The risk of decision making with incomplete criteria weight information
Bibliographic record
Abstract
In many cases, it may be difficult to obtain explicit information on criteria weights for multicriteria decision analysis. Usually, however, at least the relevant criteria can be assumed to be known, even if their weights are not. In addition, complete or incomplete rank order of these criteria can be known, and it may be possible to obtain estimates for at least some of the value-function parameters. With some decision support tools, such as stochastic multicriteria acceptability analysis (SMAA), it is possible to use incomplete information. The main results of SMAA are the probabilities of certain alternative obtaining a given rank, given all the information available. These probabilities can be used for choosing the most recommendable alternative. However, recommendations are risky when the preference information is incomplete. In this study, the risks are studied through a simulation study based on a previous forestry decision problem with multiple criteria. (1) The probability that the best alternative is recommended and (2) the expected losses in the value of value function due to choosing the wrong alternative are modelled as a function of the characteristics of the true value function and the best alternative. The results show that the quality of decisions improves very quickly with improving information on weights. Determining at least the complete rank order of criteria is advisable, especially if the importances vary markedly among the criteria.
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How this classification was reachedexpand
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 | 0.020 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".