Automatic validation for multi criteria decision making models in simulation environments
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
This paper presents a technique for validating decision support models that are tested using simulated data. In human-based Multi Criteria Decision Making (MCDM), the data about the different decision criteria is entered to a decision support model. Then, the decision support model suggests or sorts the decision alternatives. The validity of the decision support models can be evaluated by calculating the degree of decision makers' satisfaction. The more degree of satisfaction is achieved, the more reliable and accurate a decision support model is. However, in most cases, it is not possible for the implementers of the decision making models to find realistic data for validating these models. Therefore, they use simulated data. This paper proposes a technique to measure the satisfactions of simulated decision makers (agents). The experiments show that using this technique can provide the implementers of decision models with more confidence about the results of the implemented decision support models
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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