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Neural Networks in Economic Problems

In: Selected Issues in Experimental Economics

Author

Listed:
  • Wojciech Sałabun

    (West Pomeranian University of Technology Szczecin)

  • Marcin Pietrzykowski

    (West Pomeranian University of Technology Szczecin)

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.

Suggested Citation

  • Wojciech Sałabun & Marcin Pietrzykowski, 2016. "Neural Networks in Economic Problems," Springer Proceedings in Business and Economics, in: Kesra Nermend & Małgorzata Łatuszyńska (ed.), Selected Issues in Experimental Economics, edition 1, chapter 0, pages 245-266, Springer.
    • Handle: RePEc:spr:prbchp:978-3-319-28419-4_16
    DOI: 10.1007/978-3-319-28419-4_16
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    Keywords

    Neural networks; Economics;

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