Maxificing: Life on a Budget; or, If You Would Maximize, Then Satisfice!
Bibliographic record
Abstract
The Issue In recent times, the view that the doctrine of maximization is too something — too demanding, too unrealistic, too stringent, or some such thing — has come into a certain vogue. Not that we are supposed to “minimize,” however: The Hegelian synthesis proposed has it that instead, the rational individual “satisfices.” Roughly, the idea is that we set a threshold such that the next sample of what we are looking for — call it F — that meets that criterion is to be chosen, even though we may be well aware that somewhere out there, there are bigger and better Fs. The question has always been what the status of the satisficing template is by comparison with the maximizing one. Prima facie , if the rational chooser is confronted, essentially simultaneously, with two samples of F, one clearly better than the other, and he must choose between them, then he will choose the better. It seems incomprehensible that he should choose the worse, in the absence of special contexts or reasons. Is the satisficer insisting that he do so? There is considerable temptation simply to say that one who prefers x to y even when he agrees that y is better is eo ipso irrational. If we do say this, it would be, I think, because of the practical commitments of appraisal words like ‘better’ and ‘good.’ Is to say that x is good to imply that one would choose x, other things being equal?
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.031 | 0.011 |
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".