Neural Networks in Economic Problems

Wojciech Sałabun () and Marcin Pietrzykowski ()
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Wojciech Sałabun: West Pomeranian University of Technology Szczecin
Marcin Pietrzykowski: West Pomeranian University of Technology Szczecin

Chapter Chapter 16 in Selected Issues in Experimental Economics, 2016, pp 245-266 from Springer

Abstract: Abstract In most of experimental research, we generally need to solve a problem of classification, regression or time-series forecasting. On the other hand, artificial neural networks are universal and highly flexible function approximators. Neural networks are primarily used in the fields of cognitive science and engineering. In recent years, the use of neural network applications in economics has dramatically increased. Today, neural networks are a basic tool in experimental economics. However, the large number of parameters that must be selected to develop a neural network model has meant that the design process still involves much effort. The objective of this chapter is to provide a practical introductory guide in the design of a neural network for solving problems in experimental economics. Our proposed procedure to design a neural network to solve economic experiments uses Matlab® environment. The approach is explained, and the chapter includes a discussion of trade-offs in parameter selection, as well as some common pitfalls.

Keywords: Neural networks; Economics (search for similar items in EconPapers)
Date: 2016
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Persistent link: https://EconPapers.repec.org/RePEc:spr:prbchp:978-3-319-28419-4_16

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DOI: 10.1007/978-3-319-28419-4_16

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