1 teck h. ho spring 2006 a cognitive hierarchy (ch) model of games teck h. ho haas school of...
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Spring 2006 1Teck H. Ho
A Cognitive Hierarchy (A Cognitive Hierarchy (CCH) H) Model of GamesModel of Games
Teck H. Ho
Haas School of Business
University of California, Berkeley
Joint work with Colin Camerer, Caltech
Juin-Kuan Chong, NUS
Spring 2006 2Teck H. Ho
MotivationMotivationNash equilibrium and its refinements: Dominant
theories in economics and marketing for predicting behaviors in competitive situations.
Subjects do not play Nash in many one-shot games.
Behaviors do not converge to Nash with repeated interactions in some games.
Multiplicity problem (e.g., coordination games).
Modeling heterogeneity really matters in games.
Spring 2006 3Teck H. Ho
Main GoalsMain Goals
Provide a behavioral theory to explain and predict behaviors in any one-shot game.Normal-form games (e.g., zero-sum game, p-
beauty contest)Extensive-form games (e.g., centipede)
Provide an empirical alternative to Nash equilibrium (Camerer, Ho, and Chong, QJE, 2004) and backward induction principle (Ho, Camerer, and Chong, 2005)
Spring 2006 4Teck H. Ho
Modeling PrinciplesModeling Principles
Principle Nash CH
Strategic Thinking
Best Response
Mutual Consistency
Spring 2006 5Teck H. Ho
Modeling PhilosophyModeling Philosophy
Simple (Economics)General (Economics)Precise (Economics)Empirically disciplined (Psychology)
“the empirical background of economic science is definitely inadequate...it would have been absurd in physics to expect Kepler and Newton without Tycho Brahe” (von Neumann & Morgenstern ‘44)
“Without having a broad set of facts on which to theorize, there is a certain danger of spending too much time on models that are mathematically elegant, yet have little connection to actual behavior. At present our empirical knowledge is inadequate...” (Eric Van Damme ‘95)
Spring 2006 6Teck H. Ho
Example 1: “zero-sum game”Example 1: “zero-sum game”
COLUMNL C R
T 0,0 10,-10 -5,5
ROW M -15,15 15,-15 25,-25
B 5,-5 -10,10 0,0
Messick(1965), Behavioral Science
Spring 2006 7Teck H. Ho
Nash Prediction: Nash Prediction: “zero-sum game”“zero-sum game”
Nash COLUMN Equilibrium
L C RT 0,0 10,-10 -5,5 0.40
ROW M -15,15 15,-15 25,-25 0.11
B 5,-5 -10,10 0,0 0.49Nash
Equilibrium 0.56 0.20 0.24
Spring 2006 8Teck H. Ho
CH Prediction: CH Prediction: “zero-sum game”“zero-sum game”
Nash CH ModelCOLUMN Equilibrium ( = 1.55)
L C RT 0,0 10,-10 -5,5 0.40 0.07
ROW M -15,15 15,-15 25,-25 0.11 0.40
B 5,-5 -10,10 0,0 0.49 0.53Nash
Equilibrium 0.56 0.20 0.24CH Model( = 1.55) 0.86 0.07 0.07
Spring 2006 9Teck H. Ho
Empirical Frequency: Empirical Frequency: “zero-sum game”“zero-sum game”
http://groups.haas.berkeley.edu/simulations/CH/
Nash CH Model EmpiricalCOLUMN Equilibrium ( = 1.55) Frequency
L C RT 0,0 10,-10 -5,5 0.40 0.07 0.13
ROW M -15,15 15,-15 25,-25 0.11 0.40 0.33
B 5,-5 -10,10 0,0 0.49 0.53 0.54Nash
Equilibrium 0.56 0.20 0.24CH Model( = 1.55) 0.86 0.07 0.07Empirical
Frequency 0.88 0.08 0.04
Spring 2006 10Teck H. Ho
The Cognitive Hierarchy (CH) The Cognitive Hierarchy (CH) ModelModelPeople are different and have different decision rules.
Modeling heterogeneity (i.e., distribution of types of players). Types of players are denoted by levels 0, 1, 2, 3,…,
Modeling decision rule of each type.
Spring 2006 11Teck H. Ho
Modeling Decision RuleModeling Decision RuleFrequency of k-step is f(k)
Step 0 choose randomly
k-step thinkers know proportions f(0),...f(k-1)
Form beliefs and best-respond based on beliefs
Iterative and no need to solve a fixed point
gk (h) f (h)
f (h ' )h ' 1
K 1
Spring 2006 12Teck H. Ho
COLUMNL C R
T 0,0 10,-10 -5,5
ROW M -15,15 15,-15 25,-25
B 5,-5 -10,10 0,0
K's K+1's ROW COLLevel (K) Proportion Belief T M B L C R
0 0.212 1.00 0.33 0.33 0.33 0.33 0.33 0.33Aggregate 1.00 0.33 0.33 0.33 0.33 0.33 0.33
0 0.212 0.39 0.33 0.33 0.33 0.33 0.33 0.331 0.329 0.61 0 1 0 1 0 0
Aggregate 1.00 0.13 0.74 0.13 0.74 0.13 0.130 0.212 0.27 0.33 0.33 0.33 0.33 0.33 0.331 0.329 0.41 0 1 0 1 0 02 0.255 0.32 0 0 1 1 0 0
Aggregate 1.00 0.09 0.50 0.41 0.82 0.09 0.09
K Proportion, f(k)0 0.2121 0.3292 0.2553 0.132
>3 0.072
Spring 2006 13Teck H. Ho
Theoretical ImplicationsTheoretical Implications
Exhibits “increasingly rational expectations”
Normalized gK(h) approximates f(h) more closely as k ∞∞ (i.e., highest level types are
“sophisticated” (or "worldly") and earn the most.
Highest level type actions converge as k ∞∞
marginal benefit of thinking harder 00
Spring 2006 14Teck H. Ho
Modeling Heterogeneity, Modeling Heterogeneity, f(k)f(k)
A1:
sharp drop-off due to increasing difficulty in simulating others’ behaviors
A2: f(0) + f(1) = 2f(2)
kkf
kf
)1(
)(
Spring 2006 15Teck H. Ho
ImplicationsImplications
!)(
kekf
k A1 Poisson distribution with mean and variance =
A1,A2 Poisson, golden ratio Φ)
Spring 2006 16Teck H. Ho
Poisson DistributionPoisson Distribution f(k) with mean step of thinking :
!)(
kekf
k
Poisson distributions for various
00.05
0.10.15
0.20.25
0.30.35
0.4
0 1 2 3 4 5 6
number of steps
fre
qu
en
cy
Spring 2006 17Teck H. Ho
Theoretical Properties of CH Theoretical Properties of CH ModelModelAdvantages over Nash equilibrium
Can “solve” multiplicity problem (picks one statistical distribution)
Sensible interpretation of mixed strategies (de facto purification)
Theory: τ∞ converges to Nash equilibrium in (weakly)
dominance solvable games
Spring 2006 18Teck H. Ho
Estimates of Mean Thinking Estimates of Mean Thinking Step Step
Spring 2006 19Teck H. Ho
Figure 3b: Predicted Frequencies of Nash Equilibrium for Entry and Mixed Games
y = 0.707x + 0.1011
R2 = 0.4873
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Empirical Frequency
Pre
dic
ted
Fre
qu
en
cy
Nash: Theory vs. Data
Spring 2006 20Teck H. Ho
Figure 2b: Predicted Frequencies of Cognitive Hierarchy Models for Entry and Mixed Games (common )
y = 0.8785x + 0.0419
R2 = 0.8027
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Empirical Frequency
Pre
dic
ted
Fre
qu
ency
CH Model: Theory vs. Data
Spring 2006 21Teck H. Ho
Economic ValueEconomic Value
Evaluate models based on their value-added rather than statistical fit (Camerer and Ho, 2000)
Treat models like consultants
If players were to hire Mr. Nash and Ms. CH as consultants and listen to their advice (i.e., use the model to forecast what others will do and best-respond), would they have made a higher payoff?
Spring 2006 22Teck H. Ho
Nash versus CH Model: Economic Value
Spring 2006 23Teck H. Ho
Application: Strategic IQhttp://128.32.67.154/siq
A battery of 30 "well-known" games
Measure CH player and a subject's strategic IQ by how much money they make (matched against a defined pool of subjects)
Factor analysis + fMRI to figure out whether certain brain region accounts for superior performance in "similar" games
Specialized subject poolsSoldiers
Writers
Chess players
Patients with brain damages
Spring 2006 24Teck H. Ho
Example 2Example 2: P: P-Beauty Contest-Beauty Contest n players
Every player simultaneously chooses a number from 0
to 100
Compute the group average
Define Target Number to be 0.7 times the group
average
The winner is the player whose number is the closet to
the Target Number
The prize to the winner is US$20Ho, Camerer, and Weigelt (AER, 1998)
Spring 2006 25Teck H. Ho
A Sample of CEOsA Sample of CEOs
David Baltimore President California Institute of Technology
Donald L. Bren
Chairman of the BoardThe Irvine Company
• Eli BroadChairmanSunAmerica Inc.
• Lounette M. Dyer Chairman Silk Route Technology
• David D. Ho Director The Aaron Diamond AIDS Research Center
• Gordon E. Moore Chairman Emeritus Intel Corporation
• Stephen A. Ross Co-Chairman, Roll and Ross Asset Mgt Corp
• Sally K. Ride President Imaginary Lines, Inc., and Hibben Professor of Physics
Spring 2006 26Teck H. Ho
Results in various p-BC gamesResults in various p-BC games
Subject Pool Group Size Sample Size Mean Error (Nash) Error (CH)
CEOs 20 20 37.9 -37.9 -0.1 1.0
80 year olds 33 33 37.0 -37.0 -0.1 1.1
Economics PhDs 16 16 27.4 -27.4 0.0 2.3
Portfolio Managers 26 26 24.3 -24.3 0.1 2.8
Game Theorists 27-54 136 19.1 -19.1 0.0 3.7
Spring 2006 27Teck H. Ho
Example 3: Centipede GameExample 3: Centipede Game
1 2 2 21 1
0.400.10
0.200.80
1.600.40
0.803.20
6.401.60
3.2012.80
25.606.40
Figure 1: Six-move Centipede Game
Spring 2006 28Teck H. Ho
CH vs. Backward Induction CH vs. Backward Induction Principle (BIP)Principle (BIP)
Can CH be extended (i.e., extensive CH) to provide an empirical alternative to BIP in predicting behavior in the Centipede?
Is there a difference between steps of thinking and look-ahead (planning)?
Spring 2006 29Teck H. Ho
The three underlying premises The three underlying premises
Rationality: Given a choice between two alternatives, a player chooses the most preferred.
Truncation consistency: Replacing a subgame with its equilibrium payoffs does not affect play elsewhere in the game.
Subgame consistency: Play in a subgame is independent of the subgame’s position in a larger game.
Binmore, McCarthy, Ponti, and Samuelson (JET, 2002) show violations of both truncation and subgame consistencies.
Spring 2006 30Teck H. Ho
Truncation ConsistencyTruncation Consistency
VS.
1 2 2 21 1
0.400.10
0.200.80
1.600.40
0.803.20
6.401.60
3.2012.80
25.606.40
Figure 1: Six-move Centipede game
1 2 21
0.400.10
0.200.80
1.600.40
0.803.20
6.401.60
Figure 2: Four-move Centipede game (Low-Stake)
Spring 2006 31Teck H. Ho
The three underlying premisesThe three underlying premises
Rationality: Given a choice between two alternatives, a player chooses the most preferred.
Truncation consistency: Replacing a subgame with its equilibrium payoffs does not affect play elsewhere in the game.
Subgame consistency: Play in a subgame is independent of the subgame’s position in a larger game.
Binmore, McCarthy, Ponti, and Samuelson (JET, 2002) show violations of both truncation and subgame consistencies.
Spring 2006 32Teck H. Ho
Subgame ConsistencySubgame Consistency
1 2 2 21 1
0.400.10
0.200.80
1.600.40
0.803.20
6.401.60
3.2012.80
25.606.40
VS.
2 21 1
1.600.40
0.803.20
6.401.60
3.2012.80
25.606.40
Figure 1: Six-move Centipede game
Figure 3: Four-move Centipede game (High-Stake (x4))
Spring 2006 33Teck H. Ho
Data: Truncation & Subgame Data: Truncation & Subgame ConsistenciesConsistencies
Data p1 p2 p3 p4 p5 p6
6-move 0.01 0.06 0.21 0.53 0.73 0.85
4-move(Low Stake) 0.07 0.38 0.65 0.75
4-move(High Stake) 0.15 0.44 0.67 0.69
*Data from McKelvey and Palfrey (1992)
Spring 2006 34Teck H. Ho
Limited thinking and PlanningLimited thinking and Planning
Steps of Reasoning(People)
Ste
ps
of P
lann
ing
(Tim
e)
0 1 2 3 4 5
1
2
3
4
5
Bivariate Poisson
Spring 2006 35Teck H. Ho
Estimation Results (out-of-Estimation Results (out-of-sample)sample)
Thinking steps and steps of planning are perfectly correlated
6 stages All sessionsLow-Stake High-Stake
Sample Size 281 100 281 662
CalibrationSample Size 197 70 197 464
Agent Quantal Response Eqlbm (AQRE) -287.0 -106.8 -409.8 -848.2
Extensive Cognitive Hierarchy (xCH) -276.1 -105.9 -341.2 -753.0xCH (Step of Thinking = Steps of Planning) -276.1 -105.9 -341.2 -753.0
4 stages
Spring 2006 36Teck H. Ho
Data and xCH Prediction: Data and xCH Prediction: Truncation & Subgame ConsistenciesTruncation & Subgame Consistencies
xCH Prediction
6-move 0.06 0.16 0.15 0.48 0.90 0.99
4-move(Low-Stake) 0.15 0.31 0.76 0.97
4-move(High-Stake) 0.21 0.34 0.71 0.95
Data p1 p2 p3 p4 p5 p6
6-move 0.01 0.06 0.21 0.53 0.73 0.85
4-move(Low Stake) 0.07 0.38 0.65 0.75
4-move(High Stake) 0.15 0.44 0.67 0.69
Spring 2006 37Teck H. Ho
SummarySummaryCH Model:
Discrete thinking stepsFrequency Poisson distributed
One-shot gamesFits better than Nash and adds more economic valueSensible interpretation of mixed strategies Can “solve” multiplicity problem
xCH:Provides an empirical alternative to BIPLimited steps of thinking and planning are perfectly
correlated
Spring 2006 38Teck H. Ho
Spring 2006 39Teck H. Ho
Bounded Rationality in Markets: Revised Utility Function
Behavioral Regularities Standard Assumption New Model Specification Marketing ApplicationReference Example Example
1. Revised Utility Function
- Reference point and - Expected Utility Theory - Prospect Theory - Two-part tariff - double loss aversion Kahneman and Tversky (1979) marginalization problem
- Fairness - Self-interested - Inequality aversion - Price discrimination Fehr and Schmidt (1999)
- Impatience - Exponential discounting - Hyperbolic Discounting - Price promotion and Ainslie (1975) packaging size design
Spring 2006 40Teck H. Ho
Bounded Rationality in Markets: Alternative Solution Concepts
Behavioral Regularities Standard Assumption New Model Specification Marketing ApplicationExample Example
2. Bounded Computation Ability
- Nosiy Best Response - Best Response - Quantal Best Response - NEIO McKelvey and Palfrey (1995)
- Limited Thinking Steps - Rational expectation - Cognitive hierarchy - Market entry competition Camerer, Ho, Chong (2004)
- Myopic and learn - Instant equilibration - Experience weighted attraction - Lowest price guarantee Camerer and Ho (1999) competition
Spring 2006 41Teck H. Ho
Neural Foundations of Game TheoryNeural foundation of game theory
Spring 2006 42Teck H. Ho
Strategic IQ: A New Taxonomy of Games
Spring 2006 43Teck H. Ho
Spring 2006 44Teck H. Ho
First-Shot GamesFirst-Shot Games
The FCC license auctions, elections, military campaigns, legal disputes
Many marketing/IO models
Simple gane experiments in economics and marketing
Spring 2006 45Teck H. Ho
Nash versus CH Model: LL and MSD (in-sample)
Spring 2006 46Teck H. Ho
Economic Value:Economic Value:Definition and MotivationDefinition and Motivation
“A normative model must produce strategies that are at least as good as what people can do without them.” (Schelling, 1960)
A measure of degree of disequilibrium, in dollars.
If players are in equilibrium, then an equilibrium theory will advise them to make the same choices they would make anyway, and hence will have zero economic value
If players are not in equilibrium, then players are mis-forecasting what others will do. A theory with more accurate beliefs will have positive economic value (and an equilibrium theory can have negative economic value if it misleads players)
Spring 2006 47Teck H. Ho
Alternative SpecificationsAlternative Specifications
Overconfidence:
k-steps think others are all one step lower (k-1) (Stahl, GEB, 1995; Nagel, AER, 1995; Ho, Camerer and Weigelt, AER, 1998)
“Increasingly irrational expectations” as K ∞
Has some odd properties (e.g., cycles in entry games)
Self-conscious:
k-steps think there are other k-step thinkers
Similar to Quantal Response Equilibrium/Nash
Fits worse
Spring 2006 48Teck H. Ho
Implied Take ProbabilityImplied Take ProbabilityImplied take probability at each stage, pj
Truncation consistency: For a given j, pj is identical in both 4-move (low-stake) and 6-move games.
Subgame consistency: For a given j, pn-j (n=4 or 6)
is identical in both 4-move (high-stake) and 6-move games.
Spring 2006 49Teck H. Ho
KK-Step Look-ahead (Planning)-Step Look-ahead (Planning)
1 2 2 21 1
0.400.10
0.200.80
1.600.40
0.803.20
6.401.60
3.2012.80
25.606.40
1 2
0.400.10
0.200.80
V1
V2
Example: 1-step look-ahead