By K. Arai, H. Deguchi, H. Matsui
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Additional resources for Agent-Based Modeling Meets Gaming Simulation
It is very difﬁcult to design a model that combines the complexity to imitate real markets and the simplicity to allow computational experiments to be conducted, and 3. Researchers need to share common understanding on experimental conﬁguration and results that are much more complicated than those of conventional theoretical models. U-Mart [3,4] is a research program that provides a method for the study of artiﬁcial markets. The program builds an artiﬁcial market simulation system as a test bed for economists and computer scientists to conduct studies through shared understanding.
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. This conﬁrmed the importance of agent tuning when playing a hybrid-gaming simulation. In future work, we will formulate our current model in an extensive game form and analyze the model more thoroughly. By carrying out more hybrid-gaming simulations with a dynamic model, it will be possible to analyze the model and create a robust theoretical formula.
A Time period, b satisfaction, c disparity of committee activities Garbage Can Model for Design of Citizen Participation System 47 with a high attendance ratio, involving citizens with high awareness, in small cities, type (e) ﬂat and coordination centered is the most effective. 2. Satisfaction: in cases with a high problem arrival ratio, the rankings are (e) > (c) > (b) > (d) > (a), with the (a) tree hierarchy giving the highest probability that citizens will see a solution in the committee. With a low case problem arrival ratio, we ﬁnd (c) > (e) > (b) > (d) > (a) and the (c) laered hierarchy giving the highest value of satisfaction indicator.