Computational Intelligence in Economics and Finance: Volume by Paul P. Wang, Tzu-Wen Kuo

By Paul P. Wang, Tzu-Wen Kuo

Readers will locate, during this hugely appropriate and groundbreaking booklet, examine starting from functions in monetary markets and company management to varied economics difficulties. not just are empirical stories using numerous CI algorithms offered, yet so are also theoretical types in accordance with computational tools. as well as direct functions of computational intelligence, readers may also detect how those tools are mixed with traditional analytical equipment equivalent to statistical and econometric versions to yield most popular results.

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Young did not necessarily advocate using such a model without considering experience studies but presented the simplified model to demonstrate more clearly how to represent linguistic rules. Reference [79] was extended to include claim experience data in [80]. In this later article, Young described step-by-step how an actuary/decision maker could use FL to adjust workers compensation insurance rates. Expanding on her previous article, she walked through the prescriptive and descriptive phases with a focus on targeted adjustments to filed rates and rate departures.

8604. Institute for Cognitive Science, UC San Diego 32. Kohonen T (1995) Self-organizing Maps. Springer. 33. Kovalerchuk B, Vityaev E (2000) Data mining in finance: advances in relational and hybrid methods. Kluwer. 34. Kramer M (1990) Nonlinear principal analysis using autoassociative neural networks. AIChE Journal 37(2):233–243 35. Kuan CM, Liu T (1995) Forecasting exchange rates using feedforward and recurrent neural networks. Journal of Applied Econometrics 10:347–364 36. Kumbhakar S, Knox Lovell C (2003) Stochastic frontier analysis.

Reference [19] presented a methodology for deriving an Overall Disability Index (ODI) for measuring an individual’s disability. Their approach involved the transformation of the ODI derivation problem into a multiple-criteria decision-making problem. Essentially, they used the analytic hierarchy process, a multicriteria decision making technique that uses pairwise comparisons to estimate the relative importance of each risk factor ([60]), along with entropy theory and FST, to elicit the weights among the attributes and to aggregate the multiple attributes into a single ODI measurement.

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