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What methodology do the authors use and why? Are there any implications of the finding for policy makers or business leaders?

What methodology do the authors use and why? Are there any implications of the finding for policy makers or business leaders?

ENGR.ECONOMIC ANALYSIS
ENGR.ECONOMIC ANALYSIS
14th Edition
ISBN:9780190931919
Author:NEWNAN
Publisher:NEWNAN
Chapter1: Making Economics Decisions
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Explain with clear brief points with correct answer - please do not attempt to answer if you are not confident please do not waste my chance to ask questions as they are not for free and please do not copy. What methodology do the authors use and why? Are there any implications of the finding for policy makers or business leaders?
វ៉ Co (0 5 3 2 O x LoserRegret auctions o NoFeedback auctions IM 0 .1 000 2 BO 3 4 .5 ratio .6 A. Ratan, Y. Wen/Economics Letters 143 (2016) 114-117 .7 8 .9 1 Fig. 1. Empirical distribution of the bid/value ratio by type of auction. (Salo and Weber, 1995)-are not relevant for bidding in our design.² Our results do not suggest any significant differences based on an- ticipated regret across treatments.³ 2. The experiment We use FP auctions in which human participants bid against pre-programmed computers; this allows that objective probabili- ties of winning conditional on bids can be derived. The experiment was run at the Monash Laboratory for Experimental Economics at Monash University. Students in undergraduate and master's level courses in various disciplines participated in the experiment. Each participant was randomly assigned to a treatment (see Appendix A for instructions). 2.1. LoserRegret auctions The sequence of events in a session corresponding to LoserRe- gret auctions were as follows: 1. Initial instructions described the showup fee (7 AUD) and the rate at which experimental currency-ECU was converted to money (AUD). The following instructions were communicated: (a) there will be 10 rounds in a session. (b) In each round, participants would be bidding in a FP auction against three pre-programmed computer bidders: each computer opponent would submit a bid by drawing randomly and independently from the set (0.75, 1.5, 2.25,...,75), and each number had an equal chance of being drawn. Participants could be as- signed values drawn from the set of integers: {1, 2, 3, ..., 100). (c) Participants could bid discrete integers up to their values and could not see the bids submitted by rival bidders at the time of bidding, (d) the highest bidder would win the auction and pay 2 Our study has developed contemporaneously with Katuščák et al. (2015) in which treatment conditions similar to ours, have been studied. 3 Thus, we are equating feedback with regret as in FO. Although recent papers have attempted to generalize the regret theory (e.g. Saran and Serrano, 2014), the theory as applied to auctions (FO, EWK) does not specify the circumstances under which regret may or may not be anticipated. 4 These bids correspond to 75% of the values that could have been drawn for computer bidders from the set (1, 2, 3, ..., 100) in each auction using alternative procedures. Thus, in our design computer bidders submit risk-neutral Nash bids without subjects being explicitly instructed about the correspondence between computer bids and value. Thus, we are able to circumvent the "anchoring" confound which may have influenced bidding if a bidding rule which described computer bids as a fraction of their values was used. 115 a price equal to her bid. Ties would be resolved in favor of the human bidder: if the bid submitted by the human agent was among the highest bids, then he would be the winner, (e) after bids were submitted for 10 rounds, auction outcomes would be revealed such that participants would get to know their earn- ings based on auction outcomes and one of the 10 rounds would be selected randomly for final payment. 2. A short quiz was administered to evaluate participants' under- standing of the instructions and participants practiced bidding in three unpaid rounds. 3. In the bidding rounds that had payoff consequences a set of values was created by pre-selecting 10 ECU values from the set (1, 2... 100). The set of 10 values selected was (31, 37, 43, 49, 55, 61, 67, 73, 79, 85). This set was fixed for each partici- pant. Values were assigned to participants (in each round) by drawing randomly without replacement from this set. 2.2. Treatment differences The sequence of events for NoFeedback auctions was similar to those above, except for the following design differences. The bidders were instructed that after bids have been submitted for all rounds the computer will display whether they won the auction or not, their earnings based on auction outcomes, and a. LoserRegret auctions: the highest (winning) bid for that auction. b. NoFeedback auctions: any other information regarding the bids of the other bidders will not be shown.5 Table 1 summarizes treatment differences. 3. Results A total of 40 and 38 participants were assigned to LoserRegret and NoFeedback auctions, respectively. The actual mean payoff for a participant inclusive of the participation fee was about AUD 23. The summary statistics for bids at various value draws arereported in Table 2. Treatment differences First, we explore treatment differences at various value draws. As described in Table 2, although the means of the bids are slightly larger at most value draws in LoserRegret auctions, the p values for the Wilcoxon rank-sum test and the Kolmogorov-Smirnov test for equality of distribution of bids, suggest that the hypothesis of equality of distribution of bids cannot be rejected at any value draw except for ECU value = 67 at 10% level. Second, we calculate the bid-value ratio for each bid-value pair. In Fig. 1, the cumulative distribution functions (CDFs) of the bid-value ratios for LoserRegret the vide Co auctions and NoFeedback auctions are plotted. The CDF of the bid-value ratio for LoserRegret lies below the corresponding CDF for NoFeedback auctions for bid-value ratio less than 0.8; however for bid-value ratio larger than 0.8, the CDFs for these treatments tend to overlap. This figure indicates slightly more aggressive bidding in LoserRegret auctions. We further explore treatment differences by estimating the following: yir = a + BR+ ni.r (1) where i = 1, 2, ..., N; r = 1, 2,..., 10, and R = 1 for LoserRegret auctions, 0 otherwise and nir are errors. The dependent variable yir equals the bid or the bid-value ratio in various specifications. If yir = bidi,r value-fixed effects were added. If yir= bidir (Vi.r = 5 These differences are consistent with the feedback (treatment) conditions reported in FO.
Transcribed Image Text:វ៉ Co (0 5 3 2 O x LoserRegret auctions o NoFeedback auctions IM 0 .1 000 2 BO 3 4 .5 ratio .6 A. Ratan, Y. Wen/Economics Letters 143 (2016) 114-117 .7 8 .9 1 Fig. 1. Empirical distribution of the bid/value ratio by type of auction. (Salo and Weber, 1995)-are not relevant for bidding in our design.² Our results do not suggest any significant differences based on an- ticipated regret across treatments.³ 2. The experiment We use FP auctions in which human participants bid against pre-programmed computers; this allows that objective probabili- ties of winning conditional on bids can be derived. The experiment was run at the Monash Laboratory for Experimental Economics at Monash University. Students in undergraduate and master's level courses in various disciplines participated in the experiment. Each participant was randomly assigned to a treatment (see Appendix A for instructions). 2.1. LoserRegret auctions The sequence of events in a session corresponding to LoserRe- gret auctions were as follows: 1. Initial instructions described the showup fee (7 AUD) and the rate at which experimental currency-ECU was converted to money (AUD). The following instructions were communicated: (a) there will be 10 rounds in a session. (b) In each round, participants would be bidding in a FP auction against three pre-programmed computer bidders: each computer opponent would submit a bid by drawing randomly and independently from the set (0.75, 1.5, 2.25,...,75), and each number had an equal chance of being drawn. Participants could be as- signed values drawn from the set of integers: {1, 2, 3, ..., 100). (c) Participants could bid discrete integers up to their values and could not see the bids submitted by rival bidders at the time of bidding, (d) the highest bidder would win the auction and pay 2 Our study has developed contemporaneously with Katuščák et al. (2015) in which treatment conditions similar to ours, have been studied. 3 Thus, we are equating feedback with regret as in FO. Although recent papers have attempted to generalize the regret theory (e.g. Saran and Serrano, 2014), the theory as applied to auctions (FO, EWK) does not specify the circumstances under which regret may or may not be anticipated. 4 These bids correspond to 75% of the values that could have been drawn for computer bidders from the set (1, 2, 3, ..., 100) in each auction using alternative procedures. Thus, in our design computer bidders submit risk-neutral Nash bids without subjects being explicitly instructed about the correspondence between computer bids and value. Thus, we are able to circumvent the "anchoring" confound which may have influenced bidding if a bidding rule which described computer bids as a fraction of their values was used. 115 a price equal to her bid. Ties would be resolved in favor of the human bidder: if the bid submitted by the human agent was among the highest bids, then he would be the winner, (e) after bids were submitted for 10 rounds, auction outcomes would be revealed such that participants would get to know their earn- ings based on auction outcomes and one of the 10 rounds would be selected randomly for final payment. 2. A short quiz was administered to evaluate participants' under- standing of the instructions and participants practiced bidding in three unpaid rounds. 3. In the bidding rounds that had payoff consequences a set of values was created by pre-selecting 10 ECU values from the set (1, 2... 100). The set of 10 values selected was (31, 37, 43, 49, 55, 61, 67, 73, 79, 85). This set was fixed for each partici- pant. Values were assigned to participants (in each round) by drawing randomly without replacement from this set. 2.2. Treatment differences The sequence of events for NoFeedback auctions was similar to those above, except for the following design differences. The bidders were instructed that after bids have been submitted for all rounds the computer will display whether they won the auction or not, their earnings based on auction outcomes, and a. LoserRegret auctions: the highest (winning) bid for that auction. b. NoFeedback auctions: any other information regarding the bids of the other bidders will not be shown.5 Table 1 summarizes treatment differences. 3. Results A total of 40 and 38 participants were assigned to LoserRegret and NoFeedback auctions, respectively. The actual mean payoff for a participant inclusive of the participation fee was about AUD 23. The summary statistics for bids at various value draws arereported in Table 2. Treatment differences First, we explore treatment differences at various value draws. As described in Table 2, although the means of the bids are slightly larger at most value draws in LoserRegret auctions, the p values for the Wilcoxon rank-sum test and the Kolmogorov-Smirnov test for equality of distribution of bids, suggest that the hypothesis of equality of distribution of bids cannot be rejected at any value draw except for ECU value = 67 at 10% level. Second, we calculate the bid-value ratio for each bid-value pair. In Fig. 1, the cumulative distribution functions (CDFs) of the bid-value ratios for LoserRegret the vide Co auctions and NoFeedback auctions are plotted. The CDF of the bid-value ratio for LoserRegret lies below the corresponding CDF for NoFeedback auctions for bid-value ratio less than 0.8; however for bid-value ratio larger than 0.8, the CDFs for these treatments tend to overlap. This figure indicates slightly more aggressive bidding in LoserRegret auctions. We further explore treatment differences by estimating the following: yir = a + BR+ ni.r (1) where i = 1, 2, ..., N; r = 1, 2,..., 10, and R = 1 for LoserRegret auctions, 0 otherwise and nir are errors. The dependent variable yir equals the bid or the bid-value ratio in various specifications. If yir = bidi,r value-fixed effects were added. If yir= bidir (Vi.r = 5 These differences are consistent with the feedback (treatment) conditions reported in FO.
116 Table 1 Design features. Loser Regret auctions Participant gets to know her earnings from each auction Winner's bid and value revealed at the end of the session - 400 40 participants 10 rounds Notes: (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. R Post auction information Male Constant No. of bids Table 2 Summary of bids across treatments. Value draw LoserRegret auctions ** p < 0.05. *** p <0.01. 31 37 43 49 55 Table 3 Testing for differences: Linear regression. 61 67 73 79 85 Notes: *p<0.10. **p<0.05. *** p<0.01. (1) Bid 4. Conclusion 2.03 (1.63) Mean 25.5 30.63 35.85 42.33 46.68 55.13 60.68 65.05 71.3 76.5 24.41*** (1.22) 780 0.749 (2) Bid 1.90 (1.58) -1.61 Std. error 6.93 9.64 9.61 8.33 12.39 5.35 6.05 9.73 7.35 8 (1.63) 25.22*** (1.16) 780 0.750 A. Ratan, Y. Wen/Economics Letters 143 (2016) 114-117 NoFeedback auctions Mean 25.4 30.79 36.16 40 46.22 51.35 55.9 61.24 68.64 73.72 (3) Ratio 0.03 (0.03) 0.81*** (0.03) 780 0.011 Std. error 5.74 6.89 7.37 9.05 9.51 10.82 11.72 12.73 11.48 11.58 (4) Ratio Observations Adjusted R2 Notes: (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 for the values 37, 43, 49, 55, 61, 67, 73, 79, and 85 (except value = 31); the coefficients for these dummies are positive and highly significant (p < 0.01); and (iv) standard errors clustered at the individual level are reported in parentheses. *p <0.10. 0.03 (0.03) -0.03 (0.03) 0.82*** (0.03) 780 0.020 ratior) the coefficient ß captures systematic differences in the means of bids (bid-value ratio) across treatments. The results are presented in Table 3. The estimate of the coefficient is positive and insignificant (specification (1), and (3)). The results of specifications that include gender-fixed effects are also reported (specification (2) and (4)). The estimate of B, however, remains insignificant. Overall, the results in Table 3 do not suggest significant treatment differences. Thus, we arrive at the following: Result. No differences in bidding are observed in LoserRegret auctions with respect to NoFeedback auctions. We tested whether differences in how auction outcomes are revealed (whether the winning bid is communicated or not) influences bidding in FP auctions against pre-programmed computerized bidders. This was previously explored in auctions 0.748 0.46 0.821 0.301 0.38 Wilcoxon rank-sum (Mann-Whitney) test (p value) 0.243 0.095* NoFeedback auctions - Participant gets to know her earnings from each auction 0.201 0.386 0.31 -380 38 participants 10 rounds Kolmogorov-Smirnov test (p value) 0.99 0.49 0.741 0.26 0.355 0.624 0.347 0.48 0.882 0.309 against human bidders. Our results do not suggest any significant differences in behavior across treatments. Thus, our results. are different from those which suggest that loser regret driven considerations might induce aggressive bidding (FO; EWK). Our results are consistent with the results reported in Katuščák CL et al. (2015) for treatment conditions similar to ours: these are CHC treatment differences based on protocol HC in which one human bids against one computer. In addition, they also report similar qualitative findings in auctions with two and four human bidders. This suggests that overbidding in FP auctions cannot be explained by loser regret driven considerations and alternative explanations could be more relevant for explaining overbidding in FP auctions against human bidders. Acknowledgment The research was funded by faculty research grant of Monash University. Appendix A. Supplementary data Supplementary material related to this article can be found. online at http://dx.doi.org/10.1016/j.econlet.2016.03.021. References Crawford, V.P., Iriberri, N., 2007. Level-Kappa auctions: Can a nonequilibrium model of 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 revelation of winning bids, experiments that suggest support for anticipated-regret are, in fact, testing for saliency effects that may trigger regret in auctions (as correctly discussed in EWK). This calls for a careful interpretation of previous evidence. 7 Our sample size (based on 78 participants) is slightly larger than the sample for the corresponding treatments reported in FO (based on 64 participants).
Transcribed Image Text:116 Table 1 Design features. Loser Regret auctions Participant gets to know her earnings from each auction Winner's bid and value revealed at the end of the session - 400 40 participants 10 rounds Notes: (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. R Post auction information Male Constant No. of bids Table 2 Summary of bids across treatments. Value draw LoserRegret auctions ** p < 0.05. *** p <0.01. 31 37 43 49 55 Table 3 Testing for differences: Linear regression. 61 67 73 79 85 Notes: *p<0.10. **p<0.05. *** p<0.01. (1) Bid 4. Conclusion 2.03 (1.63) Mean 25.5 30.63 35.85 42.33 46.68 55.13 60.68 65.05 71.3 76.5 24.41*** (1.22) 780 0.749 (2) Bid 1.90 (1.58) -1.61 Std. error 6.93 9.64 9.61 8.33 12.39 5.35 6.05 9.73 7.35 8 (1.63) 25.22*** (1.16) 780 0.750 A. Ratan, Y. Wen/Economics Letters 143 (2016) 114-117 NoFeedback auctions Mean 25.4 30.79 36.16 40 46.22 51.35 55.9 61.24 68.64 73.72 (3) Ratio 0.03 (0.03) 0.81*** (0.03) 780 0.011 Std. error 5.74 6.89 7.37 9.05 9.51 10.82 11.72 12.73 11.48 11.58 (4) Ratio Observations Adjusted R2 Notes: (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 for the values 37, 43, 49, 55, 61, 67, 73, 79, and 85 (except value = 31); the coefficients for these dummies are positive and highly significant (p < 0.01); and (iv) standard errors clustered at the individual level are reported in parentheses. *p <0.10. 0.03 (0.03) -0.03 (0.03) 0.82*** (0.03) 780 0.020 ratior) the coefficient ß captures systematic differences in the means of bids (bid-value ratio) across treatments. The results are presented in Table 3. The estimate of the coefficient is positive and insignificant (specification (1), and (3)). The results of specifications that include gender-fixed effects are also reported (specification (2) and (4)). The estimate of B, however, remains insignificant. Overall, the results in Table 3 do not suggest significant treatment differences. Thus, we arrive at the following: Result. No differences in bidding are observed in LoserRegret auctions with respect to NoFeedback auctions. We tested whether differences in how auction outcomes are revealed (whether the winning bid is communicated or not) influences bidding in FP auctions against pre-programmed computerized bidders. This was previously explored in auctions 0.748 0.46 0.821 0.301 0.38 Wilcoxon rank-sum (Mann-Whitney) test (p value) 0.243 0.095* NoFeedback auctions - Participant gets to know her earnings from each auction 0.201 0.386 0.31 -380 38 participants 10 rounds Kolmogorov-Smirnov test (p value) 0.99 0.49 0.741 0.26 0.355 0.624 0.347 0.48 0.882 0.309 against human bidders. Our results do not suggest any significant differences in behavior across treatments. Thus, our results. are different from those which suggest that loser regret driven considerations might induce aggressive bidding (FO; EWK). Our results are consistent with the results reported in Katuščák CL et al. (2015) for treatment conditions similar to ours: these are CHC treatment differences based on protocol HC in which one human bids against one computer. In addition, they also report similar qualitative findings in auctions with two and four human bidders. This suggests that overbidding in FP auctions cannot be explained by loser regret driven considerations and alternative explanations could be more relevant for explaining overbidding in FP auctions against human bidders. Acknowledgment The research was funded by faculty research grant of Monash University. Appendix A. Supplementary data Supplementary material related to this article can be found. online at http://dx.doi.org/10.1016/j.econlet.2016.03.021. References Crawford, V.P., Iriberri, N., 2007. Level-Kappa auctions: Can a nonequilibrium model of 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 revelation of winning bids, experiments that suggest support for anticipated-regret are, in fact, testing for saliency effects that may trigger regret in auctions (as correctly discussed in EWK). This calls for a careful interpretation of previous evidence. 7 Our sample size (based on 78 participants) is slightly larger than the sample for the corresponding treatments reported in FO (based on 64 participants).
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