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This paper analyzes the InvestESG multi-agent simulation to characterize conditions leading to intertemporal social dilemmas where individual incentives conflict with collective welfare. It then applies Advantage Alignment, an opponent shaping algorithm, to influence agent learning within InvestESG, demonstrating its ability to systematically favor socially beneficial equilibria. The work provides theoretical justification for why Advantage Alignment promotes cooperation and shows that shaping agent learning can improve outcomes related to sustainability goals.
Shaping how economic agents learn can systematically favor cooperative outcomes in climate-related investment scenarios, offering a promising avenue for policy interventions.
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.
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