Higher Order Neural Network Architectures for Agent-Based Computational Economics and Finance

Higher Order Neural Network Architectures for Agent-Based Computational Economics and Finance

John Seiffertt (Missouri University of Science and Technology, USA) and Donald C. Wunsch II (Missouri University of Science and Technology, USA)
Copyright: © 2012 |Pages: 15
DOI: 10.4018/978-1-60960-818-7.ch207
OnDemand PDF Download:
$30.00
List Price: $37.50

Abstract

As the study of agent-based computational economics and finance grows, so does the need for appropriate techniques for the modeling of complex dynamic systems and the intelligence of the constructive agent. These methods are important where the classic equilibrium analytics fail to provide sufficiently satisfactory understanding. In particular, one area of computational intelligence, Approximate Dynamic Programming, holds much promise for applications in this field and demonstrate the capacity for artificial Higher Order Neural Networks to add value in the social sciences and business. This chapter provides an overview of this area, introduces the relevant agent-based computational modeling systems, and suggests practical methods for their incorporation into the current research. A novel application of HONN to ADP specifically for the purpose of studying agent-based financial systems is presented.
Chapter Preview
Top

Background

The fundamental Agent-Based Computational Economics framework structure is overviewed in Testafasion (2006) and will be reviewed here. The particular formulation of the agent problem proposed in this chapter is based on the presentation in Chiarella (2003) and will be discussed following the general overview. Finally, other supporting literature will be surveyed to help solidify the main ideas of this section and to guide the reader in other directions of possible research interest.

Complete Chapter List

Search this Book:
Reset