I’d do anything for a cookie (but I won’t do that): Children’s understanding of the costs and rewards underlying rational action

Julian Jara-EttingerMassachusetts Institute of Technology, Cambridge, Massachusetts, United States
Hyowon GweonMassachusetts Institute of Technology
Josh TenenbaumMassachusetts Institute of Technology
Laura SchulzMassachusetts Institute of Technology

Abstract

Humans explain and predict other agents’ behavior using mental state concepts, such as beliefs and desires. Computational and developmental evidence suggest that such inferences are enabled by a principle of rational action: the expectation that agents act efficiently, within situational constraints, to achieve their goals. Here we propose that the expectation of rational action is instantiated by a naïve utility calculus sensitive to both agent-constant and agent-specific aspects of costs and rewards associated with actions. We show that children can infer unobservable aspects of costs (differences in agents’ competence) from information about subjective differences in rewards (i.e., agents’ preferences) and vice versa. Moreover, children can design informative interventions on both objects and agents to infer unobservable constraints on agents’ actions.

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