再抽样减少了在实验性社交网络中的偏见放大
Mathew D Hardy1, Bill D Thompson2, P M Krafft3
1Department of Psychology, Princeton University, Princeton, NJ, USA. mdhardy@princeton.edu.
Nature human behaviour
|October 16, 2023
概括
社交网络扩大了用户在决策中的偏见. 一个简单的算法调整可以减轻这种偏见放大,促进不同的观点,同时保持信息共享的好处.
科学领域:
- 社会心理学 社会心理学
- 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.
背景情况:
- 大规模的社交网络被假设通过放大个人偏见来增加社会两极分化.
- 这些数字平台的复杂性掩盖了驱动偏见放大的具体机制,并阻碍了开发有效的缓解策略.
研究的目的:
- 调查社交网络传播对受控环境中的动机偏差放大因果关系的影响.
- 开发和评估一种计算策略,以减轻社交网络中的偏见放大.
主要方法:
- 在受控的实验室条件下,使用简单的人工决策任务进行了一项大型行为实验.
- 参与者被分配到40个独立进化的群体的社交网络或非社会条件中.
- 受贝叶斯统计学启发的内容选择算法调整旨在促进对观点的代表性抽样.
主要成果:
- 与非社交参与者相比,参与社交网络导致偏见决策率显著增加.
- 拟议的算法调整有效地减少了两个大型实验中的偏差放大.
- 缓解策略成功地保持了信息共享的好处,同时减少了偏见.
结论:
- 社会网络结构显然在决策任务中放大了动机偏见.
- 计算衍生策略可以有效地抵消社交网络中的偏见放大.
- 这种方法为增强在线信息生态系统的健康提供了一个有希望的方法.
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