Risk neutrality in learning classifier systems

Both economics and biology have come to agree that successful behavior in a stochastic environment responds to the variance of potential outcomes. Unfortunately, when biological and economic paradigms are mated together in a learning classifier system (LCS), decision-making agents called classifiers typically simply ignore risk. Since a fundamental problem of learning is risk management, LCS have not always performed as well as theoretically predicted. This paper develops a novel model of risk-neutral reinforcement learning in a traditional Bucket Brigade credit-allocation market under the pressure of a Genetic Algorithm. I demonstrate the applicability of the basic model to the classical LCS design and reexamine two basic issues where traditional LCS performance fails to meet expectations: default hierarchies and long chains of coupled classifiers. Risk-neutrality and noisy probabilistic auctions create dynamic instability in both areas, while identical preferences result in market failure in default hierarchies and exponential attenuation of price signals down classifier chains. Despite the limitations of simple risk-neutral classifiers, I show they’re capable of cheap short-run emulation of more rational behaviors. Still, risk-neutral information markets are a dead end. The model suggests a path toward a new type of LCS built on stable, heterogeneous, and risk-averse preferences under efficient auctions and access to more complete markets exploitable by competing risk management strategies. This will require a radical rethinking of the evolutionary and economic algorithms, but ultimately heralds a return to a market-based approach to LCS.