Jason Shachat

Prof. Jason Shachat


Research Focus & Metrics

Academic Career Length

29Years (1998-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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9 August 2021 in Journal of Economic Behavior & Organization
Verified
Journal Article
Closed access
JSJason Shachat
Jason Shachat
MWMatthew J. Walker
Matthew J. Walker
LWLijia Wei
Lijia Wei

1 May 2002 in Journal of Economic Theory
Verified
Journal Article
Closed access
JSJason Shachat
Jason Shachat

1 January 2003 in IBM Systems Journal
Verified
Journal Article
Closed access
HHH. Huang
H. Huang
CKC. Keser
C. Keser
JLJ. Leland
J. Leland
JSJason Shachat
Jason Shachat

1 December 2020 in Journal of Risk and Uncertainty
Verified
Journal Article
Open access
PDF file
THTimo Heinrich
Timo Heinrich
JSJason Shachat
Jason Shachat
This study experimentally evaluates the risk preferences of children and adolescents living in an urban Chinese environment. We use a simple binary choice task that tests risk aversion, as well as prudence. This is the first test for prudence in children and adolescents. Our results reveal that subjects from grades 5 to 11 (10 to 17 years) make mostly risk-averse and prudent choices. The choices of 3rd graders (8 to 9 years) do not differ statistically from risk neutral benchmarks, but at the same time they make mostly prudent choices. We also find evidence for a transmission of risk preferences. There is positive correlation between all children’s and their parents’ tendency to make risk-averse choices. There is also positive correlation between girls’ and their parents’ tendency to make prudent choices.

1 March 2012 in Marketing Science
Verified
Journal Article
Closed access
JSJason Shachat
Jason Shachat
LWLijia Wei
Lijia Wei
We use laboratory experiments to examine the relative performance of the English auction (EA) and the first-price sealed-bid auction (FPA) when procuring a commodity. The mean and variance of prices are lower in the FPA than in the EA. Bids and prices in the EA agree with game-theoretic predictions, but they do not agree in the FPA. To resolve these deviations found in the FPA, we introduce a mixture model with three bidding rules: constant absolute markup, constant percentage markup, and strategic best response. A dynamic specification in which bidders can switch strategies as they gain experience is estimated as a hidden Markov model. Initially, about three quarters of the subjects are strategic bidders, but over time, the number of strategic bidders falls to below 65%. There is a corresponding growth in those who use the constant absolute markup rule.

1 July 2004 in Mathematical Methods of Operations Research
Verified
Journal Article
Closed access
PDF file
JSJason Shachat
Jason Shachat
JSJ. Todd Swarthout
J. Todd Swarthout