Why Average When You Can Stack? Better Methods for Generating Accurate Group Credences

Abstract

AbstractFormal and social epistemologists have devoted significant attention to the question of how to aggregate the credences of a group of agents who disagree about the probabilities of events. Moss (2011) and Pettigrew (2019) argue that group credences can be a linear mean of the credences of each individual in the group. By contrast, I argue that if the epistemic value of a credence function is determined solely by its accuracy, then we should, where possible, aggregate the underlying statistical models that individuals use to generate their credence functions, using “stacking” techniques from statistics and machine learning first developed by Wolpert (1992).