Corresponding conditional

Abstract: The appropriateness, or acceptability, of a conditional does not just ‘go with’ the corresponding conditional probability. A condition of dependence is required as well (cf. Douven in Synthese 164:19–44, 2008, The epistemology of indicative conditionals. Formal and empirical approaches. Cambridge University Press, Cambridge, 2016; Skovgaard-Olsen et al. in Cognition 150:26–36, 2016). In this paper a particular notion of dependence is proposed. It is shown that under both a forward causal and a backward evidential (or diagnostic) reading of the conditional, this appropriateness condition reduces to conditional probability under some natural circumstances. Because this is in particular the case for the so-called diagnostic reading of the conditional, this analysis might help to explain some of Douven and Verbrugge’s (Cognition 117:302–318, 2010) empirical observations.

Abstract: In _nancial econometrics the modelling of asset return series is closely related to the estimation of the corresponding conditional densities[ One reason why one is interested in the whole conditional density and not only in the conditional mean is that the conditional variance can be interpreted as a measure of time!dependent volatility of the return series[ In fact\ the mod! elling and the prediction of volatility is one of the central topics in asset pricing[ In this paper we propose to estimate conditional densities semi! non!parametrically in a neural network framework[ Our recurrent mixture density networks realize the basic ideas of prominent GARCH approaches but they are capable of modelling any continuous conditional density also allowing for time!dependent higher!order moments[ Our empirical analysis of daily FTSE 099 data demonstrates the importance of distributional assumptions in volatility prediction and shows that the out!of!sample fore! casting performance of neural networks slightly dominates those of GARCH models[ Copyright 1999 John Wiley + Sons\ Ltd[

Abstract: The Ramseyan thesis that the probability of an indicative conditional is equal to the corresponding conditional probability of its consequent given its antecedent is both widely confirmed and subject to attested counterexamples (e.g., McGee, in Analysis 60(1):107–111, 2000; Kaufmann, in J Philos Logic 33:583–606, 2004). This raises several puzzling questions. For instance, why are there interpretations of conditionals that violate this Ramseyan thesis in certain contexts, and why are they otherwise very rare? In this paper, I raise some challenges to Stefan Kaufmann’s account of why the Ramseyan thesis sometimes fails, and motivate my own theory. On my theory, the proposition expressed by an indicative conditional is partially determined by a background partition, and hence its probability depends on the choice of such a partition. I hold that this background partition is contextually determined, and in certain conditions is set by a salient question under discussion in the context. I argue that the resulting theory offers compelling answers to the puzzling questions raised by failures of the Ramseyan thesis.