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dc.contributor.authorHula, Andreas
dc.contributor.authorMontague, P. Read
dc.contributor.authorDayan, Peter
dc.date.accessioned2018-11-19T18:38:15Z
dc.date.available2018-11-19T18:38:15Z
dc.date.issued2015-06
dc.identifier.issn1553-734X
dc.identifier.othere1004254
dc.identifier.urihttp://hdl.handle.net/10919/85909
dc.description.abstractReciprocating interactions represent a central feature of all human exchanges. They have been the target of various recent experiments, with healthy participants and psychiatric populations engaging as dyads in multi-round exchanges such as a repeated trust task. Behaviour in such exchanges involves complexities related to each agent's preference for equity with their partner, beliefs about the partner's appetite for equity, beliefs about the partner's model of their partner, and so on. Agents may also plan different numbers of steps into the future. Providing a computationally precise account of the behaviour is an essential step towards understanding what underlies choices. A natural framework for this is that of an interactive partially observable Markov decision process (IPOMDP). However, the various complexities make IPOMDPs inordinately computationally challenging. Here, we show how to approximate the solution for the multi-round trust task using a variant of the Monte-Carlo tree search algorithm. We demonstrate that the algorithm is efficient and effective, and therefore can be used to invert observations of behavioural choices. We use generated behaviour to elucidate the richness and sophistication of interactive inference.en_US
dc.description.sponsorshipThis work was supported by a Wellcome Trust Principal Research Fellowship (PRM, AH) under grant 091188/Z/10/Z, The Kane Family Foundation (PRM), NIDA grant R01DA11723 (PRM), NIMH grant R01MH085496 (PRM), NIA grant RC4AG039067 (PRM), and The Gatsby Charitable Foundation (PD). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
dc.format.mimetypeapplication/pdfen_US
dc.language.isoen_US
dc.publisherPLOS
dc.rightsCreative Commons Attribution 4.0 Internationalen_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0en_US
dc.subjectdecision-making
dc.subjectgame-theory
dc.subjectcooperation
dc.subjectreciprocity
dc.subjectfairness
dc.subjecttrust
dc.subjectnorms
dc.subjectmodel
dc.titleMonte Carlo Planning Method Estimates Planning Horizons during Interactive Social Exchangeen_US
dc.typeArticle - Refereed
dc.title.serialPLOS Computational Biology
dc.identifier.doihttps://doi.org/10.1371/journal.pcbi.1004254
dc.identifier.volume11
dc.identifier.issue6
dc.type.dcmitypeTexten_US
dc.identifier.pmid26053429
dc.identifier.eissn1553-7358


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Creative Commons Attribution 4.0 International
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