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：偏见在我们选择领导者、我们认为有影响力的人以及我们与谁互动等方面存在，不仅存在于社会，也存在于组织环境中。借鉴领导力出现和社会影响力理论，我们探究了可能支持多元化领导者的干预措施。使用智能体模拟，我们对适应性景观上的集体搜索过程进行了建模。智能体结合个体和社会学习，并以特征向量表示，融合相关特征（例如个体学习特性）和不相关特征（例如种族或性别）。智能体使用学习的理性原则，通过性能预测来估计特征权重，并用于动态定义他们网络中的社会影响力。我们展示了基于历史特权而产生的偏见如何产生，但大大可以通过一种干预措施（例如导师制度）减少。这项工作为我们提供了关于偏见构建和解构的认知机制的重要洞察，同时指向了未来实证研究中将要测试的现实世界干预措施。