Title page for ETD etd-091399-112633


Type of Document Dissertation
Author Larkin, George Richard
Author's Email Address dlarkin@westga.edu
URN etd-091399-112633
Title Policy Subsystem Portfolio Management: A Neural Network Model of the Gulf of Mexico Program
Degree PhD
Department Public Administration and Public Affairs
Advisory Committee
Advisor Name Title
Dickey, John. W. Committee Chair
Dudley, Larkin S. Committee Member
Kopfler, Frederick C. Committee Member
Wamsley, Gary L. Committee Member
Wolf, James F. Committee Member
Keywords
  • policy subsystems
  • public policy
  • policy networks
  • neural networks
Date of Defense 1999-08-16
Availability unrestricted
Abstract
This study provides insights into the behavior of an environmental policy subsystem. The study uses neural network theory to model the Gulf of Mexico Program's allocation of implementation funds. The Gulf of Mexico Program is a prototype effort to institutionalize a policy subsystem. A project implementation fund is at the core of the Gulf of Mexico Program. The United States Environmental Protection Agency provides the implementation fund and the Mexico Program Office (GMPO) administers it. The GMPO uses the implementation fund to encourage other federal, state, local, and private organizations to undertake projects designed to improve the environmental quality and economic vitality of the Gulf of Mexico and its surrounding region. The implementation fund constitutes a program "portfolio" and is the Gulf of Mexico Program's primary means of influencing policy. The way a policy subsystem manages its program portfolio through the allocation of fiscal resources provides important insights about its priority concerns and dominant actors.

The benefits of this study are threefold. First, the study offers an initial systematic description and analysis of the Gulf of Mexico Program and its policy implementation process. Second, using the Gulf of Mexico Program as a prototype, the study sheds new light on why and how policy subsystems formulate and implement policy. Finally, the study provides a means to assess the value of neural network theory as a technique for modeling and analyzing policy subsystem behavior.

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