Implementation in Bayes-Nash equilibrium

Slides:



Advertisements
Similar presentations
(Single-item) auctions Vincent Conitzer v() = $5 v() = $3.
Advertisements

6.896: Topics in Algorithmic Game Theory Lecture 20 Yang Cai.
(More on) characterizing dominant-strategy implementation in quasilinear environments (see, e.g., Nisan’s review: Chapter 9 of Algorithmic Game Theory.
Approximating optimal combinatorial auctions for complements using restricted welfare maximization Pingzhong Tang and Tuomas Sandholm Computer Science.
CPS Bayesian games and their use in auctions Vincent Conitzer
This Segment: Computational game theory Lecture 1: Game representations, solution concepts and complexity Tuomas Sandholm Computer Science Department Carnegie.
Game Theory in Wireless and Communication Networks: Theory, Models, and Applications Lecture 6 Auction Theory Zhu Han, Dusit Niyato, Walid Saad, Tamer.
Optimal auction design Roger Myerson Mathematics of Operations research 1981.
Variable Public Good Quantities E.g. how many broadcast TV programs, or how much land to include into a national park.
1. problem set 12 from Binmore’s Fun and Games. p.564 Ex. 41 p.565 Ex. 42.
Mechanism Design and Auctions Jun Shu EECS228a, Fall 2002 UC Berkeley.
Agent Technology for e-Commerce Chapter 10: Mechanism Design Maria Fasli
An Algorithm for Automatically Designing Deterministic Mechanisms without Payments Vincent Conitzer and Tuomas Sandholm Computer Science Department Carnegie.
Economics and Computer Science Introduction to Game Theory
Sequences of Take-It-or-Leave-it Offers: Near-Optimal Auctions Without Full Valuation Revelation Tuomas Sandholm and Andrew Gilpin Carnegie Mellon University.
Multi-unit auctions & exchanges (multiple indistinguishable units of one item for sale) Tuomas Sandholm Computer Science Department Carnegie Mellon University.
Mechanisms for a Spatially Distributed Market Moshe Babaioff, Noam Nisan and Elan Pavlov School of Computer Science and Engineering Hebrew University of.
Auctioning one item PART 2 Tuomas Sandholm Computer Science Department Carnegie Mellon University.
Exchanges = markets with many buyers and many sellers Let’s consider a 1-item 1-unit exchange first.
Complexity of Mechanism Design Vincent Conitzer and Tuomas Sandholm Carnegie Mellon University Computer Science Department.
Automated Mechanism Design: Complexity Results Stemming From the Single-Agent Setting Vincent Conitzer and Tuomas Sandholm Computer Science Department.
Yang Cai Sep 15, An overview of today’s class Myerson’s Lemma (cont’d) Application of Myerson’s Lemma Revelation Principle Intro to Revenue Maximization.
Multi-unit auctions & exchanges (multiple indistinguishable units of one item for sale) Tuomas Sandholm Computer Science Department Carnegie Mellon University.
CPS 173 Mechanism design Vincent Conitzer
Sequences of Take-It-or-Leave-it Offers: Near-Optimal Auctions Without Full Valuation Revelation Tuomas Sandholm and Andrew Gilpin Carnegie Mellon University.
Game representations, solution concepts and complexity Tuomas Sandholm Computer Science Department Carnegie Mellon University.
More on Social choice and implementations 1 TexPoint fonts used in EMF. Read the TexPoint manual before you delete this box.: AAA A Using slides by Uri.
6.853: Topics in Algorithmic Game Theory Fall 2011 Constantinos Daskalakis Lecture 21.
The Cost and Windfall of Manipulability Abraham Othman and Tuomas Sandholm Carnegie Mellon University Computer Science Department.
Mechanism Design CS 886 Electronic Market Design University of Waterloo.
Auction Theory תכנון מכרזים ומכירות פומביות Topic 7 – VCG mechanisms 1.
Game-theoretic analysis tools Tuomas Sandholm Professor Computer Science Department Carnegie Mellon University.
Mechanism design. Goal of mechanism design Implementing a social choice function f(u 1, …, u |A| ) using a game Center = “auctioneer” does not know the.
Regret Minimizing Equilibria of Games with Strict Type Uncertainty Stony Brook Conference on Game Theory Nathanaël Hyafil and Craig Boutilier Department.
Automated Mechanism Design Tuomas Sandholm Presented by Dimitri Mostinski November 17, 2004.
Mechanism Design II CS 886:Electronic Market Design Sept 27, 2004.
Mechanism design (strategic “voting”) Tuomas Sandholm Professor Computer Science Department Carnegie Mellon University.
6.853: Topics in Algorithmic Game Theory Fall 2011 Constantinos Daskalakis Lecture 22.
Web-Mining Agents Multiple Agents and Rational Behavior: Mechanism Design Ralf Möller Institut für Informationssysteme Universität zu Lübeck.
1 TexPoint fonts used in EMF. Read the TexPoint manual before you delete this box.: A AAA A AA A.
Comp/Math 553: Algorithmic Game Theory Lecture 10
Comp/Math 553: Algorithmic Game Theory Lecture 11
Automated mechanism design
Bayesian games and mechanism design
Ralf Möller Institut für Informationssysteme Universität zu Lübeck
Bayesian games and their use in auctions
Comp/Math 553: Algorithmic Game Theory Lecture 08
CPS Mechanism design Michael Albert and Vincent Conitzer
Failures of the VCG Mechanism in Combinatorial Auctions and Exchanges
Mechanism design with correlated distributions
Tuomas Sandholm Computer Science Department Carnegie Mellon University
Arbitration and Mediation
Applied Mechanism Design For Social Good
Implementation in Bayes-Nash equilibrium
Game Theory in Wireless and Communication Networks: Theory, Models, and Applications Lecture 6 Auction Theory Zhu Han, Dusit Niyato, Walid Saad, Tamer.
Market Design and Analysis Lecture 4
Implementation in Bayes-Nash equilibrium
Tuomas Sandholm Computer Science Department Carnegie Mellon University
Robust Mechanism Design with Correlated Distributions
Social choice theory = preference aggregation = voting assuming agents tell the truth about their preferences Tuomas Sandholm Professor Computer Science.
Vincent Conitzer Mechanism design Vincent Conitzer
Vincent Conitzer CPS 173 Mechanism design Vincent Conitzer
Automated mechanism design
Chapter Thirty-Five Public Goods.
Bayes Nash Implementation
Information, Incentives, and Mechanism Design
Auction Theory תכנון מכרזים ומכירות פומביות
Vincent Conitzer CPS Mechanism design Vincent Conitzer
CPS Bayesian games and their use in auctions
Class 2 – Revenue equivalence
Presentation transcript:

Implementation in Bayes-Nash equilibrium Tuomas Sandholm Computer Science Department Carnegie Mellon University

Implementation in Bayes-Nash equilibrium Goal is to design the rules of the game (aka mechanism) so that in Bayes-Nash equilibrium (s1, …, s|A|), the outcome of the game is f(u1, …, u|A|) Weaker requirement than dominant strategy implementation An agent’s best response strategy may depend on others’ strategies Agents may benefit from counterspeculating each others’ preferences rationality endowments capabilities … Can accomplish more than under dominant strategy implementation E.g., budget balance & Pareto efficiency (social welfare maximization) under quasilinear preferences …

dAGVA expected externality mechanism [d’Aspremont & Gerard-Varet 79; Arrow 79] Like Groves mechanism, but sidepayment is computed based on agent’s revelation vi , averaging over possible true types of the others v-i Outcome (x1, x2, ..., xk, m1, m2, ..., m|A| ) Quasilinear preferences: ui(x, m) = mi + vi(x1, x2, ..., xk) Utilitarian setting: Social welfare maximizing choice Outcome s(v1, v2, ..., v|A| ) = maxx i vi(x1, x2, ..., xk) Others’ expected welfare when agent i announces vi is (vi) = v-i p(v-i) ji vj(s(vi , v-i)) Measures change in expected externality as agent i changes her revelation Thrm. Assume quasilinear preferences and independently drawn valuation functions vi. A utilitarian social choice function f: v -> (s(v), m(v)) can be implemented in Bayes-Nash equilibrium if mi(vi)= (vi) + hi(v-i) for arbitrary function h Unlike in dominant strategy implementation, budget balance achievable Intuitively, have each agent contribute an equal share of others’ payments Formally, set hi(v-i) = - [1 / (|A|-1)] ji (vj) Does not satisfy participation constraints (aka individual rationality constraints) in general Agent might get higher expected utility by not participating

Myerson-Satterthwaite impossibility Avrim is selling a car to Tuomas, both are risk neutral, quasilinear Each party knows his own valuation, but not the other’s valuation The probability distributions are common knowledge Want a mechanism that is Ex post budget balanced Ex post Pareto efficient: Car changes hands iff vbuyer > vseller (Interim) individually rational: Both Avrim and Tuomas get higher expected utility by participating than not Thrm. Such a mechanism does not exist (even if randomized mechanisms are allowed) This impossibility is at the heart of more general exchange settings (NYSE, NASDAQ, combinatorial exchanges, …) !

Proof Seller’s valuation is sL w.p. a and sH w.p. (1-a) Buyer’s valuation is bL w.p. b and bH w.p. (1-b). Say bH > sH > bL > sL By revelation principle, can focus on truthful direct revelation mechanisms p(b,s) = probability that car changes hands given revelations b and s Ex post efficiency requires: p(b,s) = 0 if (b = bL and s = sH), otherwise p(b,s) = 1 Thus, E[p|b=bH] = 1 and E[p|b = bL] = a E[p|s = sH] = 1-b and E[p|s = sL] = 1 m(b,s) = expected price buyer pays to seller given revelations b and s Since parties are risk neutral, equivalently m(b,s) = actual price buyer pays to seller Buyer pays what seller gets paid  ex post budget balance E[m|b] = (1-a) m(b, sH) + a m(b, sL) E[m|s] = (1-b) m(bH, s) + b m(bL, s) Individual rationality (IR) requires b E[p|b] – E[m|b]  0 for b = bL, bH E[m|s] – s E[p|s]  0 for s = sL, sH Bayes-Nash incentive compatibility (IC) requires b E[p|b] – E[m|b]  b E[p|b’] – E[m|b’] for all b, b’ E[m|s] – s E[p|s]  E[m|s’] – s E[p|s’] for all s, s’ Suppose a=b= ½, sL=0, sH=y, bL=x, bH=x+y, where 0 < 3x < y. Now, IR(bL): ½ x – [ ½ m(bL,sH) + ½ m(bL,sL)]  0 IR(sH): [½ m(bH,sH) + ½ m(bL,sH)] - ½ y  0 Summing gives m(bH,sH) - m(bL,sL)  y-x Also, IC(sL): [½ m(bH,sL) + ½ m(bL,sL)]  [½ m(bH,sH) + ½ m(bL,sH)] I.e., m(bH,sL) - m(bL,sH)  m(bH,sH) - m(bL,sL) IC(bH): (x+y) - [½ m(bH,sH) + ½ m(bH,sL)]  ½ (x+y) - [½ m(bL,sH) + ½ m(bL,sL)] I.e., x+y  m(bH,sH) - m(bL,sL) + m(bH,sL) - m(bL,sH) So, x+y  2 [m(bH,sH) - m(bL,sL)]  2(y-x). So, 3x  y, contradiction. ■

Myerson-Satterthwaite impossibility… Actually, the impossibility applies to any priors, as long as the priors’ supports overlap, and the priors don’t have gaps The inefficiency is caused by two-sided private information

Advanced topics

Full implementation: virtual implementation Here we don’t necessarily assume that payments are possible Want to implement so that the right social choice function is achieved in all equilibria Virtual implementation: relax this by allowing a social choice within ε to be implemented (for all ε > 0) Thm [Serrano & Vohra GEB 2005]. Consider pure strategies only, finite type spaces, private types, and assume no-total-indifference (i.e., for each agent and each type, there are no ties in expected utility (as long as beliefs are updated using Bayes rule)). A social choice function in such environments is virtually Bayesian implementable iff it satisfies Bayesian incentive compatibility and a condition called virtual monotonicity. Virtual monotonicity is weak in the sense that it is generically satisfied in environments with at least three alternatives So, in most environments virtual Bayesian implementation is as successful as it can be (i.e., incentive compatibility is the only condition needed)

Full implementation: subgame perfection (Hart-Moore example of Moore-Repullo mechanism) Mechanism (assumes common knowledge between B and S)