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Learning Dynamics for Mechanism Design Paul J. Healy California Institute of Technology An Experimental Comparison of Public Goods Mechanisms.

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Presentation on theme: "Learning Dynamics for Mechanism Design Paul J. Healy California Institute of Technology An Experimental Comparison of Public Goods Mechanisms."— Presentation transcript:

1 Learning Dynamics for Mechanism Design Paul J. Healy California Institute of Technology An Experimental Comparison of Public Goods Mechanisms

2 Overview Institution (mechanism) design –Public goods Experiments –Equilibrium, rationality, convergence (How) Can experiments improve institution/mechanism design?

3 Plan of the Talk Introduction The framework –Mechanism design, existing experiments New experiments –Design, data, analysis A (better) model of behavior in mechanisms Comparing the model to the data

4 A Simple Example Environment –Condo owners –Preferences –Income, existing park Outcomes –Gardening budget / Quality of the park Mechanism –Proposals, votes, majority rule Repeated Game, Incomplete Info

5 Mechanism Design Implementation: g   (e)  F( e )

6 The Role of Experiments Field: e unknown => F ( e ) unknown Experiment: everything fixed/induced except 

7 The Public Goods Environment n agents 1 private good x, 1 public good y Endowed with private good only  i  Preferences: u i (x i,y)=v i (y)+x i Linear technology (  ) Mechanisms:

8 Five Mechanisms “Efficient” => g   (e)  PO(e) Inefficient Mechanisms Voluntary Contribution Mech. (VCM) Proportional Tax Mech. (Outcome-) Efficient Mechanisms –Dominant Strategy Equilibrium Vickrey, Clarke, Groves (VCG) (1961, 71, 73) –Nash Equilibrium Groves-Ledyard (1977) Walker (1981)

9 The Experimental Environment n = 5 Four sessions of each mech. 50 periods (repetitions) Quadratic, quasilinear utility Preferences are private info Payoff ≈ $25 for 1.5 hours Computerized, anonymous Caltech undergrads Inexperienced subjects History window “What-If Scenario Analyzer”

10 What-If Scenario Analyzer An interactive payoff table Subjects understand how strategies → outcomes Used extensively by all subjects

11 Environment Parameters Loosely based on Chen & Plott ’96  = 100 Pareto optimum: y o =(  b i -  )/(  2a i )= aiai bibi  i Player Player Player Player Player

12 Voluntary Contribution Mechanism Previous experiments: –All players have dominant strategy: m * = 0 –Contributions decline in time Current experiment: –Players 1, 3, 4, 5 have dom. strat.: m * = 0 –Player 2’s best response: m 2 * = 1 -  i  2 m i –Nash equilibrium: (0,1,0,0,0) M i = [0,6] y(m) =  i m i t i (m)=  m i

13 VCM Results Player 2 Nash Equilibrium: (0,1,0,0,0) Dominant Strategies

14 Proportional Tax Mechanism No previous experiments (?) Foundation of many efficient mechanisms Current experiment: –No dominant strategies –Best response: m i * = y i *   k  i m k –(y 1 *,…,y 5 * ) = (7, 6, 5, 4, 3) –Nash equilibrium: (6,0,0,0,0) M i = [0,6] y(m) =  i m i t i (m)=(  /n)y(m)

15 Prop. Tax Results Player 2 Player 1

16 Groves-Ledyard Mechanism Theory: –Pareto optimal equilibrium, not Lindahl –Supermodular if  /n > 2a i for every i Previous experiments: –Chen & Plott ’96 – higher  => converges better Current experiment: –  =100 => Supermodular –Nash equilibrium: (1.00, 1.15, 0.97, 0.86, 0.82)

17 Groves-Ledyard Results

18 Walker’s Mechanism Theory: –Implements Lindahl Allocations –Individually rational (nice!) Previous experiments: –Chen & Tang ’98 – unstable Current experiment: –Nash equilibrium: (12.28, -1.44, -6.78, -2.2, 2.94)

19 Walker Mechanism Results NE: (12.28, -1.44, -6.78, -2.2, 2.94)

20 VCG Mechanism: Theory Truth-telling is a dominant strategy Pareto optimal public good level Not budget balanced Not always individually rational

21 VCG Mechanism: Best Responses Truth-telling ( ) is a weak dominant strategy There is always a continuum of best responses:

22 VCG Mechanism: Previous Experiments Attiyeh, Franciosi & Isaac ’00 –Binary public good: weak dominant strategy –Value revelation around 15%, no convergence Cason, Saijo, Sjostrom & Yamato ’03 –Binary public good: 50% revelation Many play non-dominant Nash equilibria –Continuous public good with single-peaked preferences: 81% revelation Subjects play the unique equilibrium

23 VCG Experiment Results Demand revelation: 50 – 60% –NEVER observe the dominant strategy equilibrium 10/20 subjects fully reveal in 9/10 final periods –“Fully reveal” = both parameters 6/20 subjects fully reveal < 10% of time Outcomes very close to Pareto optimal –Announcements may be near non-revealing best responses

24 Summary of Experimental Results VCM: convergence to dominant strategies Prop Tax: non-equil., but near best response Groves-Ledyard: convergence to stable equil. Walker: no convergence to unstable equilibrium VCG: low revelation, but high efficiency Goal: A simple model of behavior to explain/predict which mechanisms converge to equilibrium Observation: Results are qualitatively similar to best response predictions

25 A Class of Best Response Models A general best response framework: –Predictions map histories into strategies –Agents best respond to their predictions A k-period best response model: –Pure strategies only –Convex strategy space –Rational behavior, inconsistent predictions

26 Testable Predictions of the k-Period Model 1.No strictly dominated strategies after period k 2.Same strategy k+1 times => Nash equilibrium 3.U.H.C. + Convergence to m * => m * is a N.E Asymptotically stable points are N.E. 4.Not always stable 4.1. Global stability in supermodular games 4.2. Global stability in games with dominant diagonal Note: Stability properties are not monotonic in k

27 Choosing the best k Which k minimizes  t |m t obs  m t pred | ? k=5 is the best fit

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31 5-Period Best Response vs. Equilibrium: Walker

32 5-Period Best Response vs. Equilibrium: Groves-Ledyard

33 5-Period Best Response vs. Equilibrium: VCM

34 5-Period Best Response vs. Equilibrium: PropTax

35 Statistical Tests: 5-B.R. vs. Equilibrium Null Hypothesis: Non-stationarity => period-by-period tests Non-normality of errors => non-parametric tests –Permutation test with 2,000 sample permutations Problem: If then the test has little power Solution: –Estimate test power as a function of –Perform the test on the data only where power is sufficiently large.

36 Simulated Test Power Frequency of Rejecting H 0 (Power)  1  2  Prob. H 0 False Given Reject H 0

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41 5-period B.R. vs. Nash Equilibrium Voluntary Contribution (strict dom. strats): Groves-Ledyard (stable Nash equil): Walker (unstable Nash equil): 73/81 tests reject H 0 –No apparent pattern of results across time Proportional Tax: 16/19 tests reject H 0 5-period model beats any static prediction

42 Best Response in the VCG Mechanism Convert data to polar coordinates:

43 Best Response in the cVCG Mechanism Origin = Truth-telling dominant strategy 0-degree Line = Best response to 5-period average

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45 The Testable Predictions 1.Weakly dominated ε-Nash equilibria are observed (67%) –The dominant strategy equilibrium is not (0%) –Convergence to strict dominant strategies 2,3. 6 repetitions of a strategy implies ε-equilibrium (75%) 4.Convergence with supermodularity & dom. diagonal (G-L)

46 Conclusions Experiments reveal the importance of dynamics & stability Dynamic models outperform static models New directions for theoretical work Applications for “real world” implementation Open questions: –Stable mechanisms implementing Lindahl * –Efficiency/equilibrium tension in VCG –Effect of the “What-If Scenario Analyzer” –Better learning models

47 An Almost-Trivial Game Cycling (including equilibrium!) for k=3 Global convergence for k=1,2,4,5,…

48 Efficiency Confidence Intervals - All 50 Periods Mechanism Efficiency Walker VC PT GL VCG No Pub Good Efficiency

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51 Voluntary Contribution Mechanism Results


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