1. Common value auctions The same value for everyone, but different bidders have different information about the underlying value

2. 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

3. THE WINNER’S CURSE

4. 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.

5. 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

6. 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.

7. Experimental evidence (Bazerman & Samuelson 1985): Only 9% bid zero. Majority in [$50-$75]. Would learning solve the anomaly? (Weiner, Bazerman & Carroll 1987).

8. 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.

9. 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?

10. 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?

11. Series of experiments by Kagel, Levin et al. True value x* varies from trial to trial but always between x l and x h. Prior is given to each one by drawing x i 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.

12. 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?

13. 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