People Evaluate Agents Based on the Algorithms That Drive Their Behavior

When people see an agent perform a task, do they care if the underlying algorithm driving it is 'intelligent' or not? More generally, when people intuitively evaluate the performance of others, do they value external performance metrics (intuitive behaviorism) or do they also take into account the underlying algorithm driving the agent's behavior (intuitive cognitivism)? We propose 3 dimensions for examining this distinction: Action Efficiency, Representation Efficiency, and Generalization. Across 3 tasks (<i>N</i> = 598), we showed people pairs of maze-solving agents, together with the programs driving the agents' behavior. Participants were asked to pick the 'better' of the two programs, based on a single example of the two programs, evaluated on the same maze. Each pair of programs varied along one of our 3 proposed dimensions. Our framework predicts people's choice of program across the tasks, and the results support the idea that people are intuitive cognitivists.