By T. Terano, H. Kita, H. Deguchi, K. Kijima
The chapters of this publication are the chosen papers from these provided on the 3rd foreign Workshop on Agent-Based techniques in financial and Social advanced platforms held in Tokyo, Japan in 2005. Articles conceal methodological concerns, computational model/software, mix with gaming simulation, and real-world functions to monetary, management/organizational and social concerns.
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Extra resources for Agent-Based Approaches in Economic and Social Complex Systems IV: Post Proceedings of The AESCS International Workshop 2005 (Springer Series on Agent Based Social Systems)
We also have improved the user interface to better suit to self-learning by reforming the input and output screens and by adding more descriptive texts and the operating instructions. 1 Purposes and Method of Experiment The experimental games aim at verifying the following two points on learning decision-makings with the business game: - the index and the methodology for quantitatively measuring the learning effects, and - the effectiveness of the individual learning tool. Sixteen learners have attended the experimental games.
Two stochastic processes are considered in the experiments, namely, iid and the first-order Markov. Each is employed for one half of the total number of runs. The parameters of these two stochastic processes are also randomly generated in such a way that the axioms of the probability function are satisfied. 1. We have 100 independent runs of the same experiment, and each run lasts for 100 market periods (T=100). 5 Simulation Results Figure 2 shows the time series of the wealth share among the 10 types of agents.
3. Bullard J, Duffy J (1999) Using Genetic Algorithms to Model the Evolution of Heterogeneous Beliefs. Computational Economics 13 (1): 41-60. 4. Chen S-H, Huang Y-C (2004) Risk Preference, Forecasting Accuracy and Survival Dynamics: Simulations Based on a Multi-Asset Agent-Based Artificial Stock Market. Working Paper Series 2004-1, AI-ECON Research Center, National Chengchi University. 5. Chen S-H, Huang Y-C (2005) Risk Preference and Survival Dynamics. In: Terano T, Kita H, Kaneda T, Arai K and Deghchi H (eds), Agent-Based Simulation: From Modeling Methodologies to Real-World Applications.
Agent-Based Approaches in Economic and Social Complex Systems IV: Post Proceedings of The AESCS International Workshop 2005 (Springer Series on Agent Based Social Systems) by T. Terano, H. Kita, H. Deguchi, K. Kijima