Neuroeconomics has the potential to fundamentally change the way economics is done. This article identifies the ways in which this will occur, pitfalls of this approach, and areas where progress has already been made. The value of neuroeconomics studies for social policy lies in the quality, replicability, and relevance of the research produced. While most economists will not contribute to the neuroeconomics literature, we contend that most economists should be reading these studies.
1Abstract-Neural network ensemble is an approach based on cooperative usage of many neural networks for problem solving. Often this approach enables to solve problem more efficiently than approach where only one network is used. The two major stages of the neural network ensemble construction are: design and training component networks, combining of the component networks predictions to produce the ensemble output. In this paper, a probability-based method is proposed to accomplish the first stage. Although this method is based on the genetic algorithm, it requires fewer parameters to be tuned. A method based on genetic programming is proposed for combining the predictions of component networks. This method allows us to build nonlinear combinations of component networks predictions providing more flexible and adaptive solutions. To demonstrate robustness of the proposed approach, its results are compared with the results obtained using other methods.
This paper considers a hybrid approach to the identification of dynamical systems based on a self-configuring genetic programming algorithm and a differential evolution method. The value of this approach is in the automatic determination of the order, structure and parameters of differential equation, i.e., a model of a dynamic system. The application of the differential evolution method can significantly increase the accuracy of the resulting model confirmed by the results of the experiments presented in this paper.
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