Improving multicriteria group decision making with automated decision guidance
Bibliographic record
Abstract
Many managerial decisions involve consideration of more than one single criterion. Despite the development of multicriteria decision making (MCDM), decision support systems (DSS), and group decision support systems (GDSS) that are aimed at helping decision makers address these complex decisions, there is inadequate success in supporting MCDM. Problems hindering the success of these systems may be due to their undue complexity, over-reliance on quantitative modeling, and failure to accommodate the dynamic learning needs of the people who use them. This study explores the possibility of embedding decision guidance within MCDM GDSS, outlines specific guidelines for the design of such guidance, and demonstrates the potential benefits of enhancing MCDM with guidance for a resource-allocation task. A laboratory experiment was conducted to evaluate the impacts of decision guidance on learning and decision outcomes. Groups using a MCDM GDSS without decision guidance were compared to groups using the same decision model with embedded decision guidance. All groups performed a resource-allocation task. Overall, the findings suggest that the addition of guidance can bring significant advantages to group learning and consensus, as well as to group members' perceptions about their decision making processes and outcomes.
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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 | 0.016 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".