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Balance between breadth and depth in human many-alternative decisions

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dc.contributor.author Vidal, Alice
dc.contributor.author Soto-Faraco, Salvador, 1970-
dc.contributor.author Moreno Bote, Rubén
dc.date.accessioned 2023-01-18T07:33:14Z
dc.date.available 2023-01-18T07:33:14Z
dc.date.issued 2022
dc.identifier.citation Vidal A, Soto-Faraco S, Moreno-Bote R. Balance between breadth and depth in human many-alternative decisions. eLife. 2022;11:e76985. DOI: 10.7554/eLife.76985
dc.identifier.issn 2050-084X
dc.identifier.uri http://hdl.handle.net/10230/55320
dc.description.abstract Many everyday life decisions require allocating finite resources, such as attention or time, to examine multiple available options, like choosing a food supplier online. In cases like these, resources can be spread across many options (breadth) or focused on a few of them (depth). Whilst theoretical work has described how finite resources should be allocated to maximize utility in these problems, evidence about how humans balance breadth and depth is currently lacking. We introduce a novel experimental paradigm where humans make a many-alternative decision under finite resources. In an imaginary scenario, participants allocate a finite budget to sample amongst multiple apricot suppliers in order to estimate the quality of their fruits, and ultimately choose the best one. We found that at low budget capacity participants sample as many suppliers as possible, and thus prefer breadth, whereas at high capacities participants sample just a few chosen alternatives in depth, and intentionally ignore the rest. The number of alternatives sampled increases with capacity following a power law with an exponent close to 3/4. In richer environments, where good outcomes are more likely, humans further favour depth. Participants deviate from optimality and tend to allocate capacity amongst the selected alternatives more homogeneously than it would be optimal, but the impact on the outcome is small. Overall, our results undercover a rich phenomenology of closeto-optimal behaviour and biases in complex choices.
dc.description.sponsorship This work is supported by the Howard Hughes Medical Institute (HHMI, Ref: 55008742), MINECO (Spain; BFU2017-85936-P), ICREA Academia (2016) and Ministerio de Ciencia e Innovación (Ref: PID2020-114196GB-I00/AEI) to RM-B. SS-. is funded by Ministerio de Ciencia e Innovación (Ref: PID2019-108531GB-I00 AEI/FEDER) and the FEDER/ERDF Operative Programme for Catalunya 2014-2020. AV is supported by a FI fellowship from the AGAUR (2019FI_B 00302).
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher eLife
dc.relation.ispartof eLife. 2022;11:e76985.
dc.relation.isreferencedby https://osf.io/kdbqs/
dc.rights © 2022 Vidal et al. This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.
dc.rights.uri https://creativecommons.org/licenses/by/4.0/
dc.subject.other Neurociències--Presa de decisions
dc.title Balance between breadth and depth in human many-alternative decisions
dc.type info:eu-repo/semantics/article
dc.identifier.doi http://dx.doi.org/10.7554/eLife.76985
dc.relation.projectID info:eu-repo/grantAgreement/ES/2PE/PID2019-108531GB-I00
dc.relation.projectID info:eu-repo/grantAgreement/ES/2PE/BFU2017-85936-P
dc.relation.projectID info:eu-repo/grantAgreement/ES/2PE/PID2020-114196GB-I00
dc.rights.accessRights info:eu-repo/semantics/openAccess
dc.type.version info:eu-repo/semantics/publishedVersion

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