English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

People are often reluctant to sell a house, or shares of stock, below theprice at which they originally bought it. While this is generally notconsistent with rational utility maximization, it does reflect two strongempirical regularities that are central to the behavioral science of humandecision-making: a tendency to evaluate outcomes relative to a reference pointdetermined by context (in this case the original purchase price), and thephenomenon of loss aversion in which people are particularly prone to avoidoutcomes below the reference point. Here we explore the implications ofreference points and loss aversion in optimal stopping problems, where peopleevaluate a sequence of options in one pass, either accepting the option andstopping the search or giving up on the option forever. The best option seen sofar sets a reference point that shifts as the search progresses, and a biaseddecision-maker's utility incurs an additional penalty when they accept a lateroption that is below this reference point.  We formulate and study a behaviorally well-motivated version of the optimalstopping problem that incorporates these notions of reference dependence andloss aversion. We obtain tight bounds on the performance of a biased agent inthis model relative to the best option obtainable in retrospect (a type ofprophet inequality for biased agents), as well as tight bounds on the ratiobetween the performance of a biased agent and the performance of a rationalone. We further establish basic monotonicity results, and show an exponentialgap between the performance of a biased agent in a stopping problem withrespect to a worst-case versus a random order. As part of this, we establishfundamental differences between optimal stopping problems for rational versusbiased agents, and these differences inform our analysis.

Optimal Stopping with Behaviorally Biased Agents: The Role of Loss Aversion and Changing Reference Points