Bibliographic record
Abstract
The dissertation contains three articles. All of them focus on investigating change in decision resulted from change in risk. When decision makers invest in effort to reach their targets, they face multiple sources of risk: first the risk of failure and second the noise that surrounds either the target or the initial situation. In the first article, we examine how effort is adjusted to account for changes in this risky environment. Central dominance (CD) introduced in Gollier (1995, Journal of Economic Theory) is a risk concept that differs from stochastic dominance in an important way. In particular, CD implies a deterministic comparative statics of change in decision when risk changes, but SD does not have such implication. In the second article, we propose the first test of central dominance, which amounts to checking a functional inequality. We derive the asymptotic distribution of a lower bound of the proposed test and suggest a bootstrap procedure to compute the critical values. We also conduct simulations to evaluate the performance of this test. Our empirical study finds CD relations among Canadian family income distributions in different years and results in interesting policy implications. In the third article, we propose a method to calculate risk measures proposed by Aumann and Serrano (2008) and Huang et al. (2012). This method utilizes information about mean, variance, skewness, and kurtosis of a distribution. We find that the risk measure in Huang et al. (2012) is sufficient information for investment decision in a simple portfolio selection model, and therefore we construct a trading strategy with respect to the measure. Our empirical results show that this trading strategy outperforms any buy-and-hold trading strategies during sample period from January 2001 to October 2009.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".