Learning to make collective decisions: the impact of confidence escalation
Little is known about how people learn to take into account others' opinions in joint decisions. To address this question, we combined computational and empirical approaches. Human dyads made individual and joint visual perceptual decision and rated their confidence in those decisions (data pre...
| Main Authors: | , , , |
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| Format: | Journal article |
| Language: | English |
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Public Library of Science
2013
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| _version_ | 1797080420694622208 |
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| author | Mahmoodi, A Bang, D Ahmadabadi, M Bahrami, B |
| author_facet | Mahmoodi, A Bang, D Ahmadabadi, M Bahrami, B |
| author_sort | Mahmoodi, A |
| collection | OXFORD |
| description | Little is known about how people learn to take into account others' opinions in joint decisions. To address this question, we combined computational and empirical approaches. Human dyads made individual and joint visual perceptual decision and rated their confidence in those decisions (data previously published). We trained a reinforcement (temporal difference) learning agent to get the participants' confidence level and learn to arrive at a dyadic decision by finding the policy that either maximized the accuracy of the model decisions or maximally conformed to the empirical dyadic decisions. When confidences were shared visually without verbal interaction, RL agents successfully captured social learning. When participants exchanged confidences visually and interacted verbally, no collective benefit was achieved and the model failed to predict the dyadic behaviour. Behaviourally, dyad members' confidence increased progressively and verbal interaction accelerated this escalation. The success of the model in drawing collective benefit from dyad members was inversely related to confidence escalation rate. The findings show an automated learning agent can, in principle, combine individual opinions and achieve collective benefit but the same agent cannot discount the escalation suggesting that one cognitive component of collective decision making in human may involve discounting of overconfidence arising from interactions. |
| first_indexed | 2024-03-07T00:59:47Z |
| format | Journal article |
| id | oxford-uuid:8950f8ee-38fa-4ec9-ba87-d958a1ab5d76 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T00:59:47Z |
| publishDate | 2013 |
| publisher | Public Library of Science |
| record_format | dspace |
| spelling | oxford-uuid:8950f8ee-38fa-4ec9-ba87-d958a1ab5d762022-03-26T22:23:39ZLearning to make collective decisions: the impact of confidence escalationJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:8950f8ee-38fa-4ec9-ba87-d958a1ab5d76EnglishSymplectic Elements at OxfordPublic Library of Science2013Mahmoodi, ABang, DAhmadabadi, MBahrami, BLittle is known about how people learn to take into account others' opinions in joint decisions. To address this question, we combined computational and empirical approaches. Human dyads made individual and joint visual perceptual decision and rated their confidence in those decisions (data previously published). We trained a reinforcement (temporal difference) learning agent to get the participants' confidence level and learn to arrive at a dyadic decision by finding the policy that either maximized the accuracy of the model decisions or maximally conformed to the empirical dyadic decisions. When confidences were shared visually without verbal interaction, RL agents successfully captured social learning. When participants exchanged confidences visually and interacted verbally, no collective benefit was achieved and the model failed to predict the dyadic behaviour. Behaviourally, dyad members' confidence increased progressively and verbal interaction accelerated this escalation. The success of the model in drawing collective benefit from dyad members was inversely related to confidence escalation rate. The findings show an automated learning agent can, in principle, combine individual opinions and achieve collective benefit but the same agent cannot discount the escalation suggesting that one cognitive component of collective decision making in human may involve discounting of overconfidence arising from interactions. |
| spellingShingle | Mahmoodi, A Bang, D Ahmadabadi, M Bahrami, B Learning to make collective decisions: the impact of confidence escalation |
| title | Learning to make collective decisions: the impact of confidence escalation |
| title_full | Learning to make collective decisions: the impact of confidence escalation |
| title_fullStr | Learning to make collective decisions: the impact of confidence escalation |
| title_full_unstemmed | Learning to make collective decisions: the impact of confidence escalation |
| title_short | Learning to make collective decisions: the impact of confidence escalation |
| title_sort | learning to make collective decisions the impact of confidence escalation |
| work_keys_str_mv | AT mahmoodia learningtomakecollectivedecisionstheimpactofconfidenceescalation AT bangd learningtomakecollectivedecisionstheimpactofconfidenceescalation AT ahmadabadim learningtomakecollectivedecisionstheimpactofconfidenceescalation AT bahramib learningtomakecollectivedecisionstheimpactofconfidenceescalation |