By K. Arai, H. Deguchi, H. Matsui
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Additional resources for Agent-Based Modeling Meets Gaming Simulation
On the other hand, in education in the ﬁeld of social science, mainly economics, the system is mostly used for gaming simulation where students themselves participate in trading as traders. Use in gaming simulation can be classiﬁed into two types; one is network use, where a server machine is prepared on the network and multiple human agents participate in trading using client software on multiple personal computers (PCs) connected to the network; the other is standalone use, where a machine agent built in the market simulator on one PC and a human agent compete with each other.
Time period: when the number of solved problems reaches 190, planning is completed. This complete time period shows the efﬁciency of the planning system. b. Satisfaction: a rate is calculated of the total number of times that problems are solved in the committee and the total number of times the problem was seen by each citizen’s committee. This indicator suggests the degree of citizen satisfaction. c. Disparity of committee activities: this indicator shows the difference of the number of active terms between the most active and least active committees.
In response, the AI agents were adapted to play a game of 25 terms and all AI agents were changed to become long-term maximizers instead of short-term maximizers. In this simulation the number of human players was increased to 6 and the number of AI agents remained at 12: 4 long-term proﬁt maximizers, 4 long-term market share maximizers, and 4 long-term technological level maximizers. The results of the second simulation are shown in Fig. 3. In the ﬁrst simulation, learned human players defeated AI agents easily.
Agent-Based Modeling Meets Gaming Simulation by K. Arai, H. Deguchi, H. Matsui