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 <i>cross-element validation</i> 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 <i>learning mechanisms</i> 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 <i>between</i>-models addressed in conventional research but on a <i>within</i>-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>i.e.</i>, 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 <i>continuous</i> 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 <i> discrete</i> knowledge representation.