Summary of Investesg: a Multi-agent Reinforcement Learning Benchmark For Studying Climate Investment As a Social Dilemma, by Xiaoxuan Hou et al.
InvestESG: A multi-agent reinforcement learning benchmark for studying climate investment as a social dilemma
by Xiaoxuan Hou, Jiayi Yuan, Joel Z. Leibo, Natasha Jaques
First submitted to arxiv on: 15 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computers and Society (cs.CY); Multiagent Systems (cs.MA); General Economics (econ.GN)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This novel benchmark, InvestESG, combines multi-agent reinforcement learning (MARL) with Environmental, Social, and Governance (ESG) disclosure mandates to study the impact on corporate climate investments. The framework models a social dilemma where companies balance short-term profit losses against long-term benefits from reducing climate risk. Investors try to influence company behavior through their investment decisions. Companies allocate capital across mitigation, greenwashing, and resilience strategies, influencing climate outcomes and investor preferences. The benchmark is released in PyTorch and JAX formats for scalable simulations of competing incentives to mitigate climate change. Companies’ corporate mitigation efforts remain limited without ESG-conscious investors with sufficient capital, but when a critical mass prioritizes ESG, cooperation increases, reducing climate risks and enhancing long-term financial stability. Providing more information about global climate risks encourages companies to invest in mitigation, even without investor involvement. The findings align with empirical research using real-world data, highlighting MARL’s potential to inform policy by testing alternative policy and market designs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary InvestESG is a new way to study how corporations make decisions about the environment. It’s like a big game where companies try to balance making money now against saving the planet for the future. Investors can also play a role by trying to influence company behavior. The framework shows that when investors care about ESG, companies are more likely to work together and reduce their impact on the climate. The results of this study show that having enough investors who prioritize ESG makes a big difference. It encourages companies to make changes now that will benefit them in the long run. This is important because it can help us understand how to create policies that encourage corporations to take action against climate change. |
Keywords
» Artificial intelligence » Reinforcement learning
