Cross-Element Validation in Multiagent-Based Simulation: Switching Learning Mechanisms in Agents

The validity of simulation results remains an open problem in multiagent-based simulation (MABS). Since such validity is based on the validation of computational models, we propose a cross-element validation method that validates computational models by investigating whether several models can produce the same results after changing an element in the agent architecture. Specifically, this paper focuses on learning mechanisms applied to agents as one of the important elements and compares three different MABSs employing either an evolutionary strategy (ES), a learning classifier system (LCS), or a reinforcement learning (RL). This type of validation is not based on the between -models addressed in conventional research but on a within -model. A comparison of the simulation results in a bargaining game, one of the fundamental examples in game theory, reveals that (1) computational models are minimally validated in the case of ES- and RL-based agents; and (2) learning mechanisms that enable agents to acquire their rational behaviors differ according to the knowledge representation ( i.e. , the strategies in the bargaining game) of the agents. Concretely, we found that (2-a) the ES-based agents derive the same tendency in game theory but the LCS-based agents cannot in the case of employing continuous knowledge representation; and (2-b) the same ES-based agents cannot derive the same tendency in game theory but the RL-based agents derive it in the case of employing discrete knowledge representation.