1 Deliberation and Disagreement: Problem solving, Prediction, and Positive Dissensus 1 Hélėne Landemore Yale University helene.landemore@yale.edu Scott E. Page University of Michigan, Ann Arbor Santa Fe Institute spage@umich.edu Abstract: Consensus plays an ambiguous role in deliberative democracy. While it formed the horizon of early deliberative theories, many now denounce it as an empirically unachievable outcome, a logically impossible stopping-rule, and a normatively undesirable ideal. Deliberative disagreement, by contrast, is celebrated not just as an empirically unavoidable outcome, but also as a democratically sound and normatively desirable goal of deliberation. Majority rule has generally displaced unanimity as the ideal way of bringing deliberation to a close. This paper offers an epistemic perspective on this question of consensus versus disagreement. For ensuring the production of better decisions, we argue, the normative appeal of consensus varies depending on the deliberative task – whether it entails problem solving or prediction. We argue that in pure problem-solving contexts, consensus retains a strong normative appeal and forms the ideal deliberative outcome of deliberation. In contrast, on predictive tasks, consensus should generally not be used as a stopping rule nor is it likely to be epistemically desirable as an outcome. Instead deliberators may be better served by ending the deliberation with a form of deliberative disagreement we call “positive dissensus,” which paves the way for more accurate aggregated predictions. Keywords Deliberative democracy, deliberation, majority rule, epistemic democracy, Habermas, dissensus, consensus, prediction, problem solving About the authors Hélène Landemore is Assistant Professor of Political Science at Yale University. 1 This paper was presented at the 7 th European Convention of Analytical Philosophy (ECAP 7) in Milan, Italy, September 4, 2011 and the Oslo-Yale International Conference on “Epistemic Democracy in Practice” at Yale University, October 20-22, 2011. The authors thank participants at these conferences as well as Alfred Moore and Michael Fuerstein for helpful comments. Hélène Landemore thanks Erin Pineda for stellar research assistance. Scott E. Page acknowledges the National Science Foundation Award Number 1101465, the U.S. Army Research Laboratory and the U.S. Army Research Office under contract/grant number W911NF1010379.