Please briefly explain the results with explanation and clear points. Question - List the main results ?

ENGR.ECONOMIC ANALYSIS
14th Edition
ISBN:9780190931919
Author:NEWNAN
Publisher:NEWNAN
Chapter1: Making Economics Decisions
Section: Chapter Questions
Problem 1QTC
icon
Related questions
Question
Please briefly explain the results with explanation and clear points. Question - List the main results ?
ELSEVIER
HIGHLIGHTS
Does regret matter in first-price auctions?
Anmol Ratan, Yuanji Wen b.1
*Department of Economics, Monash University, Australia
Accounting and Finance, UWA Business School, University of Western Australia, Australia
ARTICLE INFO
Article history:
Received 18 December 2015
Received in revised form
11 March 2016
Accepted 25 March 2016
Available 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.
C9
D4
D8
Keywords:
Overbidding
Economics Letters 143 (2016) 114-117
Auction
Anticipated regret
Ambiguity
Laboratory experiments
Contents lists available at ScienceDirect
1. Introduction
Economics Letters
journal homepage: www.elsevier.com/locate/ecolet
http://dx.doi.org/10.1016/j.econlet.2016.03.021
0165-1765/ 2016 Elsevier B.V. All rights reserved.
ABSTRACT
Numerous experiments report bidding in excess of risk-neutral-
Nash predictions (henceforth overbidding) in first-price (hence-
forth FP) auctions (Kagel, 1995). Besides risk aversion, alternative
explanations for overbidding have been offered including antici-
pated regret (Filiz-Ozbay and Ozbay, 2007, henceforth FO). Some
of 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.
economics
letters
"Overbidding" with respect to risk-neutral Nash predictions in first-price auction experiments has been
consistently reported in the literature. One possible explanation for overbidding is that participants in
these experiments may try to avoid regret induced by the knowledge of winning bids in case they do
not 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 differences
in bidding. In our design, where individuals bid against pre-programmed computers, differences in
revelation of winning bids, does not produce significant treatment differences. Our results are in contrast
to previous experiments, which report systematic treatment differences based on whether winning bids
are revealed or not.
© 2016 Elsevier B.V. All rights reserved.
CrossMark
auctions against human bidders (games), whereas explanations
such as anticipated regret are relevant for both games and single-
agent decision problems. Previous experiments have tested the
effects 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 supporting
anticipated regret in auctions. In this paper, we test the predic-
tions based on anticipated regret in single-agent decision problems
which provides a cleaner environment for testing regret effects,
since explanations based on interpersonal comparisons-"level-
k" thinking, spitefulness, joy of winning or ambiguity aversion
Transcribed Image Text:ELSEVIER HIGHLIGHTS Does regret matter in first-price auctions? Anmol Ratan, Yuanji Wen b.1 *Department of Economics, Monash University, Australia Accounting and Finance, UWA Business School, University of Western Australia, Australia ARTICLE INFO Article history: Received 18 December 2015 Received in revised form 11 March 2016 Accepted 25 March 2016 Available 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. C9 D4 D8 Keywords: Overbidding Economics Letters 143 (2016) 114-117 Auction Anticipated regret Ambiguity Laboratory experiments Contents lists available at ScienceDirect 1. Introduction Economics Letters journal homepage: www.elsevier.com/locate/ecolet http://dx.doi.org/10.1016/j.econlet.2016.03.021 0165-1765/ 2016 Elsevier B.V. All rights reserved. ABSTRACT Numerous experiments report bidding in excess of risk-neutral- Nash predictions (henceforth overbidding) in first-price (hence- forth FP) auctions (Kagel, 1995). Besides risk aversion, alternative explanations for overbidding have been offered including antici- pated regret (Filiz-Ozbay and Ozbay, 2007, henceforth FO). Some of 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. economics letters "Overbidding" with respect to risk-neutral Nash predictions in first-price auction experiments has been consistently reported in the literature. One possible explanation for overbidding is that participants in these experiments may try to avoid regret induced by the knowledge of winning bids in case they do not 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 differences in bidding. In our design, where individuals bid against pre-programmed computers, differences in revelation of winning bids, does not produce significant treatment differences. Our results are in contrast to previous experiments, which report systematic treatment differences based on whether winning bids are revealed or not. © 2016 Elsevier B.V. All rights reserved. CrossMark auctions against human bidders (games), whereas explanations such as anticipated regret are relevant for both games and single- agent decision problems. Previous experiments have tested the effects 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 supporting anticipated regret in auctions. In this paper, we test the predic- tions based on anticipated regret in single-agent decision problems which provides a cleaner environment for testing regret effects, since explanations based on interpersonal comparisons-"level- k" thinking, spitefulness, joy of winning or ambiguity aversion
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).
Expert Solution
steps

Step by step

Solved in 3 steps

Blurred answer
Knowledge Booster
Inflation and Unemployment
Learn more about
Need a deep-dive on the concept behind this application? Look no further. Learn more about this topic, economics and related others by exploring similar questions and additional content below.
Recommended textbooks for you
ENGR.ECONOMIC ANALYSIS
ENGR.ECONOMIC ANALYSIS
Economics
ISBN:
9780190931919
Author:
NEWNAN
Publisher:
Oxford University Press
Principles of Economics (12th Edition)
Principles of Economics (12th Edition)
Economics
ISBN:
9780134078779
Author:
Karl E. Case, Ray C. Fair, Sharon E. Oster
Publisher:
PEARSON
Engineering Economy (17th Edition)
Engineering Economy (17th Edition)
Economics
ISBN:
9780134870069
Author:
William G. Sullivan, Elin M. Wicks, C. Patrick Koelling
Publisher:
PEARSON
Principles of Economics (MindTap Course List)
Principles of Economics (MindTap Course List)
Economics
ISBN:
9781305585126
Author:
N. Gregory Mankiw
Publisher:
Cengage Learning
Managerial Economics: A Problem Solving Approach
Managerial Economics: A Problem Solving Approach
Economics
ISBN:
9781337106665
Author:
Luke M. Froeb, Brian T. McCann, Michael R. Ward, Mike Shor
Publisher:
Cengage Learning
Managerial Economics & Business Strategy (Mcgraw-…
Managerial Economics & Business Strategy (Mcgraw-…
Economics
ISBN:
9781259290619
Author:
Michael Baye, Jeff Prince
Publisher:
McGraw-Hill Education