Publikation:

Conformist social learning leads to self-organised prevention against adverse bias in risky decision making

Lade...
Vorschaubild

Dateien

Toyokawa_2-k8589eny8hdk7.pdf
Toyokawa_2-k8589eny8hdk7.pdfGröße: 7.54 MBDownloads: 144

Datum

2022

Herausgeber:innen

Kontakt

ISSN der Zeitschrift

Electronic ISSN

ISBN

Bibliografische Daten

Verlag

Schriftenreihe

Auflagebezeichnung

DOI (zitierfähiger Link)
ArXiv-ID

Internationale Patentnummer

Angaben zur Forschungsförderung

Deutsche Forschungsgemeinschaft (DFG): 422037984

Projekt

Open Access-Veröffentlichung
Open Access Gold
Core Facility der Universität Konstanz

Gesperrt bis

Titel in einer weiteren Sprache

Publikationstyp
Zeitschriftenartikel
Publikationsstatus
Published

Erschienen in

eLife. eLife Sciences Publications. 2022, 11, e75308. eISSN 2050-084X. Available under: doi: 10.7554/eLife.75308

Zusammenfassung

Given the ubiquity of potentially adverse behavioural bias owing to myopic trial-and-error learning, it seems paradoxical that improvements in decision-making performance through conformist social learning, a process widely considered to be bias amplification, still prevail in animal collective behaviour. Here we show, through model analyses and large-scale interactive behavioural experiments with 585 human subjects, that conformist influence can indeed promote favourable risk taking in repeated experience-based decision making, even though many individuals are systematically biased towards adverse risk aversion. Although strong positive feedback conferred by copying the majority's behaviour could result in unfavourable informational cascades, our differential equation model of collective behavioural dynamics identified a key role for increasing exploration by negative feedback arising when a weak minority influence undermines the inherent behavioural bias. This 'collective behavioural rescue', emerging through coordination of positive and negative feedback, highlights a benefit of collective learning in a broader range of environmental conditions than previously assumed and resolves the ostensible paradox of adaptive collective behavioural flexibility under conformist influences.

Zusammenfassung in einer weiteren Sprache

Fachgebiet (DDC)
150 Psychologie

Schlagwörter

Konferenz

Rezension
undefined / . - undefined, undefined

Forschungsvorhaben

Organisationseinheiten

Zeitschriftenheft

Zugehörige Datensätze in KOPS

Zitieren

ISO 690TOYOKAWA, Wataru, Wolfgang GAISSMAIER, 2022. Conformist social learning leads to self-organised prevention against adverse bias in risky decision making. In: eLife. eLife Sciences Publications. 2022, 11, e75308. eISSN 2050-084X. Available under: doi: 10.7554/eLife.75308
BibTex
@article{Toyokawa2022-05-10Confo-57542,
  year={2022},
  doi={10.7554/eLife.75308},
  title={Conformist social learning leads to self-organised prevention against adverse bias in risky decision making},
  volume={11},
  journal={eLife},
  author={Toyokawa, Wataru and Gaissmaier, Wolfgang},
  note={Article Number: e75308}
}
RDF
<rdf:RDF
    xmlns:dcterms="http://purl.org/dc/terms/"
    xmlns:dc="http://purl.org/dc/elements/1.1/"
    xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
    xmlns:bibo="http://purl.org/ontology/bibo/"
    xmlns:dspace="http://digital-repositories.org/ontologies/dspace/0.1.0#"
    xmlns:foaf="http://xmlns.com/foaf/0.1/"
    xmlns:void="http://rdfs.org/ns/void#"
    xmlns:xsd="http://www.w3.org/2001/XMLSchema#" > 
  <rdf:Description rdf:about="https://kops.uni-konstanz.de/server/rdf/resource/123456789/57542">
    <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2022-05-16T13:46:48Z</dcterms:available>
    <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/43615"/>
    <dc:creator>Toyokawa, Wataru</dc:creator>
    <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2022-05-16T13:46:48Z</dc:date>
    <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/>
    <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/43"/>
    <dc:creator>Gaissmaier, Wolfgang</dc:creator>
    <dcterms:title>Conformist social learning leads to self-organised prevention against adverse bias in risky decision making</dcterms:title>
    <dc:contributor>Gaissmaier, Wolfgang</dc:contributor>
    <dc:language>eng</dc:language>
    <dc:contributor>Toyokawa, Wataru</dc:contributor>
    <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/43"/>
    <dcterms:abstract xml:lang="eng">Given the ubiquity of potentially adverse behavioural bias owing to myopic trial-and-error learning, it seems paradoxical that improvements in decision-making performance through conformist social learning, a process widely considered to be bias amplification, still prevail in animal collective behaviour. Here we show, through model analyses and large-scale interactive behavioural experiments with 585 human subjects, that conformist influence can indeed promote favourable risk taking in repeated experience-based decision making, even though many individuals are systematically biased towards adverse risk aversion. Although strong positive feedback conferred by copying the majority's behaviour could result in unfavourable informational cascades, our differential equation model of collective behavioural dynamics identified a key role for increasing exploration by negative feedback arising when a weak minority influence undermines the inherent behavioural bias. This 'collective behavioural rescue', emerging through coordination of positive and negative feedback, highlights a benefit of collective learning in a broader range of environmental conditions than previously assumed and resolves the ostensible paradox of adaptive collective behavioural flexibility under conformist influences.</dcterms:abstract>
    <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/43615"/>
    <bibo:uri rdf:resource="https://kops.uni-konstanz.de/handle/123456789/57542"/>
    <dcterms:issued>2022-05-10</dcterms:issued>
    <foaf:homepage rdf:resource="http://localhost:8080/"/>
    <dc:rights>terms-of-use</dc:rights>
    <dcterms:hasPart rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/57542/1/Toyokawa_2-k8589eny8hdk7.pdf"/>
    <dcterms:rights rdf:resource="https://rightsstatements.org/page/InC/1.0/"/>
    <dspace:hasBitstream rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/57542/1/Toyokawa_2-k8589eny8hdk7.pdf"/>
  </rdf:Description>
</rdf:RDF>

Interner Vermerk

xmlui.Submission.submit.DescribeStep.inputForms.label.kops_note_fromSubmitter

Kontakt
URL der Originalveröffentl.

Prüfdatum der URL

Prüfungsdatum der Dissertation

Finanzierungsart

Kommentar zur Publikation

Allianzlizenz
Corresponding Authors der Uni Konstanz vorhanden
Internationale Co-Autor:innen
Universitätsbibliographie
Ja
Begutachtet
Ja
Link zu Forschungsdaten
Beschreibung der Forschungsdaten
Code for the agent-based simulation as well as for the experimental data analyses
Diese Publikation teilen