Towards an approach for analysing external representations created during sensemaking using generative grammar

During sensemaking, users often create external representations to help them make sense of what they know, and what they need to know. In doing so, they necessarily adopt or construct some form of representational language using the tools at hand. By describing such languages implicit in representations we believe that we are better able to describe and differentiate what users do and better able to describe and differentiate interfaces that might support them. Drawing on approaches to the analysis of language, and in particular, Mann and Thompson’s Rhetorical Structure Theory, we analyse the representations that users create to expose their underlying ‘visual grammar’. We do this in the context of a user study involving evidential reasoning. Participants were asked to address an adapted version of IEEE VAST 2011 mini challenge 3 (interpret a potential terrorist plot implicit in a set of news reports). We show how our approach enables the unpacking of the heterogeneous and embedded nature of user-generated representations and allows us to show how visual grammars evolve and become more complex over time in response to evolving sensemaking needs

Pre-emption cases may support, not undermine, the counterfactual theory of causation

Pre-emption cases have been taken by almost everyone to imply the unviability of the simple counterfactual theory of causation. Yet there is ample motivation from scientific practice to endorse a simple version of the theory if we can. There is a way in which a simple counterfactual theory, at least if understood contrastively, can be supported even while acknowledging that intuition goes firmly against it in pre-emption cases – or rather, only in some of those cases. For I present several new pre-emption cases in which causal intuition does not go against the counterfactual theory, a fact that has been verified experimentally. I suggest an account of framing effects that can square the circle. Crucially, this account offers hope of theoretical salvation – but only to the counterfactual theory of causation, not to others. Again, there is (admittedly only preliminary) experimental support for this account

Should causal models always be Markovian? The case of multi-causal forks in medicine

The development of causal modelling since the 1950s has been accompanied by a number of controversies, the most striking of which concerns the Markov condition. Reichenbach's conjunctive forks did satisfy the Markov condition, while Salmon's interactive forks did not. Subsequently some experts in the field have argued that adequate causal models should always satisfy the Markov condition, while others have claimed that non-Markovian causal models are needed in some cases. This paper argues for the second position by considering the multi-causal forks, which are widespread in contemporary medicine (Section 2). A non-Markovian causal model for such forks is introduced and shown to be mathematically tractable (Sections 6, 7, and 8). The paper also gives a general discussion of the controversy about the Markov condition (Section 1), and of the related controversy about probabilistic causality (Sections 3, 4, and 5

Use of the visual range of detection to estimate effective sweep width for land search and rescue based on 10 detection experiments in North America.

Although additional experiments are required for the medium- and low-visibility search objects and in the dry-domain ecoregion, we suggest search planners use the following correction factors to convert field-measured Rd to an estimate of the effective sweep width (W): high-visibility W = 1.8 × Rd; medium-visibility W = 1.6 × Rd; and low-visibility W = 1.1 × Rd