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Unpacking buyer-seller differences in valuation from experience: A cognitive modeling approach

Overview of attention for article published in Psychonomic Bulletin & Review, March 2017
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Title
Unpacking buyer-seller differences in valuation from experience: A cognitive modeling approach
Published in
Psychonomic Bulletin & Review, March 2017
DOI 10.3758/s13423-017-1237-4
Pubmed ID
Authors

Thorsten Pachur, Benjamin Scheibehenne

Abstract

People often indicate a higher price for an object when they own it (i.e., as sellers) than when they do not (i.e., as buyers)-a phenomenon known as the endowment effect. We develop a cognitive modeling approach to formalize, disentangle, and compare alternative psychological accounts (e.g., loss aversion, loss attention, strategic misrepresentation) of such buyer-seller differences in pricing decisions of monetary lotteries. To also be able to test possible buyer-seller differences in memory and learning, we study pricing decisions from experience, obtained with the sampling paradigm, where people learn about a lottery's payoff distribution from sequential sampling. We first formalize different accounts as models within three computational frameworks (reinforcement learning, instance-based learning theory, and cumulative prospect theory), and then fit the models to empirical selling and buying prices. In Study 1 (a reanalysis of published data with hypothetical decisions), models assuming buyer-seller differences in response bias (implementing a strategic-misrepresentation account) performed best; models assuming buyer-seller differences in choice sensitivity or memory (implementing a loss-attention account) generally fared worst. In a new experiment involving incentivized decisions (Study 2), models assuming buyer-seller differences in both outcome sensitivity (as proposed by a loss-aversion account) and response bias performed best. In both Study 1 and 2, the models implemented in cumulative prospect theory performed best. Model recovery studies validated our cognitive modeling approach, showing that the models can be distinguished rather well. In summary, our analysis supports a loss-aversion account of the endowment effect, but also reveals a substantial contribution of simple response bias.

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Geographical breakdown

Country Count As %
Unknown 47 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 9 19%
Researcher 6 13%
Student > Bachelor 5 11%
Student > Master 5 11%
Professor 4 9%
Other 8 17%
Unknown 10 21%
Readers by discipline Count As %
Psychology 14 30%
Business, Management and Accounting 5 11%
Economics, Econometrics and Finance 3 6%
Neuroscience 3 6%
Computer Science 2 4%
Other 6 13%
Unknown 14 30%