arXiv cs.MA· Panayiotis Danassis, Aris Filos-Ratsikas, Haipeng Chen, Milind Tambe, Boi Faltings·· 13 小时前AI 评分31
用深度强化学习智能体为生产市场定价，以应对负外部性与可持续性
AI-driven Prices for Externalities and Sustainability in Production Markets
AI 导读
研究提出用深度强化学习“政策制定者”智能体在其他学习型智能体环境中计算市场价格与分配，可围绕可持续性、资源浪费、公平性及买卖双方福利等多目标调价。在稀缺资源环境中，该智能体维持资源可持续性的表现显著优于市场均衡结果。
正文 · 原文
Abstract:Traditional competitive markets do not account for negative externalities; indirect costs that some participants impose on others, such as the cost of over-appropriating a common-pool resource (which diminishes future stock, and thus harvest, for everyone). Quantifying appropriate interventions to market prices has proven to be quite challenging. We propose a practical approach to computing market prices and allocations via a deep reinforcement learning policymaker agent, operating in an environment of other learning agents. Our policymaker allows us to tune the prices with regard to diverse objectives such as sustainability and resource wastefulness, fairness, buyers' and sellers' welfare, etc. As a highlight of our findings, our policymaker is significantly more successful in maintaining resource sustainability, compared to the market equilibrium outcome, in scarce resource environments.
| Comments: | A preliminary version was accepted to the 22nd International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2023). This full version has been accepted at the journal of Autonomous Agents and Multi-Agent Systems (Volume 40, article number 46, 2026) |
| Subjects: | Multiagent Systems (cs.MA); Artificial Intelligence (cs.AI); Computer Science and Game Theory (cs.GT) |
| Cite as: | arXiv:2106.06060 [cs.MA] |
| (or arXiv:2106.06060v5 [cs.MA] for this version) | |
| https://doi.org/10.48550/arXiv.2106.06060 arXiv-issued DOI via DataCite |
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| Related DOI: | https://doi.org/10.1007/s10458-026-09768-2
DOI(s) linking to related resources |
Submission history
From: Panayiotis Danassis [view email]
[v1]
Thu, 10 Jun 2021 21:26:17 UTC (730 KB)
[v2]
Fri, 29 Apr 2022 10:47:40 UTC (528 KB)
[v3]
Thu, 12 Jan 2023 20:46:31 UTC (585 KB)
[v4]
Fri, 21 Aug 2026 12:54:23 UTC (561 KB)
[v5]
Wed, 21 Oct 2026 17:04:02 UTC (565 KB)
来源：arXiv cs.MA · arxiv.org