Measuring belief-dependent preferences without data on beliefs
Bibliographic record
Abstract
This Zenodo project contains the files necessary to replicate the results of our study "Measuring belief-dependent preferences without data on beliefs". In this paper we derive bounds on the causal effect of belief-dependent preferences (reciprocity and guilt aversion) on choices in sequential two-player games without data on the (higher-order) beliefs of players. We show how informative bounds can be derived by exploiting a specific invariance property common to those preferences. We illustrate our approach by analyzing data from an experiment conducted in Denmark. Our approach produces tight bounds on the causal effect of reciprocity in the games we consider. These bounds suggest there exists significant reciprocity in our population – a result also substantiated by the participants’ answers to a post-experimental questionnaire. On the other hand, our approach yields high implausible estimates of guilt aversion – participants would be willing, in some games, to pay at least 3 Danish crowns (DKK) to avoid letting others down by one DKK. We contrast our estimated bounds with point estimates obtained using data on stated higher-order beliefs, keeping all other aspects of the model unchanged. We find that point estimates fall within our estimated bounds, suggesting that elicited higher-order belief data in our experiment is weakly (if at all) affected by various reporting biases.
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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 | 0.017 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 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".