Decision making for two learning agents acting like human agents : A proof of concept for the application of a Learning Classifier Systems

摘要

The paper investigates the suitability of a Learning Classifier System (LCS) implementation for mimicking human decision making in agent based social simulations incorporating network effects. Model behavior is studied for three distinct scenario settings. We provide proof of concept for the adequacy of LCS to tackle the task at hand. Specifically, it is found that the LCS provides the agents within the simulation model with the ability to learn and to react to environmental changes while accounting for bounded rational decision making and the presence of imperfect information, as well as network effects. Moreover, it can be shown that the LCS-agents exhibit a habit like behavioural pattern.