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Valuation and estimation from experience

Olschewski, Sebastian; Newell, Ben R.; Oberholzer, Yvonne 1; Scheibehenne, Benjamin 1
1 Karlsruher Institut für Technologie (KIT)

The processing of sequentially presented numerical information is a prerequisite for decisions from experience, where people learn about potential outcomes and their associated probabilities and then make choices between gambles. Little is known, however, about how people's preference for choosing a gamble is affected by how they perceive and process numerical information. To address this, we conducted a series of experiments wherein participants repeatedly sampled numbers from continuous outcome distributions. They were incentivized either to estimate the means of the numbers or to state their minimum selling prices to forgo a consequential draw from the distributions (i.e., the certainty equivalents or valuations). We found that participants valued distributions below their means, valued high-variance sequences lower than low-variance sequences, and valued left-skewed sequences lower than right-skewed sequences. Though less pronounced, similar patterns occurred in the mean estimation task where preferences should not play a role. These results are not consistent with prior findings in decision from experience such as the overweighting of high numbers and the underweighting of rare events. ... mehr

Verlagsausgabe §
DOI: 10.5445/IR/1000134630
Veröffentlicht am 01.07.2021
DOI: 10.1002/bdm.2241
Zitationen: 2
Web of Science
Zitationen: 1
Zitationen: 2
Cover der Publikation
Zugehörige Institution(en) am KIT Universität Karlsruhe (TH) – Interfakultative Einrichtungen (Interfakultative Einrichtungen)
KIT-Zentrum Information Systeme Technologien (Information Systeme Technologien)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2021
Sprache Englisch
Identifikator ISSN: 0894-3257, 1099-0771
KITopen-ID: 1000134630
Erschienen in Journal of behavioral decision making
Verlag John Wiley and Sons
Seiten Art.-Nr.: bdm.2241
Nachgewiesen in Web of Science
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