Please briefly explain the results with explanation and clear points. Question - List the main results ? ELSEVIERHIGHLIGHTSDoes regret matter in first-price auctions?Anmol Ratan, Yuanji Wen b.1*Department of Economics, Monash University, AustraliaAccounting and Finance, UWA Business School, University of Western Australia, AustraliaARTICLE INFOArticle history:Received 18 December 2015Received in revised form11 March 2016Accepted 25 March 2016Available online 16 April 2016• We test for the predictions of anticipated regret in first-price auctions.• One human bids against three computers with pre-specified bidding strategies.JEL classification:C7• Subjects randomly assigned to one of the two treatments.• Treatment effects attributed to anticipated regret are not observed.C9D4D8Keywords:OverbiddingEconomics Letters 143 (2016) 114-117AuctionAnticipated regretAmbiguityLaboratory experimentsContents lists available at ScienceDirect1. IntroductionEconomics Lettersjournal homepage: www.elsevier.com/locate/ecolethttp://dx.doi.org/10.1016/j.econlet.2016.03.0210165-1765/ 2016 Elsevier B.V. All rights reserved.ABSTRACTNumerous experiments report bidding in excess of risk-neutral-Nash predictions (henceforth overbidding) in first-price (hence-forth FP) auctions (Kagel, 1995). Besides risk aversion, alternativeexplanations for overbidding have been offered including antici-pated regret (Filiz-Ozbay and Ozbay, 2007, henceforth FO). Someof these explanations, such as "level-k" decision-making (Craw-ford and Iriberri, 2007) and spiteful preferences, are relevant only* Correspondence to: Department of Economics, Monash Business School,Monash University, Clayton, VIC 3800, Australia. Tel.: +61 399020179.E-mail addresses: anmol.ratan@monash.edu (A. Ratan),yuanji.wen@uwa.edu.au (Y. Wen).1 Tel.: +61 864885856.economicsletters"Overbidding" with respect to risk-neutral Nash predictions in first-price auction experiments has beenconsistently reported in the literature. One possible explanation for overbidding is that participants inthese experiments may try to avoid regret induced by the knowledge of winning bids in case they donot win these auctions. Such considerations may drive bidders to bid aggressively in first-price auctions.We test whether differences in how auction outcomes are revealed produces systematic differencesin bidding. In our design, where individuals bid against pre-programmed computers, differences inrevelation of winning bids, does not produce significant treatment differences. Our results are in contrastto previous experiments, which report systematic treatment differences based on whether winning bidsare revealed or not.© 2016 Elsevier B.V. All rights reserved.CrossMarkauctions against human bidders (games), whereas explanationssuch as anticipated regret are relevant for both games and single-agent decision problems. Previous experiments have tested theeffects of anticipated regret in games. Given the effects of feed-back and repeated exposure in auction settings (Ockenfels and Sel-ten, 2005; Neugebauer and Selten, 2006), we believe that the ev-idence in FO which is based on one-shot environment, as com-pared to the evidence in Engelbrecht-Wiggans and Katok (2007,2008) (henceforth EWK) which is based on a repeated game de-sign, becomes the centerpiece of the existing evidence supportinganticipated regret in auctions. In this paper, we test the predic-tions based on anticipated regret in single-agent decision problemswhich provides a cleaner environment for testing regret effects,since explanations based on interpersonal comparisons-"level-k" thinking, spitefulness, joy of winning or ambiguity aversion 116Table 1Design features.Loser Regret auctionsParticipant gets to know her earnings from each auctionWinner's bid and value revealed at the end of the session- 40040 participants 10 roundsNotes: (i) The protocols and features are the same as in Ratan (2015) (ii) 1 ECU = 4.12 AUD; 1 AUD = 0.90 USD.1.2.RPost auction informationMaleConstantNo. of bidsTable 2Summary of bids across treatments.Value draw LoserRegret auctions** p < 0.05.*** p <0.01.3137434955Table 3Testing for differences: Linear regression.6167737985Notes:*p<0.10.**p<0.05.*** p<0.01.(1)Bid4. Conclusion2.03(1.63)Mean25.530.6335.8542.3346.6855.1360.6865.0571.376.524.41***(1.22)7800.749(2)Bid1.90(1.58)-1.61Std. error6.939.649.618.3312.395.356.059.737.358(1.63)25.22***(1.16)7800.750A. Ratan, Y. Wen/Economics Letters 143 (2016) 114-117NoFeedback auctionsMean25.430.7936.164046.2251.3555.961.2468.6473.72(3)Ratio0.03(0.03)0.81***(0.03)7800.011Std. error5.746.897.379.059.5110.8211.7212.7311.4811.58(4)RatioObservationsAdjusted R2Notes: (i) R is a dummy variable for treatment condition (1 for LoserRegret auctions,0 otherwise); (ii) Specifications 2 and 4 include a dummy variable for gender-Male(1 for male and 0 for female); (iii) Specifications 1 and 2 include dummy variables forthe values 37, 43, 49, 55, 61, 67, 73, 79, and 85 (except value = 31); the coefficientsfor these dummies are positive and highly significant (p < 0.01); and (iv) standarderrors clustered at the individual level are reported in parentheses.*p <0.10.0.03(0.03)-0.03(0.03)0.82***(0.03)7800.020ratior) the coefficient ß captures systematic differences in themeans of bids (bid-value ratio) across treatments. The resultsare presented in Table 3. The estimate of the coefficient ispositive and insignificant (specification (1), and (3)). The results ofspecifications that include gender-fixed effects are also reported(specification (2) and (4)). The estimate of B, however, remainsinsignificant.Overall, the results in Table 3 do not suggest significanttreatment differences. Thus, we arrive at the following:Result. No differences in bidding are observed in LoserRegretauctions with respect to NoFeedback auctions.We tested whether differences in how auction outcomesare revealed (whether the winning bid is communicated ornot) influences bidding in FP auctions against pre-programmedcomputerized bidders. This was previously explored in auctions0.7480.460.8210.3010.38Wilcoxon rank-sum(Mann-Whitney) test (p value)0.2430.095*NoFeedback auctions- Participant gets to know her earnings from each auction0.2010.3860.31-38038 participants 10 roundsKolmogorov-Smirnov test(p value)0.990.490.7410.260.3550.6240.3470.480.8820.309against human bidders. Our results do not suggest any significantdifferences in behavior across treatments. Thus, our results.are different from those which suggest that loser regret drivenconsiderations might induce aggressive bidding (FO; EWK). Ourresults are consistent with the results reported in KatuščákCLet al. (2015) for treatment conditions similar to ours: these areCHCtreatment differences based on protocol HC in which one humanbids against one computer. In addition, they also report similarqualitative findings in auctions with two and four human bidders.This suggests that overbidding in FP auctions cannot be explainedby loser regret driven considerations and alternative explanationscould be more relevant for explaining overbidding in FP auctionsagainst human bidders.AcknowledgmentThe research was funded by faculty research grant of MonashUniversity.Appendix A. Supplementary dataSupplementary material related to this article can be found.online at http://dx.doi.org/10.1016/j.econlet.2016.03.021.ReferencesCrawford, V.P., Iriberri, N., 2007. Level-Kappa auctions: Can a nonequilibrium modelof strategic thinking explain the winner's curse and overbidding in private-value auctions? Econometrica 75 (6), 1721-1770.Engelbrecht-Wiggans, R., Katok, E., 2007. Regret in auctions: Theory and evidence.Econom. Theory 33 (1), 81-101.6 By making the assumption that regret effects are sensitive to ex-post revelationof winning bids, experiments that suggest support for anticipated-regret are, in fact,testing for saliency effects that may trigger regret in auctions (as correctly discussedin EWK). This calls for a careful interpretation of previous evidence.7 Our sample size (based on 78 participants) is slightly larger than the sample forthe corresponding treatments reported in FO (based on 64 participants).

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