Bibliographic record
Abstract
The axiomatic foundation of the expected utility theory (which states that given a set of uncertain prospects individuals pick up the prospect which yields the highest expected utility) was first laid down by Von Neumann and Morgenstern (1947). This axiom has come under severe criticisms in recent years. A large number of experiments have shown that in making decisions involving uncertain prospects people frequently violate the independence axiom. In this paper we shall consider the problem of choice under uncertainty from a wider point of view and we shall examine the nature of the restriction imposed by the axiom of independence. We shall use the mean-variance utility function to prove our point. Then we shall consider a weak version of the independence axiom namely the weak* axiom of independence. This is the point of departure from the expected utility theory to the realm of the non-expected utility theory. The weak* axiom allows aversion to pure uncertainty and, in the context of the mean-variance utility theory, it is compatible with utility being an increasing function of expected returns at all levels.
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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 | 0.014 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".