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Common value auction

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Auctions
Auction Room, Christie's, circa 1808.
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Incommon valueauctions the value of the item for sale is identical amongst bidders, but bidders have different information about the item's value. This stands in contrast to aprivate value auction where each bidder's private valuation of the item is different and independent of peers' valuations.[1]

A classic example of a pure common valuesauction is when a jar full of quarters is auctioned off. The jar will be worth the same amount to anyone. However, each bidder has a different guess about how many quarters are in the jar. Other, real-life examples include Treasury bill auctions, initial public offerings, spectrum auctions, very prized paintings, art pieces, antiques etc.

One important phenomenon occurring in common value auctions is thewinner's curse. Bidders have only estimates of the value of the good. If, on average, bidders are estimating correctly, the highest bid will tend to have been placed by someone who overestimated the good's value. This is an example ofadverse selection, similar to the classic "lemons" example ofAkerlof. Rational bidders will anticipate the adverse selection, so that even though their information will still turn out to have been overly optimistic when they win, they do not pay too much on average.

Sometimes the term winner's curse is used differently, to refer to cases in which naive bidders ignore the adverse selection and bid sufficiently more than a fully rational bidder would that they actually pay more than the good is worth. This usage is prevalent in the experimental economics literature, in contrast with the theoretical and empirical literatures on auctions.

Interdependent value auctions

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Common-value auctions and private-value auctions are two extremes. Between these two extremes areinterdependent value auctions (also called:affiliated value auctions), where bidder's valuations (e.g.,θi=θ+νi{\displaystyle \theta _{i}=\theta +\nu _{i}}) can have a common value component (θ{\displaystyle \theta }) and a private value (νi{\displaystyle \nu _{i}}) component. The two components can be correlated so that one bidder's private valuation can influence another bidder's valuation.[2] These types of auctions comprise most real-world auctions and are sometimes confusingly referred to as common value auctions also.

Examples

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In the following examples, a common-value auction is modeled as aBayesian game. We try to find aBayesian Nash equilibrium (BNE), which is a function from the information held by a player, to the bid of that player. We focus on asymmetric BNE (SBNE), in which all bidders use the same function.

Binary signals, first-price auction

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The following example is based onAcemoglu andÖzdağlar.[3]: 44–46 

There are two bidders participating in afirst-price sealed-bid auction for an object that has either high quality (value V) or low quality (value 0) to both of them. Each bidder receives a signal that can be either high or low, with probability 1/2. The signal is related to the true value as follows:

  • If at least one bidder receives a low signal, then the true value is 0.
  • If both receive a high signal, then the true value is V.

This game has no SBNE in pure-strategies.

PROOF: Suppose that there was such an equilibriumb. This is a function from a signal to a bid, i.e., a player with signalx bidsb(x). Clearlyb(low)=0, since a player with low signal knows with certainty that the true value is 0 and does not want to pay anything for it. Also,b(high) ≤ V, otherwise there will be no gain in participation. Suppose bidder 1 hasb1(high)=B1 > 0. We are searching the best-response for bidder 2,b2(high)=B2. There are several cases:

  1. The other bidder bids B2 < B1. Then, his expected gain is 1/2 (the probability that bidder 2 has a low signal) times −B2 (since in that case he wins a worthless item and paysb2(high)), plus 1/2 (the probability that bidder 2 has a high signal) times 0 (since in that case he loses the item). The total expected gain is −B2/2 which is worse than 0, so it cannot be a best response.
  2. The other bidder bids B2 = B1. Then, his expected gain is 1/2 times −B2, plus 1/2 times 1/2 times [V− B2] (since in that case, he wins the item with probability 1/2). The total expected gain is (V − 3 B2)/4.
  3. The bidder b2 bids B2 > B1. Then, his expected gain is 1/2 times −B2, plus 1/2 times [V− B2] (since in that case, he wins the item with probability 1). The total expected gain is (2 V − 4 B2)/4.

The latter expression is positive only when B2 < V/2. But in that case, the expression in #3 is larger than the expression in #2: it is always better to bid slightly more than the other bidder. This means that there is no symmetric equilibrium.

This result is in contrast to the private-value case, where there is always a SBNE (seefirst-price sealed-bid auction).

Independent signals, second-price auction

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The following example is based on.[3]: 47–50 

There are two bidders participating in asecond-price sealed-bid auction for an object. Each bidderi{\displaystyle i} receives signalsi{\displaystyle s_{i}}; the signals are independent and havecontinuous uniform distribution on [0,1]. The valuations are:

vi=asi+bsi{\displaystyle v_{i}=a\cdot s_{i}+b\cdot s_{-i}}

wherea,b{\displaystyle a,b} are constants (a=1,b=0{\displaystyle a=1,b=0} means private values;a=b{\displaystyle a=b} means common values).

Here, there is a unique SBNE in which each player bids:

b(si)=(a+b)si{\displaystyle b(s_{i})=(a+b)\cdot s_{i}}

This result is in contrast to the private-value case, where in SBNE each player truthfully bids her value (seesecond-price sealed-bid auction).

Dependent signals, second-price auction

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This example is suggested[4]: 188–190  as an explanation tojump bidding inEnglish auctions.

Two bidders, Xenia and Yakov, participate in an auction for a single item. The valuations depend on A B and C -- three independent random variables drawn from acontinuous uniform distribution on the interval [0,36]:

Below we consider several auction formats and find a SBNE in each of them. For simplicity we look for SBNE in which each bidder bidsr{\displaystyle r} times his/her signal: Xenia bidsrX{\displaystyle r\cdot X} and Yakov bidsrY{\displaystyle r\cdot Y}. We try to find the value ofr{\displaystyle r} in each case.

In asealed-bidsecond-price auction, there is a SBNE withr=1{\displaystyle r=1}, i.e., each bidder bids exactly his/her signal.

PROOF: The proof takes the point-of-view of Xenia. We assume that she knows that Yakov bidsrY{\displaystyle rY}, but she does not knowY{\displaystyle Y}. We find the best response of Xenia to Yakov's strategy. Suppose Xenia bidsZ{\displaystyle Z}. There are two cases:

All in all, Xenia's expected gain (given her signal X) is:

Y=0Z/rX+Y2rY2f(Y|X)dY{\displaystyle \int _{Y=0}^{Z/r}{X+Y-2rY \over 2}\cdot f(Y|X)dY}

wheref(Y|X){\displaystyle f(Y|X)} is the conditional probability-density of Y given X.

By theFundamental theorem of calculus, the derivative of this expression as a function of Z is just1rX+Z/r2Z2f(Z/r|X){\displaystyle {1 \over r}{X+Z/r-2Z \over 2}\cdot f(Z/r|X)}. This is zero whenX=2ZZ/r{\displaystyle X=2Z-Z/r}. So, the best response of Xenia is to bidZ=rX2r1{\displaystyle Z={rX \over 2r-1}}.

In a symmetric BNE, Xenia bidsZ=rX{\displaystyle Z=rX}. Comparing the latter two expressions implies thatr=1{\displaystyle r=1}.

The expected auctioneer's revenue is:

=E[min(X,Y)]=E[B+min(A,C)]{\displaystyle =E[\min(X,Y)]=E[B+\min(A,C)]}
=E[B]+E[min(A,C)]{\displaystyle =E[B]+E[\min(A,C)]}
=18+12=30{\displaystyle =18+12=30}

In aJapanese auction, the outcome is the same as in the second-price auction,[4] since information is revealed only when one of the bidders exits, but in this case the auction is over. So each bidder exits at his observation.

Dependent signals, first-price auction

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In the above example, in afirst-price sealed-bid auction, there is a SBNE withr=2/3{\displaystyle r=2/3}, i.e., each bidder bids 2/3 of his/her signal.

PROOF: The proof takes the point-of-view of Xenia. We assume that she knows that Yakov bidsrY{\displaystyle rY}, but does not knowY{\displaystyle Y}. We find the best response of Xenia to Yakov's strategy. Suppose Xenia bidsZ{\displaystyle Z}. There are two cases:

All in all, Xenia's expected gain (given her signal X and her bid Z) is:

G(X,Z)=Y=0Z/rX+Y2Z2f(Y|X)dY{\displaystyle G(X,Z)=\int _{Y=0}^{Z/r}{X+Y-2Z \over 2}\cdot f(Y|X)dY}

wheref(Y|X){\displaystyle f(Y|X)} is the conditional probability-density of Y given X.

SinceY=X+CA{\displaystyle Y=X+C-A}, the conditional probability-density of Y is:

Substituting this into the above formula gives that the gain of Xenia is:

G(X,Z)=1r3(XZ2r/2+Z3/3Z3r){\displaystyle G(X,Z)={1 \over r^{3}}(XZ^{2}r/2+Z^{3}/3-Z^{3}r)}

This has a maximum whenZ=rX3r1{\displaystyle Z={rX \over 3r-1}}. But, since we want a symmetric BNE, we also want to haveZ=rX{\displaystyle Z=rX}. These two equalities together imply thatr=2/3{\displaystyle r=2/3}.

The expected auctioneer's revenue is:

=E[max(fX,fY)]=(2/3)E[B+max(A,C)]{\displaystyle =E[\max(fX,fY)]=(2/3)E[B+\max(A,C)]}
=(2/3)(E[B]+E[max(A,C)]){\displaystyle =(2/3)(E[B]+E[\max(A,C)])}
=(2/3)(18+24)=28{\displaystyle =(2/3)(18+24)=28}

Note that here, therevenue equivalence principle does NOT hold—the auctioneer's revenue is lower in a first-price auction than in a second-price auction (revenue-equivalence holds only when the values are independent).

Relationship to Bertrand competition

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Common-value auctions are comparable toBertrand competition. Here, the firms are the bidders and the consumer is the auctioneer. Firms "bid" prices up to but not exceeding the true value of the item. Competition among firms should drive out profit. The number of firms will influence the success or otherwise of the auction process in driving price towards true value. If the number of firms is small, collusion may be possible. SeeMonopoly,Oligopoly.

References

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  1. ^Athey, Susan;Segal, Ilya (2013)."An Efficient Dynamic Mechanism"(PDF).Econometrica.81 (6):2463–2485.CiteSeerX 10.1.1.79.7416.doi:10.3982/ECTA6995.
  2. ^Dirk Bergemann &Stephen Morris (2013)."Robust Predictions in Games with Incomplete Information"(PDF).Econometrica.81 (4):1251–1308.CiteSeerX 10.1.1.299.4285.doi:10.3982/ecta11105. Archived fromthe original(PDF) on 2015-02-18.
  3. ^abDaron Acemoglu & Asu Ozdaglar (2009)."Networks Lectures 19-21: Incomplete Information: Bayesian Nash Equilibria, Auctions and Introduction to Social Learning". MIT. Archived fromthe original on 22 October 2016. Retrieved8 October 2016.
  4. ^abAvery, Christopher (1998). "Strategic Jump Bidding in English Auctions".Review of Economic Studies.65 (2):185–210.CiteSeerX 10.1.1.1002.310.doi:10.1111/1467-937x.00041.
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