Online research in philosophy

EDITORS' INTRODUCTION B he Vedas tell of a conversation between a young man, Shvetaketu, and his father concerning what the son had learned in his education ...

We report two issues concerning diverging sets of Bayesian (conditional) probabilities-divergence of "posteriors"-that can result with increasing evidence. Consider a set P of probabilities typically, but not always, based on a set of Bayesian "priors." Fix E, an event of interest, and X, a random variable to be observed. With respect to P, when the set of conditional probabilities for E, given X, strictly contains the set of unconditional probabilities for E, for each possible outcome X = x, call this (...) phenomenon dilation of the set of probabilities (Seidenfeld and Wasserman 1993). Thus, dilation contrasts with the asymptotic merging of posterior probabilities reported by Savage (1954) and by Blackwell and Dubins (1962). (1) In a wide variety of models for Robust Bayesian inference the extent to which X dilates E is related to a model specific index of how far key elements of P are from a distribution that makes X and E independent. (2) At a fixed confidence level, (1-α), Classical interval estimates A n for, e.g., a Normal mean θ have length O(n -1/2 ) (for sample size n). Of course, the confidence level correctly reports the (prior) probability that θ ∈ A n ,P(A n )=1-α , independent of the prior for θ . However, as shown by Pericchi and Walley (1991), if an ε -contamination class is used for the prior on the parameter θ , there is asymptotic (posterior) dilation for the A n , given the data. If, however, the intervals A ′ n are chosen with length $O(\sqrt{\log (\text{n})/\text{n})}$ , then there is no asymptotic dilation. We discuss the asymptotic rates of dilation for ClassClassical and Bayesian interval estimates and relate these to Bayesian hypothesis testing. (shrink)

The proliferation of news and information sources has motivated a need to identify those providing legitimate journalism. One temptation is to go the route of such fields as medicine and law, namely to formally professionalize. This gives a clear method for determining who is a member, with an array of associated responsibilities and rewards. We argue that making such a formal move in journalism is a mistake: Journalism does not meet the traditional criteria, and its core ethos is in conflict (...) with the professional mindset. We thus shift the focus from whether the person is journalist to whether the work satisfies the conditions that characterize legitimate journalism. In explaining those conditions we also look at mechanisms for enhancing the power of persons doing journalism, drawing upon lessons from the labor movement. We also consider a self-declaration model while urging increased literacy from all participants in the news gathering and consuming enterprise. (shrink)