title: Monte Carlo Planning Method Estimates Planning Horizons during Interactive Social Exchange.
abstract: 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.
authors.affiliation: Wellcome Trust Centre for Neuroimaging, University College London, London, United Kingdom.
authors.forename: Andreas
authors.initials: A
authors.lastname: Hula
authors.affiliation: Wellcome Trust Centre for Neuroimaging, University College London, London, United Kingdom; Human Neuroimaging Laboratory, Virginia Tech Carilion Research Institute, Roanoke, Virginia, United States of America; Department of Physics, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, United States of America.
authors.forename: P Read
authors.initials: PR
authors.lastname: Montague
authors.affiliation: Gatsby Computational Neuroscience Unit, University College London, London, United Kingdom.
authors.forename: Peter
authors.initials: P
authors.lastname: Dayan
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D003657:Decision Making / Q000502:physiology*; D006801:Humans; D007398:Interpersonal Relations; D008390:Markov Chains; D008954:Models, Biological; D009010:Monte Carlo Method; D011582:Psychological Theory; D012201:Reward; D035502:Trust