Agent-Based Modeling Meets Gaming Simulation (Agent-Based Social Systems)
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This collection of excellent papers cultivates a new perspective on agent-based social system sciences, gaming simulation, and their hybridization. Most of the papers included here were presented in the special session titled Agent-Based Modeling Meets Gaming Simulation at ISAGA2003, the 34th annual conference of the International Simulation and Gaming Association (ISAGA) at Kazusa Akademia Park in Kisarazu, Chiba, Japan, August 25–29, 2003. This post-proceedings was supported by the twenty-?rst century COE (Centers of Excellence) program Creation of Agent-Based Social Systems Sciences (ABSSS), established at the Tokyo Institute of Technology in 2004. The present volume comprises papers submitted to the special session of ISAGA2003 and provides a good example of the diverse scope and standard of research achieved in simulation and gaming today. The theme of the special session at ISAGA2003 was Agent-Based Modeling Meets Gaming Simulation. Nowadays, agent-based simulation is becoming very popular for modeling and solving complex social phenomena. It is also used to arrive at practical solutions to social problems. At the same time, however, the validity of simulation does not exist in the magni?cence of the model. R. Axelrod stresses the simplicity of the agent-based simulation model through the “Keep it simple, stupid” (KISS) principle: As an ideal, simple modeling is essential.
All the firms that maximized their technological level had a cash level of less than 20 monetary unit. All market share maximizers obtained poor scores in both cash and market share. The winner in the first run, share3, remained the best firm among the market share maximizers; however, its overall performance was below average. The profit maximizers obtained good scores in both runs and it would seem that profit maximizers have the ability to adapt to different environments. Among the human
rule. Environmental Information Mode (EI Mode) In this mode we try to observe whether CT emerges when an agent is given only environment-related information that has less quality vis-à-vis the divine revelation in terms of encouraging CT. The assumed mechanism, including the learning process, the fitness function, etc., is just the same as that of the divine revelation mode except for the input information. Assuming /N_sense/ = 1, we provide to every agent an order as classified by the profit
S-curve. Thus, technological competency or otherwise does not explain the failure of industry leaders, but rather this is done by factors rooted in the way new product development projects are valued. Empirical evidence suggests the following causes for disruption: • Market Segment Overlap Disruption can occur only if different segments have basically the same needs, but with different feature weights. As shown in Table 1, lower system price can compensate for inferior product features and
capture a significant share of the market. On the other hand, if the efficiency is very high, it is more attractive to the incumbent to switch to the new technology than to continue using the initial technology. The result is a duopolistic market where price competition between similar products prevails. Finally, we have found that differentials in organizational inertia expose the incumbent to an increased risk of disruption. Both results regarding technological efficiency and organizational
strategy consists of the participating rate for each dealing form, a suggested sale price, and a suggested purchase price for each dealing form. We describe each factor below. Participation Rate for Each Dealing Form The sale rates in the total suggested sale of selling agent A in period t for early dealing, direct dealing, auction, and no dealing are SpA1 (t), SpA2 (t), SpA3 (t), and SpA4 (t), respectively. SpAn ( t ) = IPm An × Pm ( t ) + ISm An × Sm ( t ) + ISAn × SA ( t ) ( n = 1, 2, 3, 4 )