- In proposal, by fiat, we adopted a very particular way of specifying the kind of uncertainty we’re going to visualize – We are not dealing with sources independently, rather all sources of uncertainty are folded into a single probability distribution over outcome set.
- [Miriah] We are ignoring the role of the sources / context in informing decisions – should we make more explicit?
- Does the source of data and the source of uncertainty affect the effectiveness of particular representations?
- Assertion: 2 problems to solve – both must be explicitly specified
- What properties of pdf are we encoding? or What is the characterization of the pdf?
- How are we encoding those properties? or How are we encoding that characterization?
- Start with absolute simplest case, characterized by 1D random variable
- Not b/c it’s interesting problem but b/c it is non-trivial and well formed
- How do you know what constitutes a ‘right answer’? We’ve chosen our problem in a way that it is mathematically defined (e.g. maximum likelihood), rather than just subjectively defined
- the task requires incorporation of uncertainty information in order to give the correct response
- [Ross] One-dimensional representations are very different from two-dimensional representations, both of which are very different from from three dimensional, etc. – The results of this study may not be very generalizable
- The methodological framework for conducting the studies will still generalize to higher dimensions
- This study will act as a basic threshold test of whether people can even cognitively process uncertainty information properly
- We should include qualification language in our results - be honest about possible lack of generalizability
[Miriah] We should think more about the variety of characterizations that we can produce from a given pdf
What other sorts of task types (besides those already mentioned) can we produce to test uncertainty?
Would this be a good Visualization paper? Would this be a good Cognitive Science paper?