Can Science Learn More When Scientists Learn Less?: Modeling Question Difficulty and Epistemic Success across Scientific Networks
A scientific community can be thought of as a network of interactive agents attempting to answer questions on the basis of incomplete, conflicting, and sometimes ambiguous data. The interaction between the structure of the communication network and the nature of the question under investigation affects both prospects for accuracy and speed to community consensus. In some cases, it turns out, science *can* learn more when individual scientists learn less. Here we build on previous work, both our own and others', in order to get a firmer grasp on precisely what features of investigatory networks work with what features of problem structure in optimizing epistemic desiderata.
Daniel J. Singer, Steven Fisher, Adam Bramson, William Berger,
Christopher Reade, Carissa Flocken, Adam Sales
Professor Patrick Grim, Center for the Study of Complex Systems, University of Michigan