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

Constructing and deconstructing bias: modeling privilege and mentorship in agent-based simulations