Modeling the Components of an Economy as a Complex Adaptive System
Abstract
Complex systems science is relatively new and has not been widely applied to the field of economics. Much of current economic theory relies on principles of constrained optimization and fails to see economic variables as part of an interconnected network. While tools for forecasting economic indicators are based primarily on autoregressive techniques, these techniques may not be well-suited to predicting the future performance of highly volatile data sets such as the stock market. This research portrays the stock market as one component of a networked system of economic variables, with the federal funds rate acting as an exogenous influencing factor. Together these components form a complex adaptive system having nonlinear dynamics. The network is modeled using a system of differential equations, which are based on an expanded form of the logistic differential equation for populations with carrying capacities. An inverse problem is solved using the method of least squares, and the resulting coefficients are examined to determine the strength of relationships between the network components. The fitted model is then evaluated for adequacy and Euler's Forward Method is employed to predict the long-run behavior of the network. With this as a baseline, the research investigates several hypothetical scenarios to determine how the system reacts to changes in interest rates. Contributions and implications of the model are addressed in the context of U.S. national defense.
Document Details
- Document Type
- Technical Report
- Publication Date
- Mar 24, 2016
- Accession Number
- AD1053995
Entities
People
- Richard J. Mickelsen
Organizations
- Air Force Institute of Technology