By Yuhsuke Koyama, Ko Ishiyama, Hiroyuki Kaneko, Isao Ono, Hiroyuki Matsui (auth.), Takao Terano Ph.D., Hajime Kita Dr. Eng., Hiroshi Deguchi Ph.D., Kyoichi Kijima Dr. Eng. (eds.)
Agent-Based Modeling/Simulation (ABM/ABS) is an rising box that allows bottom-up and experimental research in social sciences similar to economics, administration, sociology and politics. The chapters of this booklet are the chosen papers from these provided the 3rd foreign Workshop on Agent-Based techniques in fiscal and Social advanced platforms held in Tokyo, Japan in 2005. Articles during this e-book covers methodological matters, computational model/software, mix with gaming simulation, and real-world functions to fiscal, management/organizational and social issues.
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Additional info for Agent-Based Approaches in Economic and Social Complex Systems IV: Post-Proceedings of The AESCS International Workshop 2005
The group-learning version of the beer game is developed using BMDL and BMDS to allow multiple learners to concurrently learn decision-makings through the gaming style simulated experience. 2 Purpose and Configuration of Individual Learning Tool The individual learning tool enables learners to understand and analyze the case individually before participating group learning. We have developed this tool based on BMDS and the agent technology . A learner plays the game by making own entries (decision-makings), and the software agents plays the roles of the competitors with the pre-determined behaviors.
Sandroni A (2000) Do Markets Favor Agents Able to Make Accurate Predictions? Econometrica 68: 1303-1341. 19. Sciubba E (1999) The Evolution of Portfolio Rules and the Capital Asset Pricing Model. DAE Working Paper No. 9909, University of Cambridge. 20. Tesfatsion L (2001) Introduction to the Special Issue on Agent-Based Computational Economics. Journal of Economic Dynamics and Control 25: 281-293. 21. Zeldes S-P (1989) Consumption and Liquidity Constraints: An Empirical Investigation. Journal of Political Economy 97 (2): 305-346.
Equivalently, it is the log utihty function.  reestablishes this result, while in an agent-based computational setting. 1. They find that in finite time the wealth share is positively related to the CRRA coefficient, and in the long run, only the agents with high CRRA coefficients can survive. All others become extinct. This paper is an extension of  in the sense that we wish to extend the earlier testing domain of the CRRA coefficient from [0, 1] to an even larger positive domain. In doing so, we are inquiring whether a higher degree of risk aversion can actually enhance the survivability of agents.
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