Common value auctions

The same value for everyone, but different bidders have different

information about the underlying value

Auction a jar full of coins (asking for an estimation)

• average bid will be significantly less than the value of the coins (bidders are risk averse)

• winning bid exceeds the value of the jar

Bazerman & Samuelson, 1983, 48 auctions• True value $8• Estimated (mean) $5.13• Bias in estimation + risk aversion should work

against over bidding

• Yet mean winning $10.01

THE WINNER’S CURSE

• Question first raised by Capen, Clapp and Campbell 1971.

• This is an example of a problem that comes from a field observation before becoming theory.

• Winner's curse cannot occur among rational bidders (Cox and Isaac 1984).

• Challenge to assumption of rationality.

• But acting rationaly is difficult. Need to distinguish between:

• expected value of the object, conditioned on prior information

• expected value of the object, conditioned on winning the auction

• Example. You have to advise the takover of firm T.

• T knows the true value, you don't: Assymetry of information.

Optimal to bid ? Cero patatero

Extreme case of winner's curse.

• Experimental evidence (Bazerman & Samuelson 1985): Only 9% bid zero. Majority in [$50-$75].

• Would learning solve the anomaly? (Weiner, Bazerman & Carroll 1987).

• Each subject (MBA) repeated it 20 times with feedback about true value and whether their bid was accepted and profit. Of 69 subjects 5 learned to bid 1 or less by the end of the 20 rounds. Learning seem not to be easy or fast.

• Shell’s boss calls me about a bid

• Calls me again to tell me that the number of bidders has increased

• Should I increase or lower my previous bid?

• need to bid more aggressively to win

• if you win more likely that you have overestimated.

• Solving it not trivial. Do people get it right?

• Series of experiments by Kagel, Levin et al. True value x* varies from trial to trial but always between xl and xh.

• Prior is given to each one by drawing xi from uniform x*±∆.

• Increase in N -> more losses.

• Teatments: a) change of type of auction (first, second), N and ∆. Compare results with RNNE.

• This done also with construction firm managers (last price) Rules of thumb.

• Field data. Oil tracks, Corporate takeovers, Publishing auctions. At least prevalence of mild form of winner's curse: unfulfilled expectations.

• Why is this important? It is part of a general problem of after decision blues. Bidders are under a cognitive illusion that makes them incur in systematic errors.

• What strategy to follow once you have discovered the winner’s curse? Reduce your bids and sell short others’ shares?

• Why people succumb to it?

• “The value of victory”:

Humans assign significant future value to victories over humans but not over computer opponents, even though such victories may incur immediate losses