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 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.Author Summary: Agents interacting in games with multiple rounds must model their partner’s thought processes over extended time horizons. This poses a substantial computational challenge that has restricted previous behavioural analyses. By taking advantage of recent advances in algorithms for planning in the face of uncertainty, we demonstrate how these formal methods can be extended. We use a well studied social exchange game called the trust task to illustrate the power of our method, showing how agents with particular cognitive and social characteristics can be expected to interact, and how to infer the properties of individuals from observing their behaviour.