Epistemic Landscapes and the Division of Cognitive Labor

Abstract

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.

The Independence Thesis: When Individual and Social Epistemology Diverge

“…4. Our model of scientific practice generalizes some features of models already extant in the philosophical literature, but it also differs in several important respects (Kitcher 1990(Kitcher , 1993(Kitcher , 2002Weisberg and Muldoon 2009). Space prevents a detailed comparison of these models.…”

Our responsibility to manage evaluative diversity

“…A population with diverse blood-types will survive more kinds of plague because different blood-types are robust against different diseases -evaluative diversity could likewise have evolved so that societies can be effective against a wider range of computational problems [12,22]. To explore this possibility, I developed the GRIN Self-Quiz.…”

Agent-based Models as Fictive Instantiations of Ecological Processes

“…1 They have been used very successfully in social sciences to explore questions of regularity in social behavior and in the ecological sciences . Agent-based systems are useful for understanding the complex interactions of organisms in ecological and evolutionary biology, as well as the cognitive division of labor in science (e.g., see Weisberg and Muldoon 2009). Like the complexity found in natural systems, these models allow complex patterns to "bubble-up" or emerge from lower-level interactions among agents.…”