Because of its complexity, contemporary scientific research is almost always tackled by groups of scientists, each of which works in a different part of a given research domain. We believe that understanding scientific progress thus requires understanding this division of cognitive labor. To this end, 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 climb uphill in this landscape, where elevation represents the significance of the (...) results discovered by employing a research approach. We consider three different search strategies scientists can adopt for exploring the landscape. In the first, scientists work alone and do not let the discoveries of the community as a whole influence their actions. This is compared with two social research strategies, which we call the follower and maverick strategies. Followers are biased towards 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 both of 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. (shrink)

In a review of my book “Wahrscheinlichkeitslehre”, Dr. Ernest Nagel has recently criticized some of my ideas on probability and induction. His review includes a good exposition of my ideas, and I have to thank him for his serious attempts to do justice to my results. He attacks, however, some very essential points of my theory. I may be allowed, therefore, to answer him as frankly and thoroughly as he attacks me.

A descriptive norm is a behavioral rule that individuals follow when their empirical expectations of others following the same rule are met. We aim to provide an account of the emergence of descriptive norms by first looking at a simple case, that of the standing ovation. We examine the structure of a standing ovation, and show it can be generalized to describe the emergence of a wide range of descriptive norms.

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. (shrink)

Discussion of the cognitive division of labor has usually made very little contact with relevant materials from other disciplines, including theoretical biology, management science, and design theory. This article draws on these materials to consider some unavoidable conundrums faced by any attempt to present a particular way of dividing tasks among a labor team as the uniquely rational way of doing this, given the interdependence of the underlying evaluative standards by which the products of a system of division of labor (...) will be judged. Divisions of labor will typically cut across these interdependencies in ways which leave the outcomes of a process of labor hostage to path dependencies and suboptimalities. Some attempts to avoid these results are shown to be unsuccessful. All these difficulties are compounded by the fact that, in many cases, the division of labor has to be constructed over a ground of values that is itself being constructed simultaneously with the products which they are invoked to assess. Key Words: risk spreading • interdependency • NK fitness landscapes • complexity • epistasis • Kuhn • modularity • path dependency. (shrink)