Alternative Measures for Modeling Risk and Expected Utility Theory (Risk Adjustment, Measurement and Attitude)
Bibliographic record
Abstract
In this paper, we propose alternative measures for modeling risk to be used in Expected Return (ER) calculations instead of “Utility” of Expected Utility Theory (EUT). These new measures are based on the idea that: First, there is a need to make a Risk-Adjustment to make the risky-alternative comparable with the risk-free alternative. Second, the new measures can be more in congruence with actual risk attitude existing in the market. This is possible by making the Risk-Measurement to be included in the probability function and the expected return to be based on the market portfolio on the Capital Market Line (CML) tangent to the Markowitz Bullet as defined in CAPM of finance theory. Finaly, these new measures may also make it possible to include Risk-Attitude in the “Probability” or “Returns” functions and there may no longer be need for a “Utility” function. The two measures introduced in the paper can be used with certain advantages as a substitute for expected utility theory of economics, game theory and decision theory in predicting choice behavior under risk, and in a more correct financial derivatives pricing using binomial models of finance.
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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 | 0.014 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.011 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".