5 research outputs found
Revealing Robust Oil and Gas Company Macro-Strategies using Deep Multi-Agent Reinforcement Learning
The energy transition potentially poses an existential risk for major
international oil companies (IOCs) if they fail to adapt to low-carbon business
models. Projections of energy futures, however, are met with diverging
assumptions on its scale and pace, causing disagreement among IOC
decision-makers and their stakeholders over what the business model of an
incumbent fossil fuel company should be. In this work, we used deep multi-agent
reinforcement learning to solve an energy systems wargame wherein players
simulate IOC decision-making, including hydrocarbon and low-carbon investments
decisions, dividend policies, and capital structure measures, through an
uncertain energy transition to explore critical and non-linear governance
questions, from leveraged transitions to reserve replacements. Adversarial play
facilitated by state-of-the-art algorithms revealed decision-making strategies
robust to energy transition uncertainty and against multiple IOCs. In all
games, robust strategies emerged in the form of low-carbon business models as a
result of early transition-oriented movement. IOCs adopting such strategies
outperformed business-as-usual and delayed transition strategies regardless of
hydrocarbon demand projections. In addition to maximizing value, these
strategies benefit greater society by contributing substantial amounts of
capital necessary to accelerate the global low-carbon energy transition. Our
findings point towards the need for lenders and investors to effectively
mobilize transition-oriented finance and engage with IOCs to ensure responsible
reallocation of capital towards low-carbon business models that would enable
the emergence of fossil fuel incumbents as future low-carbon leaders