Improving Behavior Prediction Accuracy by Using Machine Learning for Agent-Based Simulation

摘要

This study models an integration between agent-based simulation and machine learning in order to achieve comprehensive behavior prediction. The model is applied to the case of customer churning in a subscription-based business. Providing a good model for behavior prediction requires dynamic simulation based on social structure. In this study, we first executed an agent-based simulation to capture the dynamic structure of human behavior. Next, we conducted machine learning to classify human behavior using a classification algorithm. Finally, we verified the agent-based simulation and machine learning results by comparing the accuracy of both models. Based on the agent-based simulation results, we provide some recommendations to improve the accuracy of agent-based simulation based on the classification results from machine-learning procedures.