Because contemporary scientific research is conducted by groups of scientists, understanding scientific progress requires understanding this division of cognitive labor. We present a novel agent-based model of scientific research in which scientists divide their labor to explore an unknown epistemic landscape. Scientists aim to find the most epistemically significant research approaches. We consider three different search strategies that scientists can adopt for exploring the landscape. In the first, scientists work alone and do not let the discoveries of the community influence their actions. This is compared with two social research strategies: Followers are biased toward what others have already discovered, and we find that pure populations of these scientists do less well than scientists acting independently. However, pure populations of mavericks, who try to avoid research approaches that have already been taken, vastly outperform the other strategies. Finally, we show that, in mixed populations, mavericks stimulate followers to greater levels of epistemic production, making polymorphic populations of mavericks and followers ideal in many research domains. Disciplines PhilosophyThis journal article is available at ScholarlyCommons: http://repository.upenn.edu/philosophy_papers/7 Because contemporary scientific research is conducted by groups of scientists, understanding scientific progress requires understanding this division of cognitive labor. We present a novel agent-based model of scientific research in which scientists divide their labor to explore an unknown epistemic landscape. Scientists aim to find the most epistemically significant research approaches. We consider three different search strategies that scientists can adopt for exploring the landscape. In the first, scientists work alone and do not let the discoveries of the community influence their actions. This is compared with two social research strategies: Followers are biased toward what others have already discovered, and we find that pure populations of these scientists do less well than scientists acting independently. However, pure populations of mavericks, who try to avoid research approaches that have already been taken, vastly outperform the other strategies. Finally, we show that, in mixed populations, mavericks stimulate followers to greater levels of epistemic production, making polymorphic populations of mavericks and followers ideal in many research domains.

Previous literature has demonstrated the important role that trust plays in developing and maintaining well-functioning societies. However, if we are to learn how to increase levels of trust in society, we must first understand why people choose to trust others. One potential answer to this is that people view trust as normative: there is a social norm for trusting that imposes punishment for noncompliance. To test this, we report data from a survey with salient rewards to elicit people’s attitudes regarding the punishment of distrusting behavior in a trust game. Our results show that people do not behave as though trust is a norm. Our participants expected that most people would not punish untrusting investors, regardless of whether the potential trustee was a stranger or a friend. In contrast, our participants behaved as though being trustworthy is a norm. Most participants believed that most people would punish someone who failed to reciprocate a stranger’s or a friend’s trust. We conclude that, while we were able to reproduce previous results establishing that there is a norm of reciprocity, we found no evidence for a corresponding norm of trust, even among friends.

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In epistemology and the philosophy of science, there has been an increasing interest in the social aspects of belief acquisition. In particular, there has been a focus on the division of cognitive labor in science. This essay explores several different models of the division of cognitive labor, with particular focus on Kitcher, Strevens, Weisberg and Muldoon, and Zollman. The essay then shows how many of the benefits of the division of cognitive labor flow from leveraging agent diversity. The essay concludes by examining the benefits and burdens of diversity, particularly in the evaluative diversity that can be found in interdisicplinary science.

Scientific research is almost always conducted by communities of scientists of varying size and complexity. Such communities are effective, in part, because they divide their cognitive labor: not every scientist works on the same project. Philip Kitcher and Michael Strevens have pioneered efforts to understand this division of cognitive labor by proposing models of how scientists make decisions about which project to work on. For such models to be useful, they must be simple enough for us to understand their dynamics, but faithful enough to reality that we can use them to analyze real scientific communities. To satisfy the first requirement, we must employ idealizations to simplify the model. The second requirement demands that these idealizations not be so extreme that we lose the ability to describe real-world phenomena. This paper investigates the status of the assumptions that Kitcher and Strevens make in their models, by first inquiring whether they are reasonable representations of reality, and then by checking the models' robustness against weakenings of these assumptions. To do this, we first argue against the reality of the assumptions, and then develop a series of agent-based simulations to systematically test their effects on model outcomes. We find that the models are not robust against weakenings of these idealizations. In fact we find that under certain conditions, this can lead to the model predicting outcomes that are qualitatively opposite of the original model outcomes.