PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
October 2, 2019Management Science177 citationsOpen Access

Searching for the Reference Point

ABAurélien BaillonHBHan BleichrodtVSVitalie Spinu

Key Points

  • Identify the specific reference points that individuals select when making decisions under risk within a unified, reference-dependent theoretical framework.
  • Conducted a high-stakes experimental choice study with payoffs reaching up to a week's salary using an optimal experimental design.
  • Implemented a comprehensive reference-dependent model nesting prospect theory and applied Bayesian hierarchical modeling for parameter estimation.
  • The status quo and the security level (the maximum of the minimal outcomes among choice prospects) were the most frequently selected reference points.
  • Data showed minimal empirical support for the adoption of expectations-based reference points.

Abstract

Although reference dependence plays a central role in explaining behavior, little is known about the way that reference points are selected. This paper identifies empirically which reference point people use in decision under risk. We assume a comprehensive reference-dependent model that nests the main reference-dependent theories, including prospect theory, and that allows for isolating the reference point rule from other behavioral parameters. Our experiment involved high stakes with payoffs up to a week’s salary. We used an optimal design to select the choices in the experiment and Bayesian hierarchical modeling for estimation. The most common reference points were the status quo and a security level (the maximum of the minimal outcomes of the prospects in a choice). We found little support for the use of expectations-based reference points. This paper was accepted by David Simchi-Levi, decision analysis.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Baillon et al. (2019) studied this question.

synapsesocial.com/papers/69d6ea9dfca0359822aa8c5ahttps://doi.org/10.1287/mnsc.2018.3224
Ask AI
Helpful
Bookmark
Share
View Full Paper