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NSF ADVANCE has been instrumental in supporting institutional practices leading to the increased representation of women in STEM. However, research suggests institutional culture and practices evolve slowly, and much progress remains to create a collaborative and supportive work environment where women scientists, mathematicians, and engineers can thrive, particularly those with intersectional identities, including women of color and women with caregiving responsibilities. A partnership of four midwestern research universities joined together in late 2019 to adapt, design, implement, and assess the impact of a coordinated suite of programs intended to enhance the career success of women and underrepresented STEM faculty. The programs promote mentoring, male advocacy, and informed and intentional leadership as integral to campus culture, and foster community and cross-institutional data-based collaboration. This paper summarizes the programs designed and implemented to improve retention and job satisfaction of women in STEM fields with a focus on the intersectionalities of women of color and women with family responsibilities, including navigating the challenges presented by the COVID-19 pandemic, by creating support networks for these faculty. 

We introduce a sequential Bayesian binary hypothesis testing problem under social learning, termed selfish learning, where agents work to maximize their individual rewards. In particular, each agent receives a private signal and is aware of decisions made by earlier-acting agents. Beside inferring the underlying hypothesis, agents also decide whether to stop and declare, or pass the inference to the next agent. The employer rewards only correct responses and the reward per worker decreases with the number of employees used for decision making. We characterize decision regions of agents in the infinite and finite horizon. In particular, we show that the decision boundaries in the infinite horizon are the solutions to a Markov Decision Process with discounted costs, and can be solved using value iteration. In the finite horizon, we show that team performance is enhanced upon appropriate incentivization when compared to sequential social learning. 

This work explores sequential Bayesian binary hypothesis testing in the social learning setup under expertise diversity. We consider a two-agent (say advisor-learner) sequential binary hypothesis test where the learner infers the hypothesis based on the decision of the advisor, a prior private signal, and individual belief. In addition, the agents have varying expertise, in terms of the noise variance in the private signal. Under such a setting, we first investigate the behavior of optimal agent beliefs and observe that the nature of optimal agents could be inverted depending on expertise levels. We also discuss suboptimality of the Prelec reweighting function under diverse expertise. Next, we consider an advisor selection problem wherein the belief of the learner is fixed and the advisor is to be chosen for a given prior. We characterize the decision region for choosing such an advisor and argue that a learner with beliefs varying from the true prior often ends up selecting a suboptimal advisor.