InvestESG: A multi-agent reinforcement learning benchmark for studying climate investment as a social dilemma
Xiaoxuan Hou, Jiayi Yuan, Joel Z. Leibo, Natasha Jaques

TL;DR
InvestESG introduces a multi-agent reinforcement learning benchmark to analyze how ESG disclosure mandates influence corporate climate investments and social dilemmas, revealing the importance of investor influence and information in promoting mitigation efforts.
Contribution
This work presents a novel MARL benchmark for studying climate investment as a social dilemma, enabling scalable simulations of corporate and investor behaviors under ESG policies.
Findings
Investor influence is crucial for corporate mitigation efforts.
More climate risk information encourages increased mitigation investments.
MARL can effectively simulate socio-economic climate policy challenges.
Abstract
InvestESG is a novel multi-agent reinforcement learning (MARL) benchmark designed to study the impact of Environmental, Social, and Governance (ESG) disclosure mandates on corporate climate investments. The benchmark models an intertemporal social dilemma where companies balance short-term profit losses from climate mitigation efforts and long-term benefits from reducing climate risk, while ESG-conscious investors attempt to influence corporate behavior through their investment decisions. Companies allocate capital across mitigation, greenwashing, and resilience, with varying strategies influencing climate outcomes and investor preferences. We are releasing open-source versions of InvestESG in both PyTorch and JAX, which enable scalable and hardware-accelerated simulations for investigating competing incentives in mitigate climate change. Our experiments show that without ESG-conscious…
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Code & Models
Videos
Taxonomy
TopicsFinTech, Crowdfunding, Digital Finance · Climate Change Policy and Economics · Economic theories and models
MethodsALIGN
