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.

翻译：应对气候变化需要全球协调，然而理性的经济主体往往优先考虑即时收益而非集体福祉，从而导致社会困境。InvestESG 是近期提出的一种多智能体模拟框架，用于刻画气候风险下投资者与企业之间的动态交互。我们对 InvestESG 呈现跨期社会困境的条件进行了形式化刻画，推导出个体激励与集体福利发生背离的理论阈值。在此基础上，我们应用 Advantage Alignment——一种在一般和博弈中被证明有效的可扩展对手塑造算法——来影响 InvestESG 中智能体的学习过程。我们从理论上阐释了 Advantage Alignment 如何通过将学习动态偏向合作性结果，从而系统性地促进社会有益均衡。研究结果表明，策略性地塑造经济主体的学习过程能够产生更优的结果，这可为设计政策机制提供参考，以更好地使市场激励与长期可持续性目标相一致。