Bibliographic record
Abstract
When discussing Arrow’s Impossibility Theorem (AIT) in engineering design, we find that one condition, Independence of Irrelevant Alternatives (IIA), has been misunderstood generally. In this paper, two types of IIA are distinguished. One is based on Kenneth Arrow (IIA-A) that concerns the rationality condition of a collective choice rule (CCR). Another one is based on Amartya Sen (IIA-S) that is a condition for a choice function (CF). Through the analysis of IIA-A, this paper revisits three decision methods (i.e., Pugh matrix, Borda count and Quality Function Deployment) that have been criticized for their failures in some situations. It is argued that the violation of IIA-A does not immediately imply irrationality in engineering design, and more detailed analysis should be applied to examine the meaning of “irrelevant information”. Alternatively, IIA-S is concerned with the transitivity of CF, and it is associated with contraction consistency (Property α) and expansion consistency (Property β). It is shown that IIA-A and IIA-S are technically distinct and should not be confused in the rationality arguments. Other versions of IIA-A are also introduced to emphasize the significance of mathematical clarity in the discussion of AIT-related issues.
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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 | 0.047 | 0.053 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.004 | 0.017 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.007 |
| 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".