Towards Verification and Validation in Multiagent-Based Systems and Simulations: Analyzing Different Learning Bargaining Agents

摘要

Verification and validation (V&V) is a critical issue in both multi-agent systems (MAS) and agent-based social simulation (ABSS). As the first step towards V&V methods for MAS and ABSS, this paper investigates whether different computational models can produce the same results. Specifically, we compare three computational models with different learning mechanisms in a multiagent-based simulation and analyze the results of these models in a bargaining game as one of the fundamental examples in game theory. This type of V&V is not based on the between-models addressed in conventional research, but on a within-model. A comparison of the simulation results reveals that (1) computational models and simulation results are minimally verified and validated in the case of ES(evolutionary strategy)- and RL(reinforcement learning)-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.