Eliciting farmers’ subjective probabilities, risk, and uncertainty preferences using contextualized field experiments
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Subjective probabilities as well as risk and uncertainty preferences influence many farmers’ decisions. Few contextualized field experiments were recently conducted to elicit farmers’ risk preferences. Contextualized field experiments use nonabstract framings that are familiar to subjects. Despite adding of context can undermine internal validity, such experiments are increasingly used in applied economics. Contextualized field experiments were never used to elicit farmers’ uncertainty preferences. This paper aims to fill this gap in the literature. This required the development of a new approach in which uncertainty preferences were estimated while controlling for farmers’ subjective probabilities regarding future agricultural outcomes. The experiment involves Scottish farmers’ decisions to plant traditional or new potato varieties. Monetary incentives and incentive compatible elicitation techniques, such as quadratic scoring rules and certainty equivalent multiple price lists, were used. Results from the estimation of Fechner models using maximum likelihood estimation procedures show that failure to control for subjective probabilities generates an underestimation of estimated uncertainty preferences. Farmers are more averse to uncertainty than risk, and their choices are noisier under uncertainty than risk.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it