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感知干预改善了学习代理人的统计歧视
Edgar A Duéñez-Guzmán1, Ramona Comanescu1, Yiran Mao1
1Google DeepMind, Google UK Ltd., London EC4A 3TW, United Kingdom.
技术可以帮助减少社会伙伴选择中的偏见. 感知干预通过突出相关信息,增强信息处理能力和减少对虚假相关性的依赖来改善决策.
科学领域:
- 计算社会科学 计算社会科学
- 行为经济学是一种行为经济学.
- 人工智能的人工智能
背景情况:
- 选择社会合作伙伴需要平衡信息评估成本和决策质量.
- 信息处理的局限性会导致在选择合作伙伴时出现偏见.
- 技术可以改变特征的突出性,潜在地调节这些偏见.
研究的目的:
- 通过游戏理论和多代理强化学习来研究伴侣选择中的偏见的出现.
- 探索技术感知干预如何减轻这些偏见.
主要方法:
- 利用游戏理论和多代理强化学习 (MARL) 来建模社会伙伴选择.
- 实施感知干预作为代理人和他们的环境之间的技术层.
- 评估了干预和不干预的偏见和决策表现的变化.
主要成果:
- 感知干预增加了结果相关特征的突出性,减少了伴侣选择决策中的偏见.
- 接受干预的代理人对虚假相关的特征的依赖程度较低.
- 干预机制增强了代理人的有效信息处理能力.
结论:
- 技术干预可以有效地减少社会决策中的偏见.
- 马尔是建模复杂的社会行为和测试干预措施的宝贵工具.
- 优化特征突出性是改善合作伙伴选择结果的有希望的策略.
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