Bayesian games and their use in auctions

1. Bayesian games and their use in auctions Adapted from notes by Vincent Conitzer

2. What is mechanism design? • In mechanism design, we get to design the game (or mechanism) • e.g. the rules of the auction, marketplace, election, … • Goal is to obtain good outcomes when agents behave strategically (game-theoretically) • Mechanism design is often considered part of game theory • 2007 Nobel Prize in Economics! • Before we get to mechanism design, first we need to know how to evaluate mechanisms

3. Which auction generates more revenue? • Each bid depends on • bidder’s true valuation for the item (utility = valuation - payment), • bidder’s beliefs over what others will bid (→ game theory), • and... the auction mechanism used • In a first-price auction, it does not make sense to bid your true valuation • Even if you win, your utility will be 0… • In a second-price auction, it always makes sense to bid your true valuation bid 1: $10 a likely outcome for the first-price mechanism a likely outcome for the second-price mechanism bid 1: $5 bid 2: $5 bid 2: $4 bid 3: $1 bid 3: $1 0 0 Are there other auctions that perform better? How do we know when we have found the best one?

4. Collusion in the Vickrey auction • Example: two colluding bidders v1 = first colluder’s true valuation v2 = second colluder’s true valuation price colluder 1 would pay when colluders bid truthfully gains to be distributed among colluders b = highest bid among other bidders price colluder 1 would pay if colluder 2 does not bid 0

5. The term “Bayesian” • "Bayesian" refers to the mathematician and theologian Thomas Bayes (1702–1761), who provided the first mathematical treatment of a non-trivial problem of Bayesian inference. • Bayesian probability is one of the different interpretations of the concept of probability and belongs to the category of evidential probabilities

6. The probability of H and D (joint probability) can be thought of in two ways: P(H,D) = P(H|D) * P(D) = P(D|H)*P(H)

8. Now, driving is not independent from gender. Given someone is a girl, what is the probability she doesn’t drive? Given someone doesn’t drive, what is the probability she is female?

10. Bayes theorem: The posterior probability is proportional to the likelihood of the observed data, multiplied by the prior probability. Thus P(H|D) = P(D|H)*P(H)/P(D) • P(H) is the prior probability of H: the probability that H is correct before the data D are seen. • P(D|H) is the conditional probability of seeing the data D given that the hypothesis H is true. This conditional probability is called the likelihood. • P(D) is the probability of D. • P(H|D) is the posterior probability: the probability that H is true, given the data and previous state of beliefs about H.

11. Example • Marie is getting married tomorrow. In recent years, it has rained only 5 days each year. Unfortunately, the weatherman has predicted rain for tomorrow. When it actually rains, the weatherman correctly forecasts rain 90% of the time. When it doesn't rain, he incorrectly forecasts rain 5% of the time. What is the probability that it will rain on the day of Marie's wedding? • P(rain) = 5/365 • P(not raining) = 360/365 • P(weatherman predicting rain|rain) = .9 • P(weatherman predicting rain|does NOT rain) = .05

12. What is the probability of rain, given the weatherman predicts rain? • P( rain | predict rain) =   P(rain) P( predict rain| rain ) / P(predict rain) P(predict rain) = [P( rain)* P( predict rain| rain ) + P( no rain)* P( predict rain| no rain ) ] P( rain | predict rain) = (0.014)(0.9) / [ (0.014)(0.9) + (0.986)(0.05) P( rain | predict rain)] = 0.2035

13. Bayesian games • What if we didn’t even know what game is being played? (Note that in imperfect information games, we didn’t know prior moves, but we at least knew the payoffs) • On the exam, the “gift” question – both players knew whether or not a gift had been given, but suppose they didn’t have that knowledge? • We say the “type” of agent determines the game we are playing

14. Expected Utility • Ex post: utility based on all agent’s actual types • Ex interim: agent knows his own type, but not that of others • Ex ante: the agent does not know anybody’s type

15. Bayesian games • In a Bayesian game a player’s utility depends on that player’s type as well as the actions taken in the game • Notation: θiis player i’s type, drawn according to some distribution from set of types Θi • In this example, each player knows/learns its own type, not those of the others, before choosing action • Pure strategy siis a mapping from Θito Ai (where Ai is i’s set of actions) • In general players can also receive signals about other players’ utilities. Assume all know the probabilities of being in each game. L R column player U row player type 1 D (.3) (.1) L R U row player type 2 (.2) (.4) D

16. Induced normal form (based on probabilities – no types are known) If we had a NE in this induced normal form, it would be called a Bayes NE.

17. Induced normal form (based on probabilities given player 1 knows his type is type 1) Since player 1 knows he is in top games, in left game ¾ time, right ¼.

18. Could rewrite with only two rows Since player 1 knows he is in top games, in left game ¾ time, right ¼. Meaningless to find Nash equilibrium, as payoffs aren’t common knowledge

19. With commonvaluations • E.g. bidding on drilling rights for an oil field • Each bidder i has its own geologists who do tests, based on which the bidder assesses an expected value vi of the field • If you win, it is probably because the other bidders’ geologists’ tests turned out worse, and the oil field is not actually worth as much as you thought • The so-called winner’s curse • So if you have common valuation, what would you bid in a second price auction?

20. With commonvaluations • E.g. bidding on drilling rights for an oil field • Each bidder i has its own geologists who do tests, based on which the bidder assesses an expected value vi of the field • If you win, it is probably because the other bidders’ geologists’ tests turned out worse, and the oil field is not actually worth as much as you thought • The so-called winner’s curse • Hence, bidding vi is no longer a dominant strategy in the second-price auction • In English and Japanese auctions, you can update your valuation based on other agents’ bids, so no longer equivalent to second-price • In these settings, English (or Japanese) > second-price > first-price/Dutch in terms of revenue

21. Problem • You and your siblings have been left with an estate consisting of many items • The value of each item is correlated (partially common and partially private) • How can you divide the property so that the division is fair?

22. Auctions without a seller • faculty from a certain department in a university may want to decide who get to use a limited number of seminar rooms on a certain day, so that the department as a whole benefits the most; • a group of housemates who collectively own a car may want to decide who gets to use it on a particular weekend, so that the one who needs it the most gets to use it. • Agents are self interested and may lie about their valuation.

23. Caution • We are programmed to think that auctions are about earning money. It may be that we are only trying to distribute goods fairly. • In the case of dividing the estate, if there is no cost, people could be greedy or overstate their utility (given an inefficient allocation, right?). • Children’s book: Instead of charging people for items, everything was given away. The logic was that people who made baseball bats loved to do it, so they were happy to give them away to someone who would enjoy it. Then, they could make more. • I remember thinking this was a great idea.

24. no deficit: it is reasonable to assume that a mechanism shall not subsidize any allocation it comes up with, i.e. the total payments made by the agents to the center must always be non-negative • a stronger version of the no deficit property called strong budget balance, which says the total payments made by the agents to the center must be zero (useful property if jointly owned) • strategy proofness means `truthful in dominant strategy'.

25. VCG mechanism fails to have the strong budget balance property (as the auctioneer makes money), it is in fact impossible to find a mechanism that has this property and at the same time is truthful and efficient in dominant strategy Solutions? • Redistribute as much of the VCG revenue as possible back to the agents • Sacrifice truthfulness • Sacrifice efficiency (social optimality)

26. Vickrey auction without a seller v( ) = 4 ) = 2 ) = 3 v( v( pays 3 (money wasted!)‏ But if there is no cost, people won’t be honest about their valuation

27. Can we redistribute the payment? Idea: give everyone 1/n of the payment ) = 4 v( ) = 2 ) = 3 v( v( receives 1 receives 1 pays 3 receives 1 notstrategy-proof: A loser (bidding higher) increases his redistribution payment He gets 1/n of the “value” as determined by the auction- so he benefits by increasing the selling price

28. Incentive compatible redistribution[Bailey 97, Porter et al. 04, Cavallo 06] Idea: give everyone 1/n of second-highest otherbids ) = 4 v( v( ) = 2 v( ) = 3 receives 1 Who pays what? Strategy-proof Your redistribution does not depend on your bid; incentives are the same as in Vickrey

29. Incentive compatible redistribution[Bailey 97, Porter et al. 04, Cavallo 06] Idea: give everyone 1/n of second-highest otherbids ) = 4 v( v( ) = 2 v( ) = 3 receives 2/3 receives 1 pays 3 receives 2/3 2/3 wasted (22%)‏ Strategy-proof Your redistribution does not depend on your bid; incentives are the same as in Vickrey

30. R1 = V3/n R2 = V3/n R3 = V2/n R4 = V2/n ... Rn-1= V2/n Rn = V2/n Bailey-Cavallo mechanism… • Bids: V1≥V2≥V3≥... ≥Vn≥0 • First run Vickrey auction • Payment is V2 by bidder 1 • First two bidders receiveV3/n • Remaining bidders receive V2/n • Total redistributed:2V3/n+(n-2)V2/n • So why can’t we give it all to the remaining bidders?

31. R1 = V3/(n-2) - 2/[(n-2)(n-3)]V4 R2 = V3/(n-2) - 2/[(n-2)(n-3)]V4 R3 = V2/(n-2) - 2/[(n-2)(n-3)]V4 R4 = V2/(n-2) - 2/[(n-2)(n-3)]V3 ... Rn-1= V2/(n-2) - 2/[(n-2)(n-3)]V3 Rn = V2/(n-2) - 2/[(n-2)(n-3)]V3 Another redistribution mechanism • Bids: V1≥V2≥V3≥V4≥... ≥Vn≥0 • First run Vickrey • Redistribution: Receive 1/(n-2) * second-highest other bid, - 2/[(n-2)(n-3)] third-highest other bid • Total redistributed: V2-6V4/[(n-2)(n-3)] Key point is their return is never a function of their bid Idea pursued further in Guo & Conitzer 07 / Moulin 07