Monte Carlo Planning Method Estimates Planning Horizons during Interactive Social Exchange.
Reciprocating 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...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2015-06-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1004254&type=printable |
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| _version_ | 1849726618092699648 |
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| author | Andreas Hula P Read Montague Peter Dayan |
| author_facet | Andreas Hula P Read Montague Peter Dayan |
| author_sort | Andreas Hula |
| collection | DOAJ |
| description | Reciprocating 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. |
| format | Article |
| id | doaj-art-8c97a4f1877e4c0091bb794499e28307 |
| institution | DOAJ |
| issn | 1553-734X 1553-7358 |
| language | English |
| publishDate | 2015-06-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj-art-8c97a4f1877e4c0091bb794499e283072025-08-20T03:10:07ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582015-06-01116e100425410.1371/journal.pcbi.1004254Monte Carlo Planning Method Estimates Planning Horizons during Interactive Social Exchange.Andreas HulaP Read MontaguePeter DayanReciprocating 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.https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1004254&type=printable |
| spellingShingle | Andreas Hula P Read Montague Peter Dayan Monte Carlo Planning Method Estimates Planning Horizons during Interactive Social Exchange. PLoS Computational Biology |
| title | Monte Carlo Planning Method Estimates Planning Horizons during Interactive Social Exchange. |
| title_full | Monte Carlo Planning Method Estimates Planning Horizons during Interactive Social Exchange. |
| title_fullStr | Monte Carlo Planning Method Estimates Planning Horizons during Interactive Social Exchange. |
| title_full_unstemmed | Monte Carlo Planning Method Estimates Planning Horizons during Interactive Social Exchange. |
| title_short | Monte Carlo Planning Method Estimates Planning Horizons during Interactive Social Exchange. |
| title_sort | monte carlo planning method estimates planning horizons during interactive social exchange |
| url | https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1004254&type=printable |
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