Abstract
As the prevalence of automation increases, creativity will play an ever-larger role in the tasks humans accomplish. In this dissertation, we first explore empirically how a social net-work's connectivity patterns and creative outcomes are affected by factors such as creative performance, popularity, and identity attributes of people. Accordingly, we seek to devise intelligent intervention approaches that can harness the empirical insights to optimize network-wide creative outcomes. We envision our work to inform not only managerial and algorithmic decision-making, but also public policy as it relates to helping humans become more creatively productive in a social network.
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