Common value auctions The same value for everyone

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