By Aki-Hiro Sato (auth.), Shu-Heng Chen, Takao Terano, Ryuichi Yamamoto (eds.)
Agent-based modeling/simulation is an emergent method of the research of social and monetary structures. It presents a bottom-up experimental way to be utilized to social sciences corresponding to economics, administration, sociology, and politics in addition to a few engineering fields facing social actions. This e-book comprises chosen papers awarded on the 6th foreign Workshop on Agent-Based ways in monetary and Social advanced structures held in Taipei in 2009. we've got 39 displays within the convention, and 14 papers are chosen to be incorporated during this quantity. those 14 papers are then grouped into six elements: Agent-based monetary markets; monetary forecasting and funding; Cognitive modeling of brokers; Complexity and coverage research; Agent-based modeling of fine societies; and Miscellany. The examine awarded the following indicates the cutting-edge during this speedily starting to be field.
Read or Download Agent-Based Approaches in Economic and Social Complex Systems VI: Post-Proceedings of The AESCS International Workshop 2009 PDF
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Additional resources for Agent-Based Approaches in Economic and Social Complex Systems VI: Post-Proceedings of The AESCS International Workshop 2009
Keywords Genetic algorithm (GA) · Particle Swam Optimization (PSO) · Back Propagation Network (BPN) 1 Introduction Managers and investors rely on exchange rates as indexes of international market forces, as well as indicators of monetary policies of individual countries. However, accurate forecasting is difficult; moreover, many nations have experienced dramatic changes in the exchange values of their currencies over the past decade or so . Nelly and Weller  adopted exchange rates such as US/DM and US/JPY with GA, GARCH and Risk Metrics model to predict the volatility of foreign currency markets.
0. Short selling and buying on margin are prohibited. 94 The information set that each trader uses to form his expectations consists of the stock price and dividend history up to the last five periods. Table 3 summarizes the basic statistical properties for 20 simulations and Fig. 2 displays the time series properties of a typical run. In comparison with the results obtained in real financial markets, our model fits these stylized facts very well. The fifth, seventh, and eighth columns of Table 3 are the price distortion (PD ), the trading volume and its standard deviation, respectively.
A comparison of the simulated results of the above two cases indicates that training the neural network by PSO technique gives more accurate (in terms of sum square error) and also faster (in terms of number of iterations and simulation time) results than BPN. The PSO trained ANN-based differential protection scheme provides faster, more accurate, more secure and reliable results for power transformers. Some research has taken account of the macroeconomic factors to forecast exchange rates by certain models, including the Purchasing Power Parity , Monetary model , Interest Rate Parity, Balance of Payment, Portfolio Balance Model [1, 10].