A computational model of responsibility judgments from counterfactual simulations and intention inferences

Abstract

How responsible someone is for an outcome depends on both the causal role of their actions, and what those actions reveal about their moral character. Prior work has successfully modeled people’s causal attributions and mental state inferences using planning algorithms assumed to approximate people’s intuitive theory of mind about others’ behavior. In this paper, we develop a unified computational framework for responsibility judgments in which the same generative planner can model both of these processes. We test our framework on a variety of animated social scenarios in two experiments. Experiment 1 features simple cases of helping and hindering. Experiment 2 features more complex interactions that require recursive reasoning, including cases where one agent affects another by merely signaling their intentions without physically acting on the world. Our model accurately captures participants’ counterfactual simulations and intention inferences. Together, these two factors explain responsibility judgments.