TY - JOUR
T1 - Optimal stopping with behaviorally biased agents
T2 - The role of loss aversion and changing reference points
AU - Kleinberg, Jon
AU - Kleinberg, Robert
AU - Oren, Sigal
N1 - Funding Information:
Research supported in part by a Vannevar Bush Faculty Fellowship, MURI grant W911NF-19-0217, AFOSR grant FA9550-19-1-0183, and BSF grant 2018206.Research supported by NSF Grant CCF-1512964.Work supported by BSF grant 2018206 and ISF grant 2167/19.
Publisher Copyright:
© 2022 Elsevier Inc.
PY - 2022/3/29
Y1 - 2022/3/29
N2 - We explore the implications of two central human biases studied in behavioral economics, reference points and loss aversion, in optimal stopping problems. In such problems, people evaluate a sequence of options in one pass, either accepting the option and stopping the search or giving up on the option forever. Here we assume that the best option seen so far sets a reference point that shifts as the search progresses, and a biased decision-maker's utility incurs an additional penalty when they accept a later option that is below this reference point. Our results include tight bounds on the performance of a biased agent in this model relative to the best option obtainable in retrospect (a type of prophet inequality for biased agents), as well as tight bounds on the ratio between the performance of a biased agent and the performance of a rational one.
AB - We explore the implications of two central human biases studied in behavioral economics, reference points and loss aversion, in optimal stopping problems. In such problems, people evaluate a sequence of options in one pass, either accepting the option and stopping the search or giving up on the option forever. Here we assume that the best option seen so far sets a reference point that shifts as the search progresses, and a biased decision-maker's utility incurs an additional penalty when they accept a later option that is below this reference point. Our results include tight bounds on the performance of a biased agent in this model relative to the best option obtainable in retrospect (a type of prophet inequality for biased agents), as well as tight bounds on the ratio between the performance of a biased agent and the performance of a rational one.
KW - Algorithmic game theory
KW - Cognitive bias
KW - Prophet inequality
UR - http://www.scopus.com/inward/record.url?scp=85127302133&partnerID=8YFLogxK
U2 - 10.1016/j.geb.2022.03.007
DO - 10.1016/j.geb.2022.03.007
M3 - Article
AN - SCOPUS:85127302133
SN - 0899-8256
VL - 133
SP - 282
EP - 299
JO - Games and Economic Behavior
JF - Games and Economic Behavior
ER -