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

  • Keiki Takadama
  • Yutaka L. Suematsu
  • Norikazu Sugimoto
  • Norberto E. Nawa
  • Katsunori Shimohara
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2927)


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.


Verification and validation multiagent-based simulation comparison of different models learning mechanism bargaining game 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Keiki Takadama
    • 1
    • 2
  • Yutaka L. Suematsu
    • 2
    • 3
  • Norikazu Sugimoto
    • 4
  • Norberto E. Nawa
    • 2
  • Katsunori Shimohara
    • 2
  1. 1.Tokyo Institute of TechnologyYokohamaJapan
  2. 2.ATR Human Information Science Labs.“Keihanna Science City” KyotoJapan
  3. 3.Graduate School of Kyoto UniversityKyotoJapan
  4. 4.Nara Institute of Science and TechnologyNaraJapan

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