Reassessing Machine Learning for Decision Analysis: Reinforcement Learning Vs. Least Squares Monte Carlo for Real Option Decisions in Energy Transition

In this paper, we present an overview of solutions to real option valuation (ROV) problems, using a simple oil field development project as an example. We categorize the solution tools into two major groups: learning and planning. Least squares Monte Carlo (LSM) represents the planning method, whereas reinforcement learning (RL) represents the learning approach. We discuss the application of each method in detail to evaluate the effectiveness of RL, a state-of-the-art technique in machine learning, compared with conventional solution methods such as LSM in the context of ROV problems. RL, as a state-of-the-art method for sequential decision making (SDM), has demonstrated strong success in solving complex problems in finance, including trading, option pricing, and portfolio management, where decisions are continuous and adaptive. However, our findings suggest that whereas RL has the potential to handle ROV, it is often too sophisticated and unnecessary for the typical structure of ROV and managerial flexibility analyses, where simpler methods such as LSM are usually sufficient. Particularly given the common characteristics of problems in the ROV context, as exemplified by the case study here, the features that highlight RL's strengths and were key to its development are not present in the ROV context. Therefore, careful consideration is needed when choosing the appropriate solution method.