Why do scientific disciplines appear, disappear, merge together, or split apart? We might point to major events: the creation of new journals and departments, significant innovations, or new technologies. However, at the heart of things is a social process involving interactions among individual scientists, deciding who to collaborate with and on what topic. The nature of these interactions and their short-term consequences on scientific inquiry have been studied in some detail, as has the longer-term evolution of scientific disciplines throughout history. Yet this leaves unanswered questions about how the interactions among those scientists give rise to broad, long-term trends in the evolution of science.
To bring together these two areas of research, we provide a new model in which the dynamics of scientific collaboration affects the evolution of scientific fields. We build off of Sun et al. (2013)’s model, in which scientists choose collaborators based on whether they have collaborated in the past, while papers and scientists accumulate discipline associations based on these collaborations. Ultimately, new scientific fields emerge as sets of scientists cluster together, or merge as previously distinct disciplines start to blend together. While their model captures many features of how scientific fields have evolved, key aspects of the short-term interactions among scientists are not incorporated, leading to unexplored aspects of the evolution of disciplines. In particular, publications have different potential impacts depending on various factors, e.g. the reputation of the scientists. Incorporating this aspect of scientific work allows us to explore two broad historical trends in terms of the social interactions among scientists which underpin them.
First, new scientific fields are often spearheaded by a few prominent scientists. While we may explain this with reference to works of genius or larger-than-life personalities, it can also be explained with reference to the dynamics of collaboration and credit accumulation. If the impact of previous work (i.e. the credit accumulated for it) affects future productivity and number of collaborations, as well as the impact of future work (Peterson et al. 2014), new disciplines may emerge around key figures regardless of quality of work or personality. Further, social positioning may be a better predictor of ability to found new disciplines than a scientist’s personal characteristics.
Second, there seems to be a ‘contagion of disrespect’, whereby research in subfields associated with marginalized groups are increasingly dismissed as unimportant to the production of scientific knowledge (Schneider et al. forthcoming). While biased evaluation of work surely plays a role in this, collaboration dynamics are also likely part of the story. There is often unequal division of credit within collaborations, where members of marginalized groups receive less recognition for their contributions compared to members of a dominant group. This can affect both future credit accumulation and likelihood to collaborate across social identity lines (Rubin and O’Connor 2018). If collaborations become increasingly clustered according to social identity, while results from particular social identity groups generate less credit, this gives rise to a contagion of disrespect.
Petersen, A.M., Fortunato, S., Pan, R.K., Kaski, K., Penner, O., Rungi, A., Riccaboni, M., Stanley, H.E. and Pammolli, F. (2014). Reputation and Impact in Academic Careers.
Proceedings of the National Academy of Sciences, 111(43), 15316-15321.
Rubin, H., & O’Connor, C. (2018). Discrimination and Collaboration in Science. Philosophy of Science, 85(3), 380-402.
Schneider, M.D., Rubin, H, & O’Connor, C. (forthcoming). Promoting Diverse Collaborations. The Dynamics of Science: Computational Frontiers in History and Philosophy of Science, eds. G. Ramsey & A. De Block
Sun, X., Kaur, J., Milojević, S., Flammini, A., & Menczer, F. (2013). Social Dynamics of Science. Scientific Reports, 3(1), 1-6.

What is the connection between capitalism and racial hierarchy? In line with the theoretical tradition known as “the theory of racial capitalism” we show that the latter can functionally support the former. As a social construction, race has just those features which allow it to facilitate stable, inequitable distributions of resources. We support this claim using techniques from evolutionary game theory and the theory of cultural evolution.
The theory of racial capitalism proposes an origin story for how the global economy came to be racially stratified and (in the main) organized along capitalist lines. The proposal is that the very same events led to both - Europe was already organizing its workforces along proto-racial lines at about the time it was spreading its economic form through colonialism. As such, European expansion ended up simultaneously bringing capitalism and racial organization in its wake.
However, many scholars make a stronger claim than noting the mere historical contingency that racism and capitalism co-occurred. Many argue that this coincidence is functional: the development of racial hierarchy helped the capitalist social form survive and perpetuate itself. This is because capitalism will inevitably generate an unequal distribution of control over factors of production and division of the resulting social surplus. Some means of explaining, justifying, and continuing this rampant and easily observed inequality is required, and, in particular, one that allows elites to retain their place. Race and racialism, by being easily observable, hard to change, and passed down across generations, worked nicely.
But why do these features of race work to stabilize capitalist systems? Using modelling techniques from evolutionary game theory, and drawing on some previous results, we show how oppressive schemes employing race are especially well-suited for underpinning stable and highly unequal systems of dividing labor and reward. We argue that these models provide a functional explanation for the co-occurrence of race and capitalism that vindicates arguments from racial capitalist theory.
We describe in detail several different models intended to illuminate the functional role that various aspects of race play in capitalist systems. We start with the fact that race is hard to change or imitate. I.e., it is fairly inflexible. We then discuss the fact that race is often fairly easy to identify compared to alternative tags or markers. And last we discuss the heritability of race. In each case we show how these features underpin systems of inequality. In models without these features, inequitable systems are unlikely to emerge. In models with them, inequality is stabilized. We also show that if powerful groups were to select some categorical system to ground inequality, they benefit themselves by picking race for this reason. We conclude by discussing the normative political consequences of this relationship.

In this paper, we investigate one factor that can directly contribute to—as well as indirectly shed light on the other causes of—the gender gap in academic publications: time spent in peer review. To study our problem, we link administrative data from an economics field journal with bibliographic and demographic information on the articles and authors it publishes. Our results suggest that in each round of review, referees spend 4.4 more days reviewing female-authored papers and female authors spend 12.3 more days revising their manuscripts. However, both gender gaps decline—and eventually disappear—as the same referee reviews more papers. This pattern suggests novice referees initially statistically discriminate against female authors; as their information about and confidence in the refereeing process improves, however, the gender gaps fall.

When solving a complex problem in a group, should we always choose the best available solution? In this paper, I build simulation models to show that, surprisingly, a group of agents who randomly follow a better available solution than their own can end up outperforming a group of agents who follow the best available solution. The reason for this relates to the concept of transient diversity in science (Zollman 2010). In my models, the “better” strategy preserves a diversity of practice for some time, so agents can sufficiently try out a range of solutions before settling down. The “best” strategy, in contrast, may lock the group in a suboptimal position that prevents further exploration. In a slogan, “better” beats “best.”
My models are adapted from Lazer and Friedman (2007)’s model where a network of agents is tasked to solve an NK landscape problem. Here, agents search in a solution space with multiple “peaks.” They only have knowledge of their neighbor’s solutions, as well as (sometimes) the results of limited local exploration, so they may fail to ever discover the global optimal solution(s). The NK landscape model can be fruitfully applied to cultural innovation and problem solving, especially to complex problems where optimal solutions are not readily accessible from all starting points. Besides, NK landscape models are more general and realistic than other epistemic landscape models (e.g. Weisberg and Muldoon (2009)), due to their ability to represent multi-dimensional and interconnected solutions (Alexander et al. 2015).
My result of “better” beating “best” has several implications in social epistemology. First, this is another instance of the Independence Thesis, which states that individual and group decision-making can come apart (Mayo-Wilson et al. 2011). In my models, every round, an agent’s epistemic gain when they follow the “better” strategy is no greater than when they follow the “best” strategy, yet, they have greater long-term gain in a social setting.
Second, Zollman (2007, 2010) and Lazer and Friedman (2007) previously showed that a less connected community is more likely to arrive at superior beliefs or solutions, due to the transient diversity present. But limiting connectivity for the gain of diversity of practice may be too costly or impractical (Rosenstock et al. 2015). My result suggests that we can achieve comparable benefits if instead people choose “better.” Indeed, a completely connected group that follows the “better” strategy can outperform a very sparsely connected group that follows the “best” strategy.
Finally, insofar as some approaches to a problem are associated with particular social groups (Longino 1990; Fehr 2011), the “better” strategy also makes it more likely to preserve solutions arising from marginalized perspectives. These solutions may not be the most optimal at a given time, perhaps due to a historical lack of resources, but may nevertheless become promising after further explorations.
Alexander, J. M., Himmelreich, J., and Thompson, C. (2015). Epistemic Landscapes, Optimal Search, and The Division of Cognitive Labor. Philosophy of Science, 82(3):424–453.
Fehr, C. (2011). What is in It for Me? The Benefits of Diversity in Scientific Communities. In Feminist Epistemology And Philosophy Of Science, pages 133– 155. Springer.
Lazer, D. and Friedman, A. (2007). The Network Structure of Exploration and Exploitation. Administrative Science Quarterly, 52(4):667–694.
Longino, H. E. (1990). Science as Social Knowledge: Values and Objectivity in Scientific Inquiry. Princeton University Press.
Mayo-Wilson, C., Zollman, K. J., & Danks, D. (2011). The Independence Thesis: When Individual and Social Epistemology Diverge. Philosophy of Science, 78(4), 653-677.
Rosenstock, S., Bruner, J., & O’Connor, C. (2017). In Epistemic Networks, is Less Really More?. Philosophy of Science, 84(2), 234-252.
Weisberg, M. and Muldoon, R. (2009). Epistemic Landscapes and the Division of Cognitive Labor. Philosophy of Science, 76(2):225–252.
Zollman, K. J. (2007). The Communication Structure of Epistemic Communities. Philosophy of Science, 74(5):574–587. Zollman, K. J. (2010). The Epistemic Benefit of Transient Diversity. Erkenntnis, 72(1):17.