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Updated: Sep 19, 2025

The HoneyComb Paradigm for Research on Collective Human Behavior
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塔布拉拉萨的代理人显示出新兴的群体内行为.

Raphael Köster1, Edgar A Duéñez-Guzmán1, William A Cunningham2,3,4

  • 1Google DeepMind, London EC4A 3TW, United Kingdom.

Proceedings of the National Academy of Sciences of the United States of America
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PubMed
概括

人工智能代理人,即使没有先天的社会偏见,也会产生群体偏见. 这些偏见来自经验和奖励的关联,可以通过足够的积极互动来克服,尽管忘记需要比学习更长的时间.

关键词:
在这里,我们可以看到AIAIAI.认知模型是认知模型.群体偏见 群体偏见 群体偏见多种代理强化学习的学习.社会心理学 社会心理学

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科学领域:

  • 认知科学 认知科学
  • 人工智能的人工智能
  • 心理学 心理学 心理学

背景情况:

  • 群体偏见理论经常提出从一般认知过程中产生的先天性倾向或新兴性质.
  • 由于难以控制观察行为中的先天偏见,因此区分这些起源一直是具有挑战性的.

研究的目的:

  • 通过使用人工智能 (AI) 在受控环境中调查群体偏见的起源.
  • 为了确定"白板"学习代理人是否仅仅从经验中发展偏见.

主要方法:

  • 利用深度强化学习代理,从原始感官输入学习到奖励驱动的行动.
  • 控制了人工智能代理人的一生经验,将它们作为'白板' (白板) 开始,没有内在的社会偏见.
  • 在代理人的环境中引入任意的群体差异 (例如颜色).

主要成果:

  • 人工智能代理人基于任意的视觉差异 (颜色) 开发了群体偏见.
  • 偏见的获得与熟悉的经验和视觉模式与奖励的关联有关.
  • 形成的偏见并不是输入的静态反映;它可以通过足够的积极经验来克服,但忘记是比获取更慢的.

结论:

  • 群体偏见可以从非专业化的学习过程和环境相互作用中出现,即使没有先天的倾向.
  • 人工智能模型提供了一个强大的工具,用于测试心理理论对群体行为的细粒度预测.
  • 这项研究提供了一种新的计算方法,以了解社会偏见背后的基本机制.