Model-Based Wisdom of the Crowd for Sequential Decision-Making Tasks.
Balloon analogue risk task
Bandit problems
Bayesian graphical models
Optimal stopping problems
Wisdom of the crowd
Journal
Cognitive science
ISSN: 1551-6709
Titre abrégé: Cogn Sci
Pays: United States
ID NLM: 7708195
Informations de publication
Date de publication:
07 2021
07 2021
Historique:
revised:
27
05
2021
received:
04
09
2020
accepted:
28
05
2021
entrez:
2
7
2021
pubmed:
3
7
2021
medline:
5
10
2021
Statut:
ppublish
Résumé
We study the wisdom of the crowd in three sequential decision-making tasks: the Balloon Analogue Risk Task (BART), optimal stopping problems, and bandit problems. We consider a behavior-based approach, using majority decisions to determine crowd behavior and show that this approach performs poorly in the BART and bandit tasks. The key problem is that the crowd becomes progressively more extreme as the decision sequence progresses, because the diversity of opinion that underlies the wisdom of the crowd is lost. We also consider model-based approaches to each task. This involves inferring cognitive models for each individual based on their observed behavior, and using these models to predict what each individual would do in any possible task situation. We show that this approach performs robustly well for all three tasks and has the additional advantage of being able to generalize to new problems for which there are no behavioral data. We discuss potential applications of the model-based approach to real-world sequential decision problems and discuss how our approach contributes to the understanding of collective intelligence.
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
e13011Informations de copyright
© 2021 Cognitive Science Society LLC.
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