McAnulty College and Graduate School of Liberal Arts
Agent Based Modeling, Genetic Programing, Optimal Control, Sugarscape, Taxation
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.
Garuccio, A. (2016). A Genetic Programming Approach to Solving Optimization Problems on Agent-Based Models (Master's thesis, Duquesne University). Retrieved from https://dsc.duq.edu/etd/569