By Hiroshi Deguchi (auth.), Kiyoshi Arai D.Eng., Hiroshi Deguchi D.Sc, D.Econ., Hiroyuki Matsui D.Eng. (eds.)
This number of very good papers cultivates a brand new standpoint on agent-based social method sciences, gaming simulation, and their hybridization. many of the papers incorporated right here have been provided within the distinct consultation titled Agent-Based Modeling Meets Gaming Simulation at ISAGA2003, the thirty fourth annual convention of the foreign Simulation and Gaming organization (ISAGA) at Kazusa Akademia Park in Kisarazu, Chiba, Japan, August 25–29, 2003. This post-proceedings used to be supported via the twenty-?rst century COE (Centers of Excellence) application construction of Agent-Based Social structures Sciences (ABSSS), confirmed on the Tokyo Institute of know-how in 2004. the current quantity contains papers submitted to the certain consultation of ISAGA2003 and offers a superb instance of the varied scope and traditional of study accomplished in simulation and gaming at the present time. The subject matter of the exact consultation at ISAGA2003 used to be Agent-Based Modeling Meets Gaming Simulation. these days, agent-based simulation is changing into highly regarded for modeling and fixing complicated social phenomena. it's also used to reach at sensible suggestions to social difficulties. while, besides the fact that, the validity of simulation doesn't exist within the magni?cence of the version. R. Axelrod stresses the simplicity of the agent-based simulation version throughout the “Keep it uncomplicated, silly” (KISS) precept: As a fantastic, easy modeling is essential.
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Extra info for Agent-Based Modeling Meets Gaming Simulation
From Fig. 3, we observed that more stable learning results are obtained when the GA ratio and random action ratio are smaller. If these ratios become large, the agents would no longer be able to learn effectively. This means that we are able to obtain better learning performance when the frequency of GA operations is larg and the frequency of new action generations for exploration is small. 2 in further experiments to obtain higher rewards. Figure 4 shows the results of 3000 simulations using the above parameters.
Our school, the Graduate School of Systems Management (GSSM), University of Tsukuba, is a good place to apply the new approach. The students are all business people from various industries, with different expertise, and different backgrounds. Therefore, although the academic levels of the students are quite diverged, they will not be satisﬁed by playing-only simulators. The students want to know how to make good management decisions by developing business models, decision support tools, and business information systems.
Firm agents (both AI agents and human players) determined the levels of R&D investment and production investment. The efﬁciencies of AI agents were compared with those of their human counterparts and the varying strategies of both AI agents and human players were assessed and veriﬁed. It was found that the learning speed of human players was much faster than AI agents and that the AI agents were validated and competed successfully against the human players. Long-term maximization agents playing against human players performed well, but short-term maximization agents failed.