Uncertainty Visualization Study Group Notes – 11/19/2012
Bill / Miriah Morning Recap
- 2.5 hour ‘intense conversation’
- Now agree about what we disagree about.
- Few days: write up few sentences about key position
- Actual will be somewhere between the two positions – there seems to be an interesting question
Lapinski Paper
- 11 step process: considerations
- sequential checklist
- laundry-list in middle
Important points made with regards to outlined process:
- (1) Identify Uncertainty Vis Task
- Task-Context is Important
- Difference between “problem statement” & “mission statement”
- our goal: technique – closer to mission statement
- fundemental problem is not create visualization, but to convey the information – assumption that visualization may help solve the problem
- (2) Understand the Data
- [Bill] criticisms:
- step has to be done at some point but more has to be done, can’t just propogate concept from this ste like they do in this paper
- open question: does knowing the provenance of the uncertainty help in decision making? Does complexity just make it more confusing if we’re having trouble understanding it in the first place?
- visualizing for system improvement –> possible; doesn’t seems to make less sense for end user in decision making
- do certain contexts cause people to use it more rationally? if “optimal” statistical estimate, why not lie to person?
- [Heidi] not all decisions are going to be Bayseian or fit into this neat format; real life not as compartmentalized
- [Ross] agree for diff reasons: at some point you have to be willing to lump things together – can’t create a completely new visualization every time the uncertainty source changes slightly
- (6/7) Determine Specific Causes and Causal Categories
- [Bill] not necessarily ‘wrong’, but asserted without support
- the question ‘Does presenting context change people’s decision?’ is different from the question ‘Does presenting context help people make better decisions?’
- creates categories from ‘measurement’, ignores modeling error sources in simulation
- these particular categories may be valid encapsulation of measured data uncertainty sources
- (9) Prepare the Uncertainty for Visualization
- difference: reasoning with uncertainty vs. reasoning about uncertainty
- will need to ensure that distinction is clear within our project(s)
- will care about different characteristics of uncertainty b/c problems are very different
- [Bill] takeaway – if talking about causality have to be vary careful to distinguish who cares / benefits and why
- possibility that uncertainty is actually getting folded into the task
- can often solve the problem without uncertainty – may not be optimal, but can solve
- if task is: ‘Do we need more data?’, do we represent uncertainty vs. represent consequence of uncertainty
- boat identity example – uncertainty is data – “here’s bad data, fix it” is data quality issue, not really a meaningful uncertainty issue in the way we’ve been discussing
- (10) Try Different Uncertainty Visualization Techniques
- need to make the reasons for choices (with regards to encoding) explicit
- just decided on categorical representation for what is underlying quantitative data, and while that may have been the right choice based on context, that decision should be explicit