Interactive Visualization for Group Decision Analysis
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.036 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.053 | 0.006 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".