By T. Terano, H. Kita, H. Deguchi, K. Kijima
The chapters of this e-book are the chosen papers from these awarded on the 3rd foreign Workshop on Agent-Based ways in monetary and Social advanced structures held in Tokyo, Japan in 2005. Articles conceal methodological concerns, computational model/software, mixture with gaming simulation, and real-world purposes to financial, management/organizational and social concerns.
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Extra info 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)
The assumptions given in these models are quite far from the reality. In this paper we focus on an empirical market and try to find a model based on assumptions as close as possible to the reality. More precisely, we are interested in the (French) Fruit and Vegetable Market of National Interest in France. In economics, different techniques are used to predict or understand the individuals' behavior on a market. Some analytical approaches study the behavior of sellers and buyers on a market considering that agents are perfectly rational but have access to limited information.
It is not easy, however, to identify the reasons or causes of such distinctions from historical data, since those d a t a do not tell any information about the behaviors of speculators. Therefore one has to employ other approaches to clarify the mechanisms of time series data. Several questionnaire studies have focused on the relations between time scales of speculators and information they need, and consequently have shown t h a t there is a diflFerence between traders at shorter horizons and investors at longer horizons [16,20,22,28].
Accordingly, it can sometimes be very difficult to trace and to predict the resulting outcome, known as the emergent behavior. In this paper, we follow what was initiated in , and equip our agents with the genetic algorithm to cope with the finite-horizon stochastic dynamic optimization problem, (1) - (3). The GA is applied here at two different levels, a high level (learning level) and a low level (optimization level). First, at the high level, it is applied as a belief-updating scheme. This is about the Bl appearing in (1).
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