued function defined on a compact space achieves its maximum values

      2. Player’s i’sutility functions are continuous in σi

    Applying kakutani1
    Applying Kakutani

    • r(σ) is convex:

      Suppose σ/,σ//є r(σ) . uiis linear, therefore for λє(0, 1)

      ui(λσi/+ (1- λ)σi//, σ-i)= λ ui(σi/, σ-i) + (1- λ)ui(σi//, σ-i). Thus, if σi/,σi//are best responsestoσ-i, then so is their weighted average. Thus, λσi/+ (1- λ)σi// є r(σ)

    • r(σ) has a closed graph:

      If with , then