Agent-Based Modeling Meets Gaming Simulation by Hiroshi Deguchi (auth.), Kiyoshi Arai D.Eng., Hiroshi

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 procedure sciences, gaming simulation, and their hybridization. lots of the papers incorporated the following have been awarded within the specified 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 was once supported by way of the twenty-?rst century COE (Centers of Excellence) application construction of Agent-Based Social structures Sciences (ABSSS), tested on the Tokyo Institute of expertise in 2004. the current quantity includes papers submitted to the unique consultation of ISAGA2003 and offers a very good instance of the various scope and conventional of analysis completed in simulation and gaming this present day. The topic of the distinct consultation at ISAGA2003 was once Agent-Based Modeling Meets Gaming Simulation. these days, agent-based simulation is changing into extremely popular for modeling and fixing advanced social phenomena. it's also used to reach at functional recommendations 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 during the “Keep it easy, silly” (KISS) precept: As an incredible, uncomplicated modeling is essential.

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It is determined by production investment FPi,t. We assume that the production cost per product (C) is fixed and is defined in Eq. 2: Qi , t = FPi , t C (2) Ti,t represents the technological level of firm i for previous term t − 1. T−i,t−1 represents the technological level of firms other than firm i for term t. T is the technological innovation function. The technological level of a firm is defined in Eq. 3: Ti , t = T ( FRi , t , Ti , t −1 , T− i , t −1 ) (3) The product price for firm i for term t is Pi,t, which is defined in Eq.

Depending on their goals all AI agents have utility functions and they evaluate their actions for learning. The types of goals are described below: 1. Type P aims to maximize profit 2. Type S aims to maximize market share 3. Type T aims to maximize technological level AI firm agents evaluate the action rule for learning; when they have different goals, firms have different evaluation functions. A reinforcement learning system with profit sharing as a decision-making tool is used for an agent who makes a decision depending on rules in the classifier system.

The allocation of resources between R&D investment and production capital investment is essential for any firm competing in the high-tech products market. Many economists now use computer simulation methods to research the details and effects of innovation. Nelson and Winter [1] carried out a simulation study to analyze the Schumpeter hypothesis. In the Nelson–Winter model, two kinds of techniques for technological improvement were defined: innovation and imitation. This simulation analysis strongly supported Schumpeter’s hypothesis [1].

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