In this thesis, we present a novel approach to solving optimization problems that are defined on agent-based models (ABM). The approach utilizes concepts in genetic programming (GP) and is demonstrated here using an optimization problem on the Sugarscape ABM, a prototype ABM that includes spatial heterogeneity, accumulation of agent resources, and agents with different attributes. The optimization problem seeks a strategy for taxation of agent resources which maximizes total taxes collected while minimizing impact on the agents over a finite time. We demonstrate how our GP approach yields better taxation policies when compared to simple flat taxes and provide reasons why GP-generated taxes perform well. We also look at ways to improve the performance of the GP optimization method. / McAnulty College and Graduate School of Liberal Arts; / Computational Mathematics / MS; / Thesis;
| Identifer | oai:union.ndltd.org:DUQUESNE/oai:digital.library.duq.edu:etd/197176 |
| Date | 17 May 2016 |
| Creators | Garuccio, Anthony |
| Contributors | Rachael Neilan, Donald Simon, John Kern |
| Source Sets | Duquesne University |
| Detected Language | English |
| Rights | Worldwide Access; |
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