Revealing Robust Oil and Gas Company Macro-Strategies using Deep Multi-Agent Reinforcement Learning
Dylan Radovic, Lucas Kruitwagen, Christian Schroeder de Witt, Ben, Caldecott, Shane Tomlinson, Mark Workman

TL;DR
This paper uses deep multi-agent reinforcement learning to simulate IOC decision-making under energy transition uncertainty, revealing that early adoption of low-carbon strategies leads to superior performance and societal benefits.
Contribution
It introduces a novel simulation framework employing adversarial multi-agent reinforcement learning to analyze IOC strategies during energy transition uncertainties.
Findings
Robust low-carbon strategies outperform business-as-usual approaches.
Early transition-oriented moves lead to better performance regardless of demand projections.
Transition strategies contribute significantly to societal and environmental goals.
Abstract
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…
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Taxonomy
TopicsMarket Dynamics and Volatility · Global Energy and Sustainability Research · Private Equity and Venture Capital
Methodsfail
