Risk Aversion, Stochastic Dominance, and Rules of Thumb: Concept and Application
Bibliographic record
Abstract
Consider the following generic and fairly narrowly defined choice problem.An individual must choose from amongst a discrete and finite set of lotteries.Suppose for concreteness that each lottery represents a monetary payoff, and that all the lotteries are constructed so as to be comparable.As a running example, the lotteries could represent incomes in different countries in given years, and comparability could be ensured, at least in principle, by converting to a common metric using inflation-and purchasing power parity-adjusted exchange rates.Each lottery is characterized by a corresponding distribution function, that is known with certainty.The uncertainty arises because, if the individual picks a particular distribution, he will receive a payoff that is a random draw from that distribution.How is he to choose amongst these lotteries?Assuming that his preferences are such that they admit of a Von Neumann-Morgenstern (VNM) expected utility representation greatly simplifies the problem.Now, the individual will pick the lottery that gives him the maximum level of expected utility.If the individual is risk-neutral, so that his expected utility function is linear in the monetary payoff, the problem is not especially interesting.From conventional economic theory, we know that maximizing expected utility in this case will reduce to maximizing the expected payoff, given the linearity of the expectation operator.The individual will simply pick the lottery that has the highest corresponding expected value, assuming, as I shall do throughout, that this (as all other relevant moments) exists and is well-defined for all the lotteries.In our example, this would involve picking the country whose income distribution has the highest mean income, i.e., income per capita.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".