深度机制设计:学习社会和经济政策以造福人类
Andrea Tacchetti1, Raphael Koster1, Jan Balaguer1
1Google DeepMind, London EC4A 3TW.
概括
人工智能,特别是具有强化学习 (RL) 的深度神经网络,正在推进机制设计. 这种人工智能方法创造了更好的定价,税收和投票系统,同时解决了伦理方面的考虑.
科学领域:
- 社会科学 社会科学 社会科学
- 经济科学 经济科学
- 政治科学 政治科学是指政治学.
- 人工智能研究 人工智能研究
背景情况:
- 人类社会依赖于协调的机制来实现关键功能,如定价,税收和选举过程.
- 为社会利益设计有效和强大的机制是社会,经济和政治学科面临的根本挑战.
- 传统的机制设计方法正在被现代计算工具所增强.
研究的目的:
- 探索人工智能 (AI) 的应用,特别是通过强化学习 (RL) 训练的深度神经网络,用于设计改进的社会机制.
- 审查机器学习在创造更有效的拍卖,最佳税收政策和流行的再分配策略中的使用.
- 讨论模拟人类偏好的复杂性,并将人工智能驱动的机制与各种人类价值观保持一致.
主要方法:
- 使用强化学习 (RL) 训练的深度神经网络进行机制设计.
- 应用机器学习技术来优化经济和政治机制.
- 基于人类用户的反和偏好来模拟和评估机制的性能.
主要成果:
- 人工智能在设计有效的拍卖和最佳税收政策方面取得了成功.
- 确定了人工智能发现人类用户喜欢的再分配政策的能力.
- 突出了准确捕捉和建模各种人类偏好的机制对齐的挑战.
结论:
- 深度强化学习提供了一个强大的新工具包,用于在社会,经济和政治背景下推进机制设计.
- 对人类偏好和伦理考虑的准确建模对于人工智能在机制设计中的成功和安全实施至关重要.
- "深度机制设计"的未来研究必须优先考虑安全,伦理和与人类价值观的协调.
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