Dynamics and Diversity in Epistemic Communities


Bruner (Synthese, 2017, https://doi.org/10.1007/s11229-017-1487-8) shows that in cultural interactions, members of minority groups will learn to interact with members of majority groups more quickly—minorities tend to meet majorities more often as a brute fact of their respective numbers—and, as a result, may come to be disadvantaged in situations where they divide resources. In this paper, we discuss the implications of this effect for epistemic communities. We use evolutionary game theoretic methods to show that minority groups can end up disadvantaged in academic interactions like bargaining and collaboration as a result of this effect. These outcomes are more likely, in our models, the smaller the minority group. They occur despite assumptions that majority and minority groups do not differ with respect to skill level, personality, preference, or competence of any sort. Furthermore, as we will argue, these disadvantaged outcomes for minority groups may negatively impact the progress of epistemic communities.

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  1. 1.

    By epistemic communities we mean groups of knowledge makers like academics and industry researchers, though our discussion will generally focus on academia.

  2. 2.

    Readers who wish to engage more thoroughly with the details of our results, but who are not familiar with the methods used, may wish to read Weibull (1997) or Gintis (2009).

  3. 3.

    Traditional games also define information available to the actors, but in evolutionary game theory, this aspect is downplayed.

  4. 4.

    These dynamics were intended as a model of change via natural selection, but have subsequently been shown to effectively model both individual learning (Börgers and Sarin 1997; Hopkins 2002) and cultural evolution (Weibull 1997).

  5. 5.

    Kitcher (1990) and Strevens (2003) appeal to the ‘credit economy’ to model academics in a classical game-theoretic framework, while Bruner (2013) uses similar assumptions in a dynamic model involving cultural evolution.

  6. 6.

    Simplified games of this sort are standardly employed in evolutionary analyses of bargaining (Young 1993; Skyrms 1996; Alexander and Brian 1999; Binmore 2008).

  7. 7.

    Pure strategies are ones where actors always take the same action rather than randomly mixing over multiple actions. This game (and the following one) also have mixed Nash equilibria—those where actors use strategies that probabilistically choose actions—but these will be less germane to our evolutionary analyses and so we do not discuss them here.

  8. 8.

    This game is extensively analyzed by Wagner (2012) who calls it the stag hunt/divide the dollar game to capture the sense that it combines joint action and bargaining. It is equivalent to a Nash demand game with an outside option.

  9. 9.

    Co-authored papers are more likely to be accepted to top journals in many fields, and are cited more (Laband 1987; Gordon 1980; de Beaver and Rosen 1979; Card and DellaVigna 2013). Many authors have argued that collaboration improves overall academic productivity (Morrison et al. 2003; Landry et al. 1996; Lee and Bozeman 2005).

  10. 10.

    Recent work on ghost authorship implies that in some disciplines such outcomes are common (Bennett and Taylor 2003).

  11. 11.

    This occurs in real academic communities. Tenure and promotion decisions are made differently for women and men (Perna 2001). Emails to professors asking for mentoring help tagged with male and/or white sounding names receive more and better responses than those with female and/or ethnic sounding names (Milkman 2014). Black applicants are less likely to receive NIH funding than white applicants controlling for other factors (Ginther et al. 2011). Researchers, when assessing otherwise identical male and female academic job candidates, are more likely to believe males are more qualified, more likely to hire the male, and more likely to offer him a higher salary (Moss-Racusin et al. 2012; Steinpreis et al. 1999). Similar findings, some outside of academia, have been garnered for job candidates who are LGBTQ or racial minorities (Tilcsik 2011; Bertrand and Mullainathan 2003).

  12. 12.

    We use a version of the discrete time, two population replicator dynamics where one population may be smaller than the other, and where all actors interact with both populations. Let x and y represent the two populations so that \(x+y = 1\) and \(x\le y\). Strategies for population x update according to \(x_{i}' = x_i(\frac{f_i(x,y)}{\sum _{j=1}^{n}f_j(x,y)x_j)})\) where \(x_i\) is the proportion of the x population playing strategy i, \(f_i(x,y)\) is the fitness of type i in x given the population states of x and y, and \(\sum _{j=1}^{n}f_j(x,y)x_j\) is the average population fitness for x given the states of x and y. Strategies for population y update according to the analogous dynamics.

  13. 13.

    Because we divide our populations into two types, where actors treat different types differently, we also assume that they learn differently from the two types. Actors in our models imitate peers of their own type, rather than the other. When it comes to choosing models for social learning humans have indeed been found to be sensitive to social identity (Henrich and Henrich 2007; Killian 1990; Losin et al. 2012).

  14. 14.

    They use dynamics where actors best respond to memory of past play. Bruner (2017) finds that for the replicator dynamics such norms likewise arise. Skyrms and Zollman (2010) find similar results.

  15. 15.

    D’Arms et al. (1998) similarly point out that allowing anti-correlated interaction between bargainers who make high and low demands allows for the evolution of these two strategies.

  16. 16.

    This effect is analogous to what is called the Red King effect in biology. When one of two mutualistic biological species evolves more slowly than the other, under the right conditions they can gain an advantage over their mutualistic partners (Bergstrom and Lachmann 2003). For this reason, this minority/majority effect has been described elsewhere as the cultural Red King effect (Bruner 2017; Rubin and O’Connor 2017; O’Connor 2017a, b).

  17. 17.

    Note that in cases like this, because actors do not need to bargain, the collaboration game is essentially equivalent to a stag hunt, but where the benefit for hunting stag is different for the two partners.

  18. 18.

    Amazingly, they find that when women co-author with each other, the effect is not as strong, indicating that cross gender collaboration results in particularly little assigned credit for women.

  19. 19.

    These results are similar to some from Wagner (2012).

  20. 20.

    In doing so, we are measuring what are called the ‘basins of attraction’ for populations playing games under the dynamics we use. These basins specify which proportions of population states evolve to which equilibria.

  21. 21.

    Weisberg and Muldoon (2009) introduced an earlier model with similar results, but see Thoma (2015) and Alexander et al. (2015).

  22. 22.

    For more on this, see work in standpoint epistemology such as Harding (1991, 1998).

  23. 23.

    It is harder to find clear cases where personal diversity is important in areas like physics or mathematics, though some have argued for such effects (Harding 1991, 1998; Fehr et al. 2011).

  24. 24.

    For an interesting example, West et al. (2013) find that traditionally women have been less likely to hold coveted first and last author positions in collaborative work. In mathematics, however, authorship is determined alphabetically, and the effects noted by these authors do not occur.


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The authors would like to thank Shahar Avin, Jeffrey Barrett, Liam K. Bright, Adrian Currie, Manuela Fernandes, Remco Heesen, Simon Huttegger, Huw Price, Eric Schliesser, Brian Skyrms, Kyle Stanford, and James Weatherall for comments and feedback on this work. This work was funded by NSF Standard Research Grant 1535139: Social Dynamics and Diversity in Epistemic Communities.

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Correspondence to Cailin O’Connor.

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O’Connor, C., Bruner, J. Dynamics and Diversity in Epistemic Communities. Erkenn 84, 101–119 (2019). https://doi.org/10.1007/s10670-017-9950-y

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