Micro- and Macro-Level Validation in Agent-Based Simulation: Reproduction of Human-Like Behaviors and Thinking in a Sequential Bargaining Game

This paper addresses both microand macro-level validation in agent-based simulation (ABS) to explore validated agents who can reproduce not only human-like behaviors externally but also human-like thinking internally. For this purpose, we employ the sequential bargaining game, which can investigate a change of humans’ behaviors and thinking longer than the ultimate game (i.e., one time bargaining game), and compare simulation results of Q-learning agents employing either of three types of action selections (i.e., the -greed, roulette, and Boltzmann distribution selections) in the game. Intensive simulations have revealed the following implications: (1) Q-learning agents with any type of three action selections can reproduce human-like behaviors but cannot human-like thinking, which means that they are validated from the macro-level viewpoint but not from the micro-level viewpoint; and (2) Q-learning agents employing Boltzmann distribution selection with changing the random parameter can reproduce both human-like behaviors and thinking, which means that they are validated from both microand macro-level viewpoints.