Xia Qu

TL;DR: This article model the behavioral data obtained from the studies using the interactive partially observable Markov decision process, appropriately simplified and augmented with well-known models simulating human learning and decision, and indicates that these could be viable ways for computationally modeling strategic behavioral data in a general way.

TL;DR: This chapter describes the general I-POMDP framework and a particular approximation that facilitates its usage and explores the effectiveness of models based on simplified I- POMDPs in fitting experimental data onto theory-of-mind–based recursive reasoning.

TL;DR: This work seeks to computationally model behavioral data that is consistent with deep recursive reasoning in competitive games and uses generative, process models built from agent frameworks that simulate the observed data well and also exhibit psychological intuition.

TL;DR: The authors used generative, process models built from agent frameworks that simulate the observed data well and also exhibit psychological intuition to model behavioral data that is consistent with deep recursive reasoning in competitive games and found that if the games are made competitive and therefore representationally simpler, humans generally exhibited behavior that was more consistent with deeper levels of recursive reasoning.