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

Wu S. A., Sridhar S., Gerstenberg T. (2023).

In Proceedings of the 45th Annual Conference 

of the Cognitive Science.

Abstract

How responsible someone is for an outcome depends on what causal role their actions played, and what those actions reveal about their mental states, such as their intentions. In this paper, we develop a computational account of responsibility attribution that integrates these two cognitive processes: causal attribution and mental state inference. Our model makes use of a shared generative planning algorithm assumed to approximate people’s intuitive theory of mind about others’ behavior. We test our model 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. Across both experiments, our model accurately captures participants’ counterfactual simulations and intention inferences, and establishes that these two factors together explain responsibility judgments.

Conclusion

In this paper, we developed and tested a computational model of responsibility judgments that bridges mechanisms of counterfactual simulation and intention inference using a shared underlying generative planner. The planner captures people’s intuitive theory of mind about agents’ behavior. Across a variety of animated scenarios, our model captured participants’ counterfactual simulations and intention inferences. Together, these two components predicted responsibility judgments better than alternative models of effort, heuristics, or either component alone. This model brings us closer to a formal, comprehensive understanding of how people attribute responsibility.

A pdf of the research is here.

Here are some of the key findings of the article:

• Responsibility judgments are based on both causal attribution and mental state inference.
• Counterfactual simulations and intention inferences are important cognitive processes that underlie responsibility judgments.
• The model provides a more comprehensive account of responsibility judgments than previous models.
• The model could be used to improve the performance of artificial agents in social settings.
• The model could be used to better understand how humans make responsibility judgments.