PMID- 31120871
OWN - NLM
STAT- MEDLINE
DCOM- 20200114
LR  - 20200114
IS  - 1932-6203 (Electronic)
IS  - 1932-6203 (Linking)
VI  - 14
IP  - 5
DP  - 2019
TI  - Learning and the possibility of losing own money reduce overbidding: Delayed 
      payment in experimental auctions.
PG  - e0213568
LID - 10.1371/journal.pone.0213568 [doi]
LID - e0213568
AB  - In this study, we designed a delayed payment mechanism in laboratory second price 
      auctions (SPAs), under which subjects received a cash endowment two weeks after 
      the experiment day and had to use their own money to pay the experimental losses 
      (if any) on the experiment day. We compared the effect of delayed payment on 
      overbidding in the induced value SPAs with the conventional "on-the-spot" payment 
      mechanism where the subjects received an endowment on the experiment day, and the 
      prepaid mechanism where the subjects received the endowment two weeks before the 
      experiment day. Each auction was repeated for 20 rounds to provide sufficient 
      learning opportunities to the bidders. Our results showed that bids converged to 
      the corresponding values over auction rounds and overbidding was reduced by 
      previous losses, consistently with the adaptive learning theory. Moreover, 
      overbidding seems to depend significantly on bidders' cash holding, and the 
      magnitude of the payment treatment effects depends crucially on liquidity 
      constraints. In the presence of liquidity constraints, both delayed and prepaid 
      payment mechanisms reduced overbidding, while in the absence of liquidity 
      constraints, only the delayed endowment mechanism reduced overbidding. 
      Furthermore, when controlling the degree of liquidity constraints, subjects with 
      higher GPAs were less likely to overbid and the delayed endowment mechanism 
      significantly reduced overbidding compared to other payment mechanisms. These 
      results suggest that overbidding in SPAs might be caused by bounded rationality 
      and could be reduced by adaptive learning especially when overbidding becomes 
      "truly" costly to subjects.
FAU - Zhang, Yu Yvette
AU  - Zhang YY
AUID- ORCID: 0000-0003-2708-338X
AD  - Department of Agricultural Economics, Texas A&M University, College Station, 
      Texas, United States of America.
FAU - Nayga, Rodolfo M Jr
AU  - Nayga RM Jr
AD  - Department of Agricultural Economics and Agribusiness, University of Arkansas, 
      Fayetteville, Arkansas, United States of America.
FAU - Depositario, Dinah Pura T
AU  - Depositario DPT
AD  - Department of Agribusiness Management and Entrepreneurship, College of Economics 
      and Management, University of the Philippines Los Banos, College, Laguna, 
      Philippines.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20190523
PL  - United States
TA  - PLoS One
JT  - PloS one
JID - 101285081
SB  - IM
MH  - Algorithms
MH  - *Commerce
MH  - Humans
MH  - *Learning
MH  - *Models, Theoretical
PMC - PMC6532848
COIS- The authors have declared that no competing interests exist.
EDAT- 2019/05/24 06:00
MHDA- 2020/01/15 06:00
PMCR- 2019/05/23
CRDT- 2019/05/24 06:00
PHST- 2017/04/21 00:00 [received]
PHST- 2019/02/25 00:00 [accepted]
PHST- 2019/05/24 06:00 [entrez]
PHST- 2019/05/24 06:00 [pubmed]
PHST- 2020/01/15 06:00 [medline]
PHST- 2019/05/23 00:00 [pmc-release]
AID - PONE-D-17-15585 [pii]
AID - 10.1371/journal.pone.0213568 [doi]
PST - epublish
SO  - PLoS One. 2019 May 23;14(5):e0213568. doi: 10.1371/journal.pone.0213568. 
      eCollection 2019.