Relativism, Faultlessness, and the Epistemology of Disagreement

Abstract: Recent years have witnessed a revival of interest in relativism. Proponents have defended various accounts that seek to model the truth-conditions of certain propositions along the lines of standard possible world semantics. The central challenge for such views has been to explain what advantage they have over contextualist theories with regard to the possibility of disagreement. I will press this worry against Max Kölbel’s account of faultless disagreement. My case will proceed along two distinct but connected lines. First, I will argue that the sense of faultlessness made possible by his relativism conflicts with our intuitive understanding of disagreement. And second, that his meta-epistemological commitments are at odds with the socio-epistemic function of disagreement. This latter problem for relativistic accounts of truth has thus far been largely ignored in the literature

On periodicity in bounded projective resolutions

Let A be a self-injective algebra over an algebraically closed field k. We show that if an A-module M of complexity one has an open orbit in the variety of d-dimensional A-modules, then M is periodic. As a corollary we see that any simple A-module of complexity one must be periodic. In the course of the proof, we also show that modules with open orbits are preserved by stable equivalences of Morita type between self-injective algebras

Forecasting Non-Stationary Volatility with Hyper-Parameters

We consider sequential data that is sampled from an unknown process, so that the data are not necessarily iid. We develop a measure of generalization for such data and we consider a recently proposed approach to optimizing hyper-parameters, based on the computation of the gradient of a model selection criterion with respect to hyper-parameters. Hyper-parameters are used to give varying weights in the historical data sequence. The approach is successfully applied to modeling the volatility of Canadian stock returns one month ahead. Nous considérons des données séquentielles échantillonnées à partir d'un processus inconnu, donc les données ne sont pas nécessairement iid. Nous développons une mesure de généralisation pour de telles données et nous considérons une approche récemment proposée pour optimiser les hyper-paramètres qui est basée sur le calcul du gradient d'un critère de sélection de modèle par rapport à ces hyper-paramètres. Les hyper-paramètres sont utilisés pour donner différents poids dans la séquence de données historiques. Notre approche est appliquée avec succès à la modélisation de la volatilité des rendements d'actions canadiennes sur un horizon de un mois.Sequential data, hyper-parameters, generalization, stock returns, volatility., Données séquentielles, hyper-paramètres, généralisation, rendement d'actions, volatilité