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Abstract Diversity of practice is widely recognized as crucial to scientific progress. If all scientists perform the same tests in their research, they might miss important insights that other tests would yield. If all scientists adhere to the same theories, they might fail to explore other options which, in turn, might be superior. But the mechanisms that lead to this sort of diversity can also generate epistemic harms when scientific communities fail to reach swift consensus on successful theories. In this paper, we draw on extant literature using network models to investigate diversity in science. We evaluate different mechanisms from the modeling literature that can promote transient diversity of practice, keeping in mind ethical and practical constraints posed by real epistemic communities. We ask: what are the best ways to promote an appropriate amount of diversity of practice in scientific communities? 

Abstract I use network models to simulate social learning situations in which the dominant group ignores or devalues testimony from the marginalized group. I find that the marginalized group ends up with several epistemic advantages due to testimonial ignoration and devaluation. The results provide one possible explanation for a key claim of standpoint epistemology, the inversion thesis, by casting it as a consequence of another key claim of the theory, the unidirectional failure of testimonial reciprocity. Moreover, the results complicate the understanding and application of previously discovered network epistemology effects, notably the Zollman effect (Zollman, 2007, 2010). 

Abstract We often need to have beliefs about things on which we are not experts. Luckily, we often have access to expert judgements on such topics. But how should we form our beliefs on the basis of expert opinion when experts conflict in their judgments? This is the core of the novice/2-expert problem in social epistemology. A closely related question is important in the context of policy making: how should a policy maker use expert judgments when making policy in domains in which she is not herself an expert? This question is more complex, given the messy and strategic nature of politics. In this paper we argue that the prediction with expert advice (PWEA) framework from machine learning provides helpful tools for addressing these problems. We outline conditions under which we should expert PWEA to be helpful and those under which we should not expect these methods to perform well. 

The COVID-19 pandemic created enormously difficult decisions for individuals trying to navigate both the risks of the pandemic and the demands of everyday life. Good decision making in such scenarios can have life and death consequences. For this reason, it is important to understand what drives risk assessments during a pandemic, and to investigate the ways that these assessments might deviate from ideal risk assessments. In a preregistered online study of U.S. residents (N = 841) using two blocks of vignettes about potential COVID exposure scenarios, we investigated the effects of moral judgment, importance, and intentionality on COVID infection risk assessments. Results demonstrate that risk judgments are sensitive to factors unrelated to the objective risks of infection. Specifically, activities that are morally justified are perceived as safer while those that might subject people to blame or culpability, are seen as riskier, even when holding objective risk fixed. Similarly, unintentional COVID exposures are judged as safer than intentional COVID exposures. While the effect sizes are small, these findings may have implications for public health and risk communications, particularly if public health officials are themselves subject to these biases. 

Why do bad methods persist in some academic disciplines, even when they have been widely rejected in others? What factors allow good methodological advances to spread across disciplines? In this paper, we investigate some key features determining the success and failure of methodological spread between the sciences. We introduce a formal model that considers factors like methodological competence and reviewer bias toward one’s own methods. We show how these self-preferential biases can protect poor methodology within scientific communities, and lack of reviewer competence can contribute to failures to adopt better methods. We then use a second model to argue that input from outside disciplines can help break down barriers to methodological improvement. In doing so, we illustrate an underappreciated benefit of interdisciplinarity.