Jingyi Wu

Are scientists incentivized to share intermediate results? Some recent work has argued that credit-maximizing scientists would share intermediate results, whereas other work has argued that sharing too much too quickly can hinder a community’s achievement of epistemic goals. This suggests a tradeoff between scientists’ credit-based self-interest and what is epistemically best. We make this tradeoff explicit in an agent-based NK landscape model, where credit-maximizing scientists face a prisoner’s dilemma due to collective epistemic and credit benefits from withholding. Multiple coders independently implemented the model, leading us to identify several coding errors. We reflect on this code comparison experience.

Are scientists incentivized to share intermediate results? Some recent work has argued that credit-maximizing scientists would share intermediate results, whereas other work has argued that sharing too much too quickly can hinder a community’s achievement of epistemic goals. This suggests a tradeoff between scientists’ credit-based self-interest and what is epistemically best. We make this tradeoff explicit in an agent-based NK landscape model, where credit-maximizing scientists face a prisoner’s dilemma due to collective epistemic and credit benefits from withholding. Multiple coders independently implemented the model, leading us to identify several coding errors. We reflect on this code comparison experience.

We consider the duality between General Relativity and the theory of Einstein algebras, in the extended setting where one permits non-Hausdorff manifolds. We show that the duality breaks down, and then go on to discuss a sense in which general relativity, formulated using non-Hausdorff manifolds, exhibits excess structure when compared to Einstein algebras. We discuss how these results bear on a class of algebraically-motivated deflationist views about spacetime ontology. We conclude with a conjecture concerning non-Hausdorff spacetimes with no bifurcate curves.

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).

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).

There is an “underrepresentation problem” in philosophy departments and journals. Empirical data suggest that while we have seen some improvements since the 1990s, the rate of change has slowed down. Some posit that philosophy has disciplinary norms making it uniquely resistant to change. We present results from an empirical case study of a philosophy department that achieved and maintained male-female gender parity among its faculty as early as 2014. Our analysis extends beyond matters of gender parity because that is only one, albeit important, dimension of inclusion. We build from the study to reflect on strategies that may catalyze change.

I analyze the role of infinite idealizations used in the renormalization group (RG hereafter) method in explaining universality across microscopically different physical systems in critical phenomena. I argue that despite the reference to infinite limit systems such as systems with infinite correlation lengths during the RG process, the key to explaining universality in critical phenomena need not involve infinite limit systems. I develop my argument by introducing what I regard as the explanatorily relevant property in RG explanations: linearization* property; I then motivate and prove a proposition about the linearization* property in support of my view. As a result, infinite limit systems in RG explanations are dispensable.

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?