Computational Game Theory Amos Fiat Spring 2012 More

Computational Game Theory Amos Fiat Spring 2012 More on Social choice and implementations Using slides by Uri Feige Robi Krauthgamer Moni Naor Costas Daskalakis 1

Examples: Second Price Auction Second Price auction: hi(t-i) = maxj(w 1, w 2, …, wi-1, wi+1, …, wn) = maxb 2 A j i tj(b) Multiunit auction : if k identical items are to be sold to k individuals. A={S wins |S ½ I, |S|=k} and vi(S) = 0 if i 2 S and vi(i)=wi if i 2 S Allocate units to top k bidders. They pay the k+1 th price Claim: this is max. S’ ½ I{i} |S’| =k j i vj(S’)- j i vj(S)

Generalized Second Price Auctions

VCG with Public Project Want to build a bridge: ◦ Cost is C (if built) ◦ Value to each individual vi ◦ Want to built iff i vj ¸ C A={build, not build} Player with vj ¸ 0 pays only if pivotal j i vj < C but j vj ¸ C in which case pays pj = C- j i vj In general: i pj < C Equality only when i vj = C Payments do not cover project cost’s Subsidy necessary!

Buying a (Short) Path in a Graph A Directed graph G=(V, E) where each edge e is “owned” by a different player and has cost ce. Want to construct a path from source s to destination t. Set A of alternatives: all s -t paths How do we solicit the real cost ce? ◦ Set of alternatives: all paths from s to t ◦ Player e has cost: 0 if e not on chosen path and –ce if on ◦ Maximizing social welfare: finding shortest s-t path: min paths e 2 path ce A VCG mechanism pays 0 to those not on path p: pay each e 0 2 p: e 2 p’ ce - e 2 p{e } ce where p’ is shortest path without eo 0 If e 0 would not have woken up in the morning, what would other edges earn? If he does wake up, what would other edges earn?

VCG (+CPP) is not perfect • Requires payments & quasilinear utility functions • In general money needs to flow away from the system – Strong budget balance = payments sum to 0 – Impossible in general [Green & Laffont 77] • Vulnerable to collusions • Maximizes sum of players’ valuations (social welfare) – (not counting payments, but does include “COST” of alternative) But: sometimes [usually, often? ? ] the mechanism is not interested in maximizing social welfare: – – E. g. the center may want to maximize revenue Minimize time Maximize fairness Etc. , Etc.

Black slides from Costas Daskalakis, MIT 6. 853: Topics in Algorithmic Game Theory Fall 2011 Vickrey’s auction and VCG are both single round and direct-revelation mechanisms. We now consider a general model of mechanisms. It can model multi-round and indirect-revelation mechanisms.

Games with Strict Incomplete Information Def: A game with (independent private values and) strict incomplete information for a set of n players is given by the following ingredients: (i) (iii)

Strategy and Equilibrium Def: A strategy of a player i is a function Def: Equilibrium (ex-post Nash and dominant strategy) ● A profile of strategies is an ex-post Nash equilibrium if for all i, all , and all we have that ● A profile of strategies if for all i, all , and all is a dominant strategy equilibrium we have that

Equilibrium (cont’d) Proposition: Let be an ex-post Nash equilibrium of a game Define equilibrium in the game , then is a dominant strategy

Mechanism Def: A (general-non direct revelation) mechanism for n players is given by The game with strict incomplete information induced by the mechanism has the same type spaces and action spaces, and utilities

Implementing a social choice function Given a social choice function A mechanism implements strategy equilibrium , in dominant strategies if for some dominant of the induced game, we have that for all. Ex: Vickrey’s auction implements the maximum social welfare function in dominant strategies, because a dominant strategy. Nash equilibrium, and maximum Similarly weiscan define ex-post implementation. social welfare is achieved at this equilibrium. outcome of the social choice function outcome of the mechanism at the equilibrium Remark: We only requires that for some equilibrium and allows other equilibria to exist.

The Revelation Principle

Revelation Principle We have defined direct revelation mechanisms in previous lectures. Clearly, the general definition of mechanisms is a superset of the direct revelation mechanisms. But is it strictly more powerful? Can it implement some social choice functions in dominant strategy that the incentive compatible (direct revelation dominant strategy implementation) mechanism can not?

Revelation Principle Proposition: (Revelation principle) If there exists an arbitrary mechanism that implements in dominant strategies, then there exists an incentive compatible [direct revelation] mechanism that implements. The payments of the players in the incentive compatible mechanism are identical to those, obtained at equilibrium, of the original mechanism.

Incentive Compatible Def: A mechanism is called incentive compatible, or truthful , or strategy-proof iff for all i, for all and for all utility of i if he says the truth utility of i if he lies i. e. no incentive to lie!

Revelation Principle Proposition: (Revelation principle) If there exists an arbitrary mechanism that implements in dominant strategies, then there exists an incentive compatible mechanism that implements. The payments of the players in the incentive compatible mechanism are identical to those, obtained at equilibrium, of the original mechanism. Proof idea: Simulation

Revelation Principle (cont’d) new mechanism original mechanism

Proof of Revelation Principle Proof: Let be a dominant strategy equilibrium of the original mechanism such that , we define a new direct revelation mechanism: Since each have that is a dominant strategy for player i, for every Thus in particular this is true for all we have that , we and any which gives the definition of the incentive compatibility of the mechanism.

Revelation Principle (cont’d) Corollary: If there exists an arbitrary mechanism that ex-post Nash equilibrium implements , then there exists an incentive compatible mechanism that implements. Moreover, the payments of the players in the incentive compatible mechanism are identical to those, obtained in equilibrium, of the original mechanism. Proof sketch: Restrict the action spaces of each player. By the previous proposition, we know in the restricted action spaces, the mechanism implements the social choice function in dominant strategies. Now we can invoke the revelation principle to get an incentive compatible mechanism.

Characterizations of Incentive Compatible Mechanisms

Characterizations What social choice functions can be implemented? ● Only look at incentive compatible mechanisms (revelation principle) ● When is a mechanism incentive compatible? Characterizations of incentive compatible mechanisms. ● Maximization of social welfare can be implemented (VCG). Any others? Basic characterization of implementable social choice functions.

Direct Characterization

Direct Characterization A mechanism is incentive compatible iff it satisfies the following conditions for every i and every : (i) i. e. , for every , there exists a price chosen alternative is , the price is , when the (ii) i. e. , for every , we have alternative where the quantification is over all alternatives in the range of

Direct Characterization (cont’d) Proof: (if part) Denote and. Since the mechanism optimizes for i, the utility of i when telling the truth is not less than the utility when lying.

Direct Characterization (cont’d) Proof (cont): (only if part; (i)) If for some type , but. WLOG, we assume. Then a player with will increase his utility by declaring. (only if part; (ii)) If Now a player with type utility by declaring. , we fix and will increase his

Weak Monotonicity

Weak Monotonicity ●The direct characterization involves both the social choice function and the payment functions. ● Weak Monotonicity provides a partial characterization that only involves the social choice function.

Weak Monotonicity (WMON) Def: A social choice function satisfies Weak Monotonicity (WMON) if for all i, all we have that i. e. WMON means that if the social choice changes when a single player changes his valuation, then it must be because the player increased his value of the new choice relative to his value of the old choice.

Weak Monotonicity Theorem: If a mechanism is incentive compatible then satisfies WMON. If all domains of preferences are convex sets (as subsets of an Euclidean space) then for every social choice function that satisfies WMON there exists payment function such that is incentive compatible. Remarks: (i) We will prove the first part of theorem. The second part is quite involved, and will not be given here. (ii) It is known that WMON is not a sufficient condition for incentive compatibility in general non-convex domains.

Weak Monotonicity (cont’d) Proof: (First part) Assume first that is incentive compatible, and fix i and in an arbitrary manner. The direct characterization implies the existence of fixed prices for all (that do not depend on ) such that whenever the outcome is then i pays exactly. Assume is incentive compatible, we have Thus, we have . Since the mechanism

Minimization of Social Welfare We know maximization of social welfare function can be implemented. How about minimization of social welfare function? No! Because of WMON.

Minimization of Social Welfare Assume there is a single good. WLOG, let case, player 1 wins the good. If we change to , such that Now we can apply the WMON. . In this . Then player 2 wins the good. The outcome changes when we change player 1’s value. But according to WMON, it should be the case that. But. Contradiction.

Weak Monotonicity WMON is a good characterization of implementable social choice functions, but is a local one. Is there a global characterization?

Weighted VCG

Affine Maximizer Def: A social choice function is called an affine maximizer if for some subrange , for some weights and for some outcome weights , for every , we have that

Payments for Affine Maximizer Proposition: Let on be an affine maximizer. Define for every i, where is an arbitrary function that does not depend. Then, is incentive compatible.

Payments for Affine Maximizer Proof: First, we can assume wlog. The utility of player i if alternative is chosen is. By multiplying by this expression is maximized when is maximized which is what happens when i reports truthfully.

Roberts Theorem [Roberts 79]: If , is onto , for every i, and is incentive compatible then is an affine maximizer. Remark: The restriction is crucial (as in Arrow’s theorem), for the case there do exists incentive compatible mechanisms beyond VCG. ,

Bayesian Mechanism Design Def: A Bayesian mechanism design environment consists of the following: setup + for every bidder a distribution is known; bidder i’s type is sampled from it Def: A Bayesian mechanism consists of the following: mech The utility that bidder i receives if the players’ actions are x 1, …, xn is :

Strategy and Equilibrium Def: A strategy of a player i is a function Def: A profile of strategies if for all i, all , and all . is a Bayes Nash equilibrium we have that expected utility of bidder i for using si, where the expectation is computed with respect to the actions of the other bidders assuming they are using their strategies expected utility if bidder i uses a different action xi’; still the expectation is computed with respect to the actions of the other bidders assuming they are using their strategies

First Price Auction Theorem: Suppose that we have a single item to auction to two bidders whose values are sampled independently from [0, 1]. Then the strategies s 1(t 1)=t 1/2 and s 2(t 2)=t 2/2 are a Bayes Nash equilibrium. Remark: In the above Bayes Nash equilibrium, social welfare is optimized, since the highest winner gets the item. Proof: ? ? ? . Expected Payoff? E[ max(X/2, Y/2)], where X, Y are independent U[0, 1] random variables. =1/3 Expected Payoff of second price auction? E[ min(X, Y)], where X, Y are independent U[0, 1] random variables. =1/3 !

Revenue Equivalence Theorem All single item auctions that allocate (in Bayes Nash equilibrium) the item to the bidder with the highest value and in which losers pay 0 have identical expected revenue. Generally: Given a social choice function we say that a Bayesian mechanism implements if for some Bayes Nash equilibrium we have that for all types : Revenue Equivalence Theorem: For two Bayesian-Nash implementations of the same social choice function f, * we have the following: if for some type t 0 i of player i, the expected (over the types of the other players) payment of player i is the same in the two mechanisms, then it is the same for every value of ti. In particular, if for each player i there exists a type t 0 i where the two mechanisms have the same expected payment for player i, then the two mechanisms have the same expected payments from each player and their expected revenues are the same.

Revenue Equivalence Theorem (cont. ) All single item auctions that allocate (in Bayes Nash equilibrium) the item to the bidder with the highest value and in which losers pay 0 have identical expected revenue. So unless we modify the social choice function we won’t increase revenue. E. g. increasing revenue in single-item auctions: - two uniform [0, 1] bidders - run second price auction with reservation price ½ (i. e. give item to highest bidder above the reserve price (if any) and charge him the second highest bid or the reserve, whichever is higher. Claim: Reporting the truth is a dominant strategy equilibrium. The expected revenue of the mechanism is 5/12 (i. e. larger than 1/3).

Bayesian Nash Implementation There is a distribution Di on the types Ti of Player i It is known to everyone The value ti 2 Di. Ti is the private information i knows A profile of strategis si is a Bayesian Nash Equilibrium if for i all ti and all x’i Ed-i[ui(ti, si(ti), s-i(t-i) )] ¸ Ed-i[ui(ti, s-i(t-i)) ]

Bayesian Nash: First Price Auction First price auction for a single item with two players. Each has a private value t 1 and t 2 in T 1=T 2=[0, 1] Does not make sense to bid true value – utility 0. There are distributions D 1 and D 2 Looking for s 1(t 1) and s 2(t 2) that are best replies to each other Suppose both D 1 and D 2 are uniform. Claim: The strategies s 1(t 1) = ti/2 are in Bayesian Nash Equilibrium Win half the time t 1 Cannot win

Expected Revenues Expected Revenue: ◦ For first price auction: max(T 1/2, T 2/2) where T 1 and T 2 uniform in [0, 1] ◦ For second price auction min(T 1, T 2) ◦ Which is better? ◦ Both are 1/3. ◦ Coincidence? Theorem [Revenue Equivalence] : under very general conditions, every two Bayesian Nash implementations of the same social choice function if for some player and some type they have the same expected payment then ◦ All types have the same expected payment to the player ◦ If all player have the same expected payment: the