Probability matching, the magnitude of reinforcement, and classifier system bidding

摘要

This paper juxtaposes the probability matching paradox of decision theory and the magnitude of reinforcement problem of animal learning theory to show that simple classifier system bidding structures are unable to match the range of behaviors required in the deterministic and probabilistic problems faced by real cognitive systems. The inclusion of a variance-sensitive bidding (VSB) mechanism is suggested, analyzed, and simulated to enable good bidding performance over a wide range of nonstationary probabilistic and deterministic environments.