Towards Sustainable Investment Policies Informed by Opponent Shaping
This work addresses global coordination in climate policy for economic actors, but it is incremental as it builds on existing algorithms in a new simulation context.
The paper tackles the problem of climate change coordination by modeling investor-company interactions in a multi-agent simulation (InvestESG) and applies an opponent shaping algorithm (Advantage Alignment) to bias learning toward cooperative outcomes, demonstrating that shaping agent learning can improve alignment with sustainability goals.
Addressing climate change requires global coordination, yet rational economic actors often prioritize immediate gains over collective welfare, resulting in social dilemmas. InvestESG is a recently proposed multi-agent simulation that captures the dynamic interplay between investors and companies under climate risk. We provide a formal characterization of the conditions under which InvestESG exhibits an intertemporal social dilemma, deriving theoretical thresholds at which individual incentives diverge from collective welfare. Building on this, we apply Advantage Alignment, a scalable opponent shaping algorithm shown to be effective in general-sum games, to influence agent learning in InvestESG. We offer theoretical insights into why Advantage Alignment systematically favors socially beneficial equilibria by biasing learning dynamics toward cooperative outcomes. Our results demonstrate that strategically shaping the learning processes of economic agents can result in better outcomes that could inform policy mechanisms to better align market incentives with long-term sustainability goals.