By Hiroshi Deguchi (auth.), Kiyoshi Arai D.Eng., Hiroshi Deguchi D.Sc, D.Econ., Hiroyuki Matsui D.Eng. (eds.)
This choice of very good papers cultivates a brand new viewpoint on agent-based social process sciences, gaming simulation, and their hybridization. many of the papers incorporated the following have been offered within the exact consultation titled Agent-Based Modeling Meets Gaming Simulation at ISAGA2003, the thirty fourth annual convention of the overseas Simulation and Gaming organization (ISAGA) at Kazusa Akademia Park in Kisarazu, Chiba, Japan, August 25–29, 2003. This post-proceedings used to be supported by means of the twenty-?rst century COE (Centers of Excellence) software production of Agent-Based Social platforms Sciences (ABSSS), validated on the Tokyo Institute of expertise in 2004. the current quantity contains papers submitted to the targeted consultation of ISAGA2003 and offers an exceptional instance of the varied scope and conventional of study completed in simulation and gaming this present day. The subject of the exact consultation at ISAGA2003 was once Agent-Based Modeling Meets Gaming Simulation. these days, agent-based simulation is turning into extremely popular for modeling and fixing advanced social phenomena. it's also used to reach at functional options to social difficulties. even as, even though, 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 basic, silly” (KISS) precept: As a great, uncomplicated modeling is essential.
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Additional resources for Agent-Based Modeling Meets Gaming Simulation
5 CF, classiﬁer; GA, genetic algorithm Table 3. Performance of reactive agents and human players Player Reward Reactive agent Human players 133 (126) 174 (141) Values in parentheses are rounded to the resolutions shown in Table 1 Experimental Results As the baseline of the series of the experiments described below, we validated the game rewards of the reactive agents and human (best) players in Table 3. In Table 3, rewards 133 and 174 are those obtained during the game, and rewards 126 and 141 are the values rounded to the resolutions of decisions shown in Table 1.
The U-Mart system is the collective name of the simulation environment in which futures of Mainichi Shimbun J30, an actual stock index, are traded on a virtual market, so that it can reﬂect the complexity of the actual market and at the same time form a unique price (Fig. 1). The U-Mart system is a server–client system that uses a dedicated protocol built on TCP/IP to exchange trading information on the Internet. The server that simulates the stock exchange accepts orders from clients, executes pricing and trading, and manages the asset account.
The action rule parameters in the reinforcement learning system are the ﬁrms’ cash, and the difference between their technological levels and average technological level. Firm agents select and carry out an action rule from their action rule sets in proportion to the weight of the action rule (Roulette selection). The set of action rules is shown in Eq. 10. Ax and Bx denote the conditional parts of the rule x, while Cx and Dx denote the action parts of the rule x. Wpx denotes the weight of proﬁt maximization of rule x, Wsx denotes the weight of market share maximization of rule x, and Wtx denotes the weight of technological level maximization of rule x.