| > Many AI frameworks define agency as utility maximization: specify a scalar objective, then learn actions that increase it. A different foundation treats //agency as imitation//. An interacting agent acquires //behavioral schemas//, compact first-person rules for how to act and what outcomes to expect, by learning compressive explanations of interaction regularities. A single idealized agent is built from three ingredients: (i) a universal, simplicity-biased pattern generator that ranges over all computable ways an interaction could unfold; (ii) a first-person distinction between actions and observations, where only observations count as evidence; and (iii) an event-triggered turn-taking interface that determines, at each step, whether the next output is produced by the world or by the agent. For any world generated by a fixed computable rule, the agent learns to imitate by translating the world’s third-person demonstrations into first-person behavior. In everyday terms, large departures from the counterfactual target, what the world would have produced in the agent’s place next, can occur only finitely many times in an appropriate averaged sense. The same framework can, in principle, support plural and heterogeneous schemas, including utility maximization, without making utility the primitive definition of purpose. | > Many AI frameworks define agency as utility maximization: specify a scalar objective, then learn actions that increase it. A different foundation treats //agency as imitation//. An interacting agent acquires //behavioral schemas//, compact first-person rules for how to act and what outcomes to expect, by learning compressive explanations of interaction regularities. A single idealized agent is built from three ingredients: (i) a universal, simplicity-biased pattern generator that ranges over all computable ways an interaction could unfold; (ii) a first-person distinction between actions and observations, where only observations count as evidence; and (iii) an event-triggered turn-taking interface that determines, at each step, whether the next output is produced by the world or by the agent. For any world generated by a fixed computable rule, the agent learns to imitate by translating the world’s third-person demonstrations into first-person behavior. In everyday terms, large departures from the counterfactual target, what the world would have produced in the agent’s place next, can occur only finitely many times in an appropriate averaged sense. The same framework can, in principle, support plural and heterogeneous schemas, including utility maximization, without making utility the primitive definition of purpose. |