Bias exists in how we pick leaders, who we perceive as being influential, and who we interact with, not only in society, but in organizational contexts. Drawing from leadership emergence and social influence theories, we investigate potential interventions that support diverse leaders. Using agent-based simulations, we model a collective search process on a fitness landscape. Agents combine individual and social learning, and are represented as a feature vector blending relevant (e.g., individual learning characteristics) and irrelevant (e.g., race or gender) features. Agents use rational principles of learning to estimate feature weights on the basis of performance predictions, which are used to dynamically define social influence in their network. We show how biases arise based on historic privilege, but can be drastically reduced through the use of an intervention (e.g. mentorship). This work provides important insights into the cognitive mechanisms underlying bias construction and deconstruction, while pointing towards real-world interventions to be tested in future empirical work.

翻译：偏见存在于我们如何选拔领导者、谁被认为具有影响力、以及我们与谁互动中，不仅在社会层面，也在组织情境中。基于领导力涌现与社会影响理论，我们探究了支持多元化领导者的潜在干预措施。通过基于智能体的模拟，我们在适应度景观上对集体搜索过程进行建模。智能体结合个体学习与社会学习，并以融合相关特征（如个体学习特性）与无关特征（如种族或性别）的特征向量表示。智能体运用理性学习原理，根据绩效预测估计特征权重，并据此动态定义其网络中的社会影响。我们展示了偏见如何基于历史特权产生，但通过干预措施（如导师制）可显著减少。这项研究为偏见构建与解构背后的认知机制提供了重要见解，同时指出了未来实证研究中可检验的现实干预措施。