Probabilistic Representation of Complexity

Abstract

We study individuals' behavior in an environment that is deterministic, but too complex to permit tractable deterministic representation. Under mild conditions, behavior is represented by a unique probabilistic model in which the agent's inability to think through all contingencies of the problem translates into uncertainty about random states. We interpret this probabilistic model as embodying all patterns the agent perceives in his environment, yet allowing for the possibility that there may be important details he had missed. The implied behavior is rational in the traditional sense, yet consistent with an agent who believes his environment is too complex to warrant precise planing, foregoes finely detailed contingent rules in favor of vaguer plans, and expresses a preference for flexibility.

Keywords: Complexity, categorization, employment relationship, agency, Pettis integral, behavior, behavioral foundation, decision making, uncertainty, bounded rationality, incomplete contracts, discretization, features, instances

JEL Classification: D00, D81, D83