In certain problem solving contexts, people organize their domain through treating themselves as the perceptual and cognitive center of their world. They identify and solve a particular problem from their perspective as a particular agent, with a particular location, at a particular time, in a particular environment. When they do this, selecting and solving problems from their perspective as an agent, they engage in a distinctive kind of agent-centered problem solving. Partially Observable Markov Decision Processes (POMDPs), a framework for modeling decision-making in uncertain environments unfolding over time, have effectively become a "standard model" of intelligent agency. Yet, as these models are ordinarily interpreted, they do not explicitly represent agent-centered problem solving. Accordingly, to model this type of problem solving, we begin by extending the standard POMDP framework to define “ePOMDPs.” This formalism models how an agent, once it centers itself on a particular self-and-world representation, plans and acts rationally from its own perspective. To capture the way that such agents choose which problem to solve, we build on our ePOMDPs to develop a “meta-ePOMDP” agent within a hierarchical Bayesian framework. We implement our meta-ePOMDP agents for two different suites of “centering game” tasks which highlight different aspects of our theory. We find that our models explain signatures of agent-centered problem solving not captured by alternative models, in particular, the difficulty of navigating spaces of possible problem representations. We close by suggesting that our model could provide the beginnings of a computational framework for a person to have a self.
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