PhD Dissertation: Computational Modeling of Systems

Dissertation Summary

This dissertation develops computational models to simulate complex systems in healthcare settings. It relies on multi-agent simulation and agent-based modeling to improve resource allocation in hospitals.

Background

Health systems face growing challenges in allocating resources efficiently, especially with rising demand and financial constraints. Computational modeling makes it possible to test different scenarios without real-world risk.

Dissertation Objectives

• Develop a multi-agent simulation model to predict patient loads
• Design a resource-allocation algorithm based on reinforcement learning
• Compare the proposed model's performance with traditional approaches
• Provide practical recommendations for health-sector decision makers

Computational modeling is not just a technical tool; it is a way of thinking that helps us understand complex systems more deeply.

Methodology

The dissertation used a mixed methodology combining Agent-Based Modeling with Discrete Event Simulation. The models were implemented in Python and NetLogo.

Results and Recommendations

The results showed that the proposed model outperformed traditional methods by 23% in resource-allocation efficiency. The study recommends adopting computational modeling as a strategic tool for managing health systems.