Interactive Visualization for Group Decision Analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Identifying the best solutions to large infrastructure decisions is a context-dependent multi-dimensional multi-stakeholder challenge in which competing objectives must be identified and trade-offs made. Our aim is to identify and explore features in an interactive visualization tool to help make group decision analysis more participatory, transparent, and comprehensible. We extended the interactive visualization tool ValueCharts to create Group ValueCharts. The new tool was introduced in two real-world scenarios in which stakeholders were in the midst of wrestling with decisions about infrastructure investment. We modeled the alternatives under consideration, for both scenarios, using prescribed criteria identified by domain experts. Participants in both groups were given instructions on how to use the tool to represent their preferences. Preferences for all participants were then displayed and discussed. The discussions were audio-recorded and the participants were surveyed to evaluate usability. The results indicate that participants felt the tool improved group interaction and information exchange and made the discussion more participatory. They expressed that visualizing individual preferences improved the ability to analyze decision outcomes based on everyone’s preferences. Additionally, the participants strongly concurred that the tool revealed disagreements and agreements and helped identify sticking points. These results suggest that a group decision tool that allows group members to input their individual preferences and then collectively probe into any differences makes the process of decision-making more participatory, transparent, and comprehensible and increases the quality and quantity of information exchange.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it