Epistemic possibility

Instead of assessing risk, I suggest that we should try to assess riskiness in the everyday sense of this term, where it refers to the epistemic possibility of harm, not merely probabilities of identified types of harm. Whereas risk relates to outcomes, riskiness is a property of a thing, situation or activity and is relative to our knowledge about it. I suggest that what are normally termed precautionary approaches are concerned with riskiness, rather than just risk they are concerned with whether, for all we know, there is a possibility of harm, not just with the probabilities of known, specifiable types of harm. 

It is noteworthy that the arguments from multiple realizability did not take a purely epistemological form, namely, that the epistemic possibility of multiple reahzability will make it impossible to uncover and describe the chemical, biological or physical kind we have to identify with the mental one, but rather came in a metaphysical shape In virtue of being multiply realized, mental kinds do not belong to the set of lower-level kinds. 

The difference between the epistemic model of explanation and the ontic one becomes clear at this point Cause... because, as Philip Kitcher elegantly puts it, is the slogan of the epistemic school. Explanations frequently refer to causes simply because they explain much, but there is no conditio sine qua non (1989). If it turned out that other non-causal regularities were better explanatory vehicles the causal explanation would be dropped. The adherent of the ontic model of explanation, on the other hand, maintains that any valid explanation has to cite causes of the explanandum because explanation owes its only possible sense to a thorough assumption about the ontic constitution of our world. Those who want to capture the validity of explanations in their dependency on causes have to give an answer to the question of what causality consists in. Here, I sketch the two major accounts of causality. 

A reasonable approach to addressing epistemic uncertainties in a quantitative way would consist of evolving from completeness to parametric uncertainties as far as possible, with an aim at allowing the comparison of PRA numerical results including uncertainty with the appropriate decision guidelines. 

Irrespective of the taxonomy used, epistemic and aleatory uncertainty, or uncertainty and variation, alternatives to probability have been suggested for the representation of the epistemic concept. These include interval or imprecise probabihty (Coolen 2004, Coolen Utkin 2007, Utkin Coolen 2007, Weichselberger 2000), fuzzy set theory and the associated theory of possibility (Zadeh 1965, Zadeh 1978, Unwin 1986), and the theory of behef functions (Shafer 1976), also known as evidence theory or the Dempster-Shafer theory of evidence. 

Specifically, it has been suggested that a possibilistic representation of epistemic uncertainty may be more adequate when sufficiently informative data are not available for statistical analysis and thus one has to resort mostly to information of qualitative namre. In particular, in cases where an expert does not have sufficiently refined knowledge or opinion to characterize the epistemic uncertainty in terms of probability distributions, possibility theory uses two measures of... 

There often seems to be a tacit assumption made that knowledge-based or subjective probability is an appropriate representation of epistemic uncertainty only when sufficient data exist, on which to base the probability or probability distribution in question. However, this is a misconception. Rather, the question is whether a comparison with the standard described in the previous section can be made. Faced with an expert/assessor who is not willing to specify a single number p or a single distribution G as his/her probability or probability distribution, respectively, but who will provide an interval or a set of distributions, an analyst may choose to define a possibUify distribution to reflect the imprecise input from the expert. Using a possibility distribution n implies that no particular probability distribution G is selected and assigned from among the family ( r) of probabihty distributions compatible with 7r. Compatible here means that for any interval... 

Epistemic uncertainty during design phase possible modifications during design phase, inexact or imprecise information from the manufacturer. 

Stating hypotheses (tentative explanations to causal questions) constitutes, in general, a complex process of combining empirical evidence, previous knowledge, and intuition (Lawson, 1995). The role of argument in this process seems to be crucial. Partial scientific claims towards an explanatory framework do need to be well grounded in warranting structures that are built on reliable epistemic criteria (Driver et al., 2000). Furthermore, the possible formulation of more than one alternative hypothesis for the same causal question, activates a process of comparative evaluation of their explanatory efficacy to decide which one is the... 

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