Summary of Carbon Market Simulation with Adaptive Mechanism Design, by Han Wang et al.
Carbon Market Simulation with Adaptive Mechanism Design
by Han Wang, Wenhao Li, Hongyuan Zha, Baoxiang Wang
First submitted to arxiv on: 12 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
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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 A novel adaptive mechanism design framework for simulating carbon markets using hierarchical, model-free multi-agent reinforcement learning (MARL) is proposed. The framework enables government agents to balance productivity, equality, and carbon emissions by allocating carbon credits and managing enterprises’ economic activities and carbon trading. This approach illustrates agents’ behavior comprehensively and provides a more accurate simulation of the market dynamics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers has created a new way to help countries reduce their carbon emissions. They developed a computer program that simulates how different groups, like governments and businesses, make decisions about carbon credits. The program helps these groups find a balance between making money, being fair, and reducing pollution. This could be an important tool in the fight against climate change. |
Keywords
» Artificial intelligence » Reinforcement learning
