Explainability and Interpretability in Decision Trees and Agent Based Modelling When Approximating Financial Time Series. A Matter of Balance with Performance

The paper discusses the notions of explainability and interpretability when using decision tree learning and agent based modeling to approximate financial time series. And how they related to the selected learning algorithm. As experimental context, the LFABS system for agent based modeling is used together with C4.5 for decision tree learning. The paper proposes the following definitions for interpretability: being able to make sense of a learning system output. And explainability: understanding how that output was generated. The study’s goal is achieved by comparing the different knowledge representations used by the two systems.