Abstract
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.
Paper submitted to LNCS for The Fourth Workshop on Multi-Agent Based Simulation (MABS’03)
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Takadama, K., Suematsu, Y.L., Sugimoto, N., Nawa, N.E., Shimohara, K. (2003). Towards Verification and Validation in Multiagent-Based Systems and Simulations: Analyzing Different Learning Bargaining Agents. In: Hales, D., Edmonds, B., Norling, E., Rouchier, J. (eds) Multi-Agent-Based Simulation III. MABS 2003. Lecture Notes in Computer Science(), vol 2927. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24613-8_3
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DOI: https://doi.org/10.1007/978-3-540-24613-8_3
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