First price auctions with general information structures: Implications for bidding and revenue

Transcription

1 First price auctions with general information structures: Implications for bidding and revenue Dirk Bergemann Yale Benjamin Brooks BFI/UChicago Stephen Morris Princeton New York University Abu Dhabi December 2016

2 Premises 1. Classical auction theory makes stylized assumptions about information 2. Assumptions about information are hard to test 3. Equilibrium behavior can depend a lot on how we specify information

3 Promises Goal: a theory of bidding that is robust to specication of information First attempt: First price auction Hold xed underlying value distribution, Consider all specications of information and equilibrium We deliver: A tight lower bound on the winning bid distribution A tight lower bound on revenue A tight upper bound on bidder surplus Other results on max revenue, min bidder surplus, min eciency

4 A (toy) model of a rst price auction Two bidders Pure common value v U[0, 1] Submit bids b i R + High bidder gets the good and pays bid = winner's surplus is v b i Allocation of good is always ecient, total surplus 1/2 Seller's expected revenue is R = E[max{b 1, b 2 }] Bidder surplus U = 1/2 R What predictions can we make about U and R in equilibrium?

5 Filling in beliefs What do bidders know about the value? What do they know about what others know? Assume beliefs are consistent with a common prior Still, many possible ways to ll in information: Bidders observe nothing; Unique equilibrium: b 1 = b 2 = R = 1/2 Bidders observe everything; b 1 = b 2 = v, R = 1/2 True information structure is likely somewhere in between: Bidders have some information about v, but not perfect But exactly how much information do they have?

6 Lower revenue? Engelbrecht-Wiggans, Milgrom, Weber (1983, EMW): Bidder 1 observes v, bidder 2 observes nothing b 1 = v/2, b 2 U[0, 1/2] and independent of v Bidder 2 is indierent: With a bid of b 2 [0, 1/2], will win whenever v 2b 2 Expected value is exactly b 2! Bidder 1 wins with a bid of b 1 with probability 2b 1 Surplus is (v b 1 )2b 1 = optimal to bid b 1 = v/2! U 1 = 1 v=0 v(v v/2)dv = 1/6, U 2 = 0, R = 1/3

7 How we model beliefs matters Welfare outcomes are sensitive to modelling of information Why? Optimal bid depends on distribution of others' bids, and on correlation between others' bids and values Problem: hard to say which specication is correct What welfare predictions do not depend on how we model information?

8 Uniform example continued Can we characterize minimum revenue? Must be greater than zero! But seems likely to be lower than EMW At min R, winning bids have been pushed down as far as they can go Force pushing back must be incentive to deviate to higher bids In EMW, informed bidder strictly prefers equilibrium bid

9 Towards a Bound: Winning Bid Consider symmetric equilibria in which winning bid is an increasing and deterministic function β(v) of true value v Which β could be incentive compatible in equilibrium? Consider the following uniform upward deviation to b: Whenever equilibrium bid, winning or not, is b < b, bid b instead! Now let bids b, b be winning bids for some values x, v respectively: b = β(x)<β(v) = b

10 Towards a Bound: Uniform Upward Deviation Now let bids b, b be winning bids for some values x, v respectively: b = β(x)<β(v) = b Bid b could have been a loosing or a winnig bid Uniform upward deviation to b = β(v) is not attractive if v 1 (β(v) β(x))dx 1 (x β(v))dx 2 x=0 2 x=0 }{{}}{{} loss when would have won v gain when would have lost Using symmetry (1/2) and deterministic winning bid β(v)

11 Restrictions on β Uniform upward deviation to b = β(v) v rearranges to 1 (β(v) β(x))dx 1 (x β(v))dx 2 x=0 2 x=0 }{{}}{{} loss when would have won v gain when would have lost β(v) 1 v (x + β(x))dx (IC) 2v x=0 What is the smallest β subject to (IC) and β 0? Must solve (IC) with equality for all v

12 Minimal Winning Bid β uniform upward deviation solves β(v) = 1 v (x + β(x))dx (IC) 2v x=0 β is conditional expectation of (average of) value and β: β(v) = 1 v v x=0 x 1 2 x dx = v 3 Conditional Expectation with respect to F (v) 1/2 = v 1/2. Compare to the bid b(v) = v/2, not even winning bid in EMW.

13 A lower bound on revenue Induced distribution of winning bids is U[0, 1/3] Revenue is 1/6 In fact, symmetry/deterministic winning bid are not needed Distribution of winning bid has to FOSD U[0, 1/3] in all equilibria under any information 1/6 is a global lower bound on equilibrium revenue

14 Bound is tight Can construct information/equilibrium that hits bound Bidders get i.i.d. signals s i F (x) = x on [0, 1] Value is highest signal Distribution of highest signal is U[0, 1] Equilibrium bid: σ i (s i ) = s i /3 ( = β(s i ) ) Defer proof until general results

15 Beyond the example Argument generalizes to: Any common value distribution! Any number of bidders! Arbitrarily correlated values!!! Assume symmetry of value distribution for some results Minimum bidding is characterized by a deterministic winning bid given the true values In general model, only depends on a one-dimensional statistic of the value prole Bound is characterized by binding uniform upward incentive constraints

16 The plan Detailed exposition of minimum bidding Maximum revenue/minimum bidder surplus Restrictions on information Other directions in welfare space (e.g., eciency)

17 General model N bidders Distribution of values: P(dv 1,..., dv N ) Support of marginals V = [v, v] R + An information structure S consists of A measurable space S i of signals for each player i, S = N i=1 S i A conditional probability measure π : V N (S)

18 Equilibrium Bidders' strategies map signals to distributions over bids in [0, v] σ i : S i (B) Assume weakly undominated strategies: bidder i never bids strictly above the support of rst-order beliefs about v i Bidder i's payo given strategy prole σ = (σ 1,..., σ N ): U i (σ, S) = v V s S σ is a Bayes Nash equilibrium if I {bi b j, j} (v i b i ) b B N arg max j b j σ(db s)π(ds v)p(dv) U i (σ, S) U i (σ i, σ i, S) i, σ i

19 Other welfare outcomes Bidder surplus: U(σ, S) = Revenue: R(σ, S) = N U i (σ, S) i=1 v V N s S max b B N i Total surplus: T (σ, S) = R(σ, S) + U(σ, S) Ecient surplus: T = max v i P(dv) i v V b i σ(b s)π(ds v)p(dv)

20 General common values As we generalize, minimum bidding continues to be characterized by a deterministic winning bid given values: β(v 1,..., v N ) β has an explicit formula Consider pure common values with v P ([v, v]) Minimum winning bid generalizes to β(v) = 1 v P(v) x=v x P(dx) 2 P(x) P(v) 1/2 generalizes to P(v) (N 1)/N with N bidders Minimum revenue: R = v v=v β(v)p(dv)

21 N = 2 and general value distributions Write P(dv 1, dv 2 ) for value distribution Similarly, lots of binding uniform upward IC Incentive to deviate up depends on value when you lose On the whole, ecient allocation reduces gains from deviating up Suggests minimizing equilibrium is ecient, winning bid is constrainted by loser's (i.e., lowest) value

22 General bounds for N = 2 Similar β, but now depends on lowest value Q(dm) is distribution of m = min{v 1, v 2 } (assume non-atomic) Minimum winning bid is β(m) = Minimum revenue: R = 1 v Q(m) v m=v x=v x Q(dx) 2 Q(x) β(m)q(dm)

23 Losing values when N > 2 With N > 2, bid minimizing equilibrium should still be ecient Intuition: coarse information about losers' values lowers revenue Consider complete information, all values are common knowledge High value bidder wins and pays second highest value

24 Average losing values I Simple variation: Bidders only observe (i) High value bidder's identity (ii) Distribution of values Winner is still high value bidder, but losing bidders don't know who has which value If prior is symmetric, believe they are equally likely to be at any point in the distribution except the highest In equilibrium, winner pays average of N 1 lowest values: ( µ(v 1,..., v N ) = 1 N ) v i max v i N 1 i i=1

25 General bounds Q(dm) is distribution of m = µ(v) (assume non-atomic) Minimum winning bid and revenue: β(m) = = 1 Q N 1 N (v) 1 Q N 1 N (v) v x=v v x=v x N 1 N Q(dx) Q 1 N (x) x Q N 1 N (dx) Minimum revenue: R = Let H(b) = Q(β 1 (b)) v m=v β (m) Q (dm)

26 Main result Theorem (Minimum Winning Bids) 1. In any equilibrium under any information structure in which the marginal distribution of values is P, the distribution of winning bids must rst-order stochastically dominate H. 2. Moreover, there exists an information structure and an ecient equilibrium in which the distribution of winning bids is exactly H.

27 Implications Corollary (Minimum revenue) Minimum revenue over all information structures and equilibria is R.

28 Implications Corollary (Minimum revenue) Minimum revenue over all information structures and equilibria is R. Corollary (Maximum bidder surplus) Maximum total bidder surplus over all information structures and equilibria is T R.

29 Proof methodology 1. Obtain a bound via relaxed program 2. Construct information and equilibrium that attain the bounds (start with #2)

30 Minimizing equilibrium and information Bidders receive independent signals s i Q 1/N (s i ) = distribution of highest signal is Q(s) Signals are correlated with values s.t. Highest signal is true average lowest value, i.e., µ(v 1,..., v n ) = max{s 1,..., s n } Bidder with highest signal is also bidder with highest value, i.e., arg max i s i arg max v i i All bidders use the monotonic pure-strategy β(s i )

31 Proof of equilibrium β is the equilibrium strategy for an as-if IPV model, in which v i = s i IC for IPV model with independent draws from Q 1/N : Local IC: (s i σ(s i ))Q N 1 N (si ) Solution is precisely (s i σ(s i ))Q N 1 N (dsi ) σ (s i )Q N 1 N (si ) = 0 σ(s i ) = 1 Q N 1 N (s i ) si x=v x Q N 1 N (dx) = β(si )

32 Proof of equilibrium β is the equilibrium strategy for an as-if IPV model, in which v i = s i IC for IPV model with independent draws from Q 1/N : Local IC: (s i σ(s i ))Q N 1 N (si ) Solution is precisely (s i σ(s i ))Q N 1 N (dsi ) σ (s i )Q N 1 N (si ) = 0 σ(s i ) = 1 Q N 1 N (s i ) si x=v x Q N 1 N (dx) = β(si )

33 Proof of equilibrium β is the equilibrium strategy for an as-if IPV model, in which v i = s i IC for IPV model with independent draws from Q 1/N : Local IC: (s i σ(s i ))Q N 1 N Solution is precisely (si ) (s i σ(m))q N 1 N (m) (s i σ(s i ))Q N 1 N (dsi ) σ (s i )Q N 1 N (si ) = 0 σ(s i ) = 1 Q N 1 N (s i ) si x=v x Q N 1 N (dx) = β(si )

34 Downward deviations Expectation of the bidder with the highest signal is ṽ(s i ) s i Downward deviator obtains surplus and (ṽ(s i ) β(m))q N 1 N (m) (ṽ(si ) β(m) ) Q N 1 N ( s i β(m) ) Q N 1 N (dm) β (m)q N 1 N (m) (dm) β (m)q N 1 N (m) Well-known that IPV surplus is single peaked: if m < s i, = (s i β(m))q N 1 N (dm) β (m)q N 1 N (dm) 0

35 Average losing values II Winning bids depend on avg of lowest values = average of losing bids (since equilibrium is ecient) Suppose winning bid in equilibrium is β(m) > β(s i ) = µ(v) = m for true values v By symmetry, all permutations of v are in µ 1 (m) and equally likely If you only know that (i) you lose in equilibrium and (ii) v µ 1 (m), you expect your value to be m! By deviating up to win on this event, gain m in surplus

36 Upward deviations Upward deviator's surplus m (ṽ(s i ) β(m))q N 1 N (si ) + (x β(m))q N 1 N (dx) x=s i Derivative w.r.t. m: (m β(m))q N 1 N (dm) β(m) Q N 1 N (m) = 0! In eect, correlation between others bids' and losing values induces adverse selection s.t. losing bidders are indierent to deviating up

37 Towards a general bound Claim is that construction attains a lower bound Show this via relaxed program Minimum CDF of winning bids subject to uniform upward IC Key WLOG properties of solution (and minimizing equilibrium): 1. Symmetry 2. Winning bid depends on average losing value 3. Eciency 4. Monotonicity of winning bids in losing values 5. All uniform upward IC bind

38 Other directions We talked about max/min revenue, max/min bidder surplus What about weighted sums? Minimum eciency? More broadly, what is the whole set of possible (U, R) pairs? Solved numerically for two bidder i.i.d. U[0, 1] model

39 Welfare set 2/3 F Unknown values Known values G C Revenue (R) 1/3 H B A E D 0 0 1/3 2/3 Bidder surplus (U) Note: Lower bound on eciency

40 What can we do with this? Applications/extensions: Many bidder limit Impact of reserve prices/entry fees Identication Other directions in welfare space Context: Part of a larger agenda on robust predictions and information design