Excess information acquisition in auctions

Abstract

The acquisition of information is an important feature in most auctions where one’s exact private valuation is unknown ex-ante. We conducted the first experiment in testing a risk-neutral expected surplus maximization model with this feature. Varying the auction format and the cost of information acquisition we found bidders in most cases acquired too much information. Moreover, bidders who remained uninformed placed bids significantly below the optimal bid. The general prediction concerning revenue and efficiency remains valid, as a higher information cost was associated with lower revenues and efficiency rates. We explore different ex-post explanations for the observed behavior and show that regret avoidance can explain the data while risk aversion and ambiguity aversion cannot.

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Notes

  1. 1.

    Due diligence is costly, and increases the precision of buyers’ information about their valuation.

  2. 2.

    The theoretical works on auctions with information acquisition largely focus on the comparison of different auction formats in terms of information acquisition strategies, revenues, and efficiency. For auctions with interdependent valuations, see Matthews (1984), Hausch and Li (1993), Bergemann et al. (2009), Persico (2000), and Hernando-Veciana (2009). For auctions with independent private values, see Lee (1985), Guzman and Kolstad (1997), Gretschko and Wambach (2014), Shi (2012), Engelbrecht-Wiggans (1988), Parkes (2005), Rasmusen (2006), Rezende (2005), Compte and Jehiel (2000, 2007).

  3. 3.

    Compte and Jehiel (2000, 2007) compare second-price auctions to English auctions and allow for the bidders to observe the number of remaining competitors in the English auction. The equilibrium bidding and information acquisition strategies obtained by Compte and Jehiel (2000) are very intuitive and do not demand sophisticated reasoning. Essentially, the decisions of the uninformed bidders boil down to comparing two random variables. Hence, their setup is well-suited for a first experimental investigation of bidders’ behavior in auctions with information acquisition.

  4. 4.

    Anticipated regret in auctions was brought forward as a possible explanation for overbidding in first-price auctions by Engelbrecht-Wiggans (1989), Engelbrecht-Wiggans and Katok (2007, 2008) and Filiz-Ozbay and Ozbay (2007). Also see the references therein.

  5. 5.

    This is due to the fact that both auction formats are solvable in (weakly) dominant strategies.

  6. 6.

    Filiz-Ozbay and Ozbay (2007) and Engelbrecht-Wiggans and Katok (2007) show that the influence of regret on equilibrium bidding depends strongly on the feedback given to bidders. Our subjects learn their true valuations only if they acquire information or win the auction. In any other case, the valuation is not revealed to the subjects. This feedback procedure only enforces regret due to overpaying, but not regret due to losing the auction.

  7. 7.

    Let \(v^{(1)}\)denote the highest order statistic of \(N-1\) independent draws from \(F\).

  8. 8.

    Compte and Jehiel (2007) is a more general version of Compte and Jehiel (2000) in the sense that Compte and Jehiel (2007) allow for information acquisition by more than one bidder. As stated above, this comes at the cost of the information acquisition strategy not being explicitly derivable.

  9. 9.

    ECU is the Experimental Currency Unit. 10 ECU are equivalent to 1 Euro (10 ECU = 1 EUR).

  10. 10.

    Thus, we can rule out any time pressure effects shaping the decision to buy information or withdraw from bidding.

  11. 11.

    This is similar to other experiments on English auctions (Levin et al. 1996).

  12. 12.

    Whether information acquisition has a positive effect on revenues in general depends on the distribution of the valuations and the number of bidders.

  13. 13.

    We used the valuations that were actually drawn for the experiments (see Appendix A of Electronic Supplementary Material) to calculate the prediction in Table 2.

  14. 14.

    T-test: \(p\)-value \(<0.0001.\)

  15. 15.

    Mann–Whitney test: \(p\)-value = 0.0091.

  16. 16.

    T-test: \(p\)-value \(<0.0001\).

  17. 17.

    T-test: \(p\)-value \(<0.0001\).

  18. 18.

    Mann–Whitney test: \(p\)-value \(<0.0001\).

  19. 19.

    Mann–Whitney test for second-price auction \((c=2\hbox { vs. }c=8)\): \(p\)-value \( <0.0001\). Mann–Whitney test for English auction \((c=2\hbox { vs, }c=8)\): \(p\)-value \(<0.0001\).

  20. 20.

    This finding is consistent with the price clock data from the high-cost treatment, where the average clock price at information acquisition is 12 ECU.

  21. 21.

    The English auction ends once the second to last bidder has dropped out, so we are not able to observe the full bidding strategy of a winner. Hence, the estimated average bid in the English auction is merely the lower boundary of the actual average bid.

  22. 22.

    For second-price auction treatments Mann–Whitney test: \(p{-}{\rm value} = 0.047\), for English auction treatments Mann–Whitney test: \(p{-}{\rm value} = 0.090.\)

  23. 23.

    For low cost treatments Mann–Whitney test: \(p{-}{\rm value} = 0.88\), for high cost treatments Mann–Whitney test: \(p-{\rm value}= 0.88.\) As we have seen in Sect. 4.1, the frequencies of information acquisition are very similar in both formats. Moreover, in the English auction the subjects fail to wait before acquiring information. Thus, similar revenues were to be expected across formats.

  24. 24.

    Mann–Whitney test: \(p\)-value\(<0.001\) for both auctions.

  25. 25.

    Mann–Whitney test: \(p\)-value\(<0.01\).

  26. 26.

    We define that a bidder has switched the information acquisition strategy in round \(k\) if he had acquired information in Round \(k-1\) but not in \(k\), or if he had not acquired information in Round \(k-1\) but in Round \(k\).

  27. 27.

    In our case this means a right-censoring of 448 bids.

  28. 28.

    Those are the most prominent theories that are brought forward to explain overbidding in first-price auctions. Moreover, those theories also seem to fit our setting well due to the fact that the underlying distribution of the lottery that our subjects face is convoluted.

  29. 29.

    In the English auction, dropping out when the price reaches a bidder’s valuation is ex-post optimal for any realization of the other bidders’ valuations.

  30. 30.

    Anticipated regret in auctions was originally brought forward as an explanation for overbidding in first-price auctions (see, e.g., Filiz-Ozbay and Ozbay 2007, or Engelbrecht-Wiggans and Katok 2007).

  31. 31.

    A formal definition for the utility function of a regret-averse subject can be found in Appendix B of Electronic Supplementary Material.

  32. 32.

    We cannot use the data from the English auction for a precise estimation of the regret parameter. This is due to the fact that the estimated average bid in the English auction is merely the lower boundary of the actual bid. However, we can estimate the upper boundary of the regret coefficient which amounts to \(\alpha =1.97.\)

  33. 33.

    A more formal argument can be found in Appendix D of Electronic Supplementary Material.

  34. 34.

    See, e.g., Salo and Weber (1995).

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Acknowledgments

We would like to thank Jennifer Brown, Jacob Goeree, Axel Ockenfels, Alexander Rasch, and Achim Wambach for their helpful comments and discussion. Financial support from the German Research Foundation through the research unit “Design & Behavior” (FOR 1371) and the Fulbright Commission is gratefully acknowledged. Substantial parts of this paper were written when the first author stayed at Yale University in 2011/2012. The author thanks the department of economics for its hospitality. We would also like to thank our two anonymous referees and the associate editor for their detailed and helpful comments that have greatly improved this paper. All remaining errors are our own.

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Correspondence to Vitali Gretschko.

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Gretschko, V., Rajko, A. Excess information acquisition in auctions. Exp Econ 18, 335–355 (2015). https://doi.org/10.1007/s10683-014-9406-z

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Keywords

  • Dynamic auctions
  • Information acquisition
  • Bidding behavior

JEL classification

  • C91
  • D44
  • D80