Two Notions Of Safety

Timothy Williamson (1992, 224–5) and Ernest Sosa (1996) have ar- gued that knowledge requires one to be safe from error. Something is said to be safe from happening iff it does not happen at “close” worlds. I expand here on a puzzle noted by John Hawthorne (2004, 56n) that suggests the need for two notions of closeness. Counterfac- tual closeness is a matter of what could in fact have happened, given the specific circumstances at hand. The notion is involved in the semantics for counterfactuals and is the one epistemologists have typically assumed. Normalized closeness is rather a matter of what could typically have happened, that is, what would go on in a class of normal alternatives to actuality, irrespectively of whether or not they could have happened in the circumstances at hand

Another Approach to Consensus and Maximally Informed Opinions with Increasing Evidence

Merging of opinions results underwrite Bayesian rejoinders to complaints about the subjective nature of personal probability. Such results establish that sufficiently similar priors achieve consensus in the long run when fed the same increasing stream of evidence. Initial subjectivity, the line goes, is of mere transient significance, giving way to intersubjective agreement eventually. Here, we establish a merging result for sets of probability measures that are updated by Jeffrey conditioning. This generalizes a number of different merging results in the literature. We also show that such sets converge to a shared, maximally informed opinion. Convergence to a maximally informed opinion is a (weak) Jeffrey conditioning analogue of Bayesian “convergence to the truth” for conditional probabilities. Finally, we demonstrate the philosophical significance of our study by detailing applications to the topics of dynamic coherence, imprecise probabilities, and probabilistic opinion pooling

Lowness and Π nullsets

We prove that there exists a noncomputable c.e. real which is low for weak 2-randomness, a definition of randomness due to Kurtz, and that all reals which are low for weak 2-randomness are low for Martin-Lof randomness

Persistent Disagreement and Polarization in a Bayesian Setting

For two ideally rational agents, does learning a finite amount of shared evidence necessitate agreement? No. But does it at least guard against belief polarization, the case in which their opinions get further apart? No. OK, but are rational agents guaranteed to avoid polarization if they have access to an infinite, increasing stream of shared evidence? No

Distention for Sets of Probabilities

A prominent pillar of Bayesian philosophy is that, relative to just a few constraints, priors “wash out” in the limit. Bayesians often appeal to such asymptotic results as a defense against charges of excessive subjectivity. But, as Seidenfeld and coauthors observe, what happens in the short run is often of greater interest than what happens in the limit. They use this point as one motivation for investigating the counterintuitive short run phenomenon of dilation since, it is alleged, “dilation contrasts with the asymptotic merging of posterior probabilities reported by Savage (1954) and by Blackwell and Dubins (1962)” (Herron et al., 1994). A partition dilates an event if, relative to every cell of the partition, uncertainty concerning that event increases. The measure of uncertainty relevant for dilation, however, is not the same measure that is relevant in the context of results concerning whether priors wash out or “opinions merge.” Here, we explicitly investigate the short run behavior of the metric relevant to merging of opinions. As with dilation, it is possible for uncertainty (as gauged by this metric) to increase relative to every cell of a partition. We call this phenomenon distention. It turns out that dilation and distention are orthogonal phenomena

Permissivism, Underdetermination, and Evidence

Permissivism is the thesis that, for some body of evidence and a proposition p, there is more than one rational doxastic attitude any agent with that evidence can take toward p. Proponents of uniqueness deny permissivism, maintaining that every body of evidence always determines a single rational doxastic attitude. In this paper, we explore the debate between permissivism and uniqueness about evidence, outlining some of the major arguments on each side. We then consider how permissivism can be understood as an underdetermination thesis, and show how this moves the debate forward in fruitful ways: in distinguishing between different types of permissivism, in dispelling classic objections to permissivism, and in shedding light on the relationship between permissivism and evidentialism

The Objectivity of Subjective Bayesianism

Subjective Bayesianism is a major school of uncertain reasoning and statistical inference. It is often criticized for a lack of objectivity: (i) it opens the door to the influence of values and biases, (ii) evidence judgments can vary substantially between scientists, (iii) it is not suited for informing policy decisions. My paper rebuts these concerns by bridging the debates on scientific objectivity and statistical method. First, I show that the above concerns arise equally for standard frequentist inference. Second, I argue that the involved senses of objectivity are epistemically inert. Third, I show that Subjective Bayesianism promotes other, epistemically relevant senses of scientific objectivity---most notably by increasing the transparency of scientific reasoning