Show simple item record

dc.contributor.authorLo, Andrew W
dc.contributor.authorMarlowe, Katherine P
dc.contributor.authorZhang, Ruixun
dc.date.accessioned2021-10-27T20:24:08Z
dc.date.available2021-10-27T20:24:08Z
dc.date.issued2021
dc.identifier.urihttps://hdl.handle.net/1721.1/135585
dc.description.abstract<jats:p>Probability matching, also known as the “matching law” or Herrnstein’s Law, has long puzzled economists and psychologists because of its apparent inconsistency with basic self-interest. We conduct an experiment with real monetary payoffs in which each participant plays a computer game to guess the outcome of a binary lottery. In addition to finding strong evidence for probability matching, we document different tendencies towards randomization in different payoff environments—as predicted by models of the evolutionary origin of probability matching—after controlling for a wide range of demographic and socioeconomic variables. We also find several individual differences in the tendency to maximize or randomize, correlated with wealth and other socioeconomic factors. In particular, subjects who have taken probability and statistics classes and those who self-reported finding a pattern in the game are found to have randomized more, contrary to the common wisdom that those with better understanding of probabilistic reasoning are more likely to be rational economic maximizers. Our results provide experimental evidence that individuals—even those with experience in probability and investing—engage in randomized behavior and probability matching, underscoring the role of the environment as a driver of behavioral anomalies.</jats:p>
dc.language.isoen
dc.publisherPublic Library of Science (PLoS)
dc.relation.isversionof10.1371/journal.pone.0252540
dc.rightsCreative Commons Attribution 4.0 International license
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.sourcePLoS
dc.titleTo maximize or randomize? An experimental study of probability matching in financial decision making
dc.typeArticle
dc.contributor.departmentSloan School of Management
dc.contributor.departmentSloan School of Management. Laboratory for Financial Engineering
dc.contributor.departmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
dc.relation.journalPLOS ONE
dc.eprint.versionFinal published version
dc.type.urihttp://purl.org/eprint/type/JournalArticle
eprint.statushttp://purl.org/eprint/status/PeerReviewed
dc.date.updated2021-09-13T12:11:24Z
dspace.orderedauthorsLo, AW; Marlowe, KP; Zhang, R
dspace.date.submission2021-09-13T12:11:26Z
mit.journal.volume16
mit.journal.issue8
mit.licensePUBLISHER_CC
mit.metadata.statusAuthority Work and Publication Information Needed


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record