Good catch on the drawbacks of ML, alee. Well, I would say, ML’s ability to “skip theory” is not a bad thing in practice, especially, when there is no available theory or maybe our theory is incomplete. The complex, dynamic world we live in poses tremendous challenges to any models (not just ML). Ideally, we would like a computational model that can learn and adapt by itself to its environment. This, in fact, motives ML people to develop more intelligent and robust models, i.e., online, active, transfer and reinforcement learning. The “black box” ML model is certainly not suitable for decision making. But, lately, there is a strong trend in ML community that focuses on interpretability and causality, i.e., causal learning. Finally, bias is a common issue for many modeling paradigms. In ML, it’s often referred to overfitting. But, there is a widely used solution (although not perfect still limited by data): cross-validation. What is your opinion on the pros/cons of ABM? alee. I would like to hear more on the downside of ABM. I guess our discussion in this thread gets more and more interesting.
The Java source code (developed in Repast) for my data-driven ABM solar paper is located at: