Eliciting farmers’ subjective probabilities, risk, and uncertainty preferences using contextualized field experiments
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.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".