By Anna Gil-lafuente, Jose Merigo

This booklet offers probably the most contemporary advancements in Computational Intelligence utilized to enterprise and economics awarded on the MS'10 overseas convention, Barcelona, 15 17 July, 2010. It offers a number of new theoretical developments and quite a lot of purposes in several enterprise and financial components together with accounting, finance, administration, advertising and marketing, activities, tourism, economics and politics, and in addition a few purposes comparable with engineering and modeling and simulation.

This ebook is especially helpful for researchers and graduate scholars drawn to pursuing examine in enterprise and economics with an orientation to trendy options for facing uncertainty resembling these similar with modeling and simulation and computational intelligence.

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Additional resources for Computational Intelligence in Business and Economics: Proceedings of the Ms'10 International Conference (World Scientific Proceedings Series on ... Science): Barcelona, Spain 15-17 July 2010

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Kennedy, “Quanto Pricing with Copulas,” Journal of Derivatives, Vol. 12, no. 1, pp. 26-45, 2004. 4. U. Cherubini and E. Luciano, “Bivariate Option Pricing with Copulas”, Applied Mathematical Finance, Vol. 8, pp. 69-85, 2002. 26 5. R. T. Clemen, and T. Reilly, “Correlations and Copulas for Decision and Risk Analysis,” Management Science, Vol. 45, no. 2, pp. 208-224, 1999. 6. E. W. Frees, and E. Valdez, “Understanding Relationships Using Copulas,” North American Actuarial Journal, Vol. 2, no. 1, pp.

Fuzzy probability density function and fuzzy cumulative distribution function. 2. Hybrid approaches to propagating randomness and fuzziness in risk assessment ~ ~ The idea is to find the output of a model g X 1 , … , X n , X 1 , … , X m that has both random variables X 1 , … , X n , given by probabilistic distributions, and ~ ~ fuzzy variables X 1 , … , X m , for the inputs. g. possibility to probability transformation). Guyonnet et al. (2003) first proposed a “hybrid approach” with both fuzzy and random types of uncertainty without transforming one type to another.

3. Set Bayesian Updating Using Copulas In Bayesian approach the joint distribution need to be specified for arriving at the posterior distribution. Since the joint distribution is defined by the copula, Bayesian updating can be done using copula functions. The joint distribution function from a copula C(u,v) is c ( u , v ) = ∂ 2 C ( u , v ) / ∂u∂v . In the proposed copula approach, from a Bayesian view, the conditional distribution of v u is ∂ c (v ) = P[V ≤v U ≤ u ] = C (u , v ) . The above conditional distribution u ∂u cu(v) is the posterior distribution of v.

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