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

Author

John E. Seiffertt IV, Missouri University of Science and TechnologyFollow
Donald C. Wunsch, Missouri University of Science and TechnologyFollow

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.

Recommended Citation

J. E. Seiffertt and D. C. Wunsch, "Higher Order Neural Network Architectures for Agent-based Computational Economics and Finance," Artificial Higher Order Neural Networks for Economics and Business, pp. 79 - 93, IGI Global, Jan 2008.

Department(s)

Electrical and Computer Engineering

International Standard Book Number (ISBN)

978-1599048970

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2008 IGI Global, All rights reserved.

Publication Date

01 Jan 2008