To Cheat or Not? Results from Behavioral Experiments on Self-monitoring in Vietnam
Bibliographic record
Abstract
The central question that we ask in this paper is: do people cheat or behave dishonestly when they are unsupervised? This question is motivated by our work on environmental management programs in developing countries where local conservation effort can be either too expensive or difficult to monitor by outsiders. We combine responses from a household survey with results from a set of field experiments in rural Vietnam to examine how people behave when they are unsupervised, and how well are they able to predict the behavior of others in their community. Both the survey and the field experiments are structured along the lines of an environmental project in which local people receive an incentive for providing their time and labor to undertake project activities. Our total sample size is 400 with experimental treatments varying by group size, whether people receive cash or in-kind incentives, and whether they are monitored. In contrast to existing studies that predict compulsory cheating, we find very little cheating behavior among our subjects. Moreover, people can accurately predict the behavior of others except in a scenario when people collect cash incentives for others. In that case, people become extra cautious than what they were predicted to be. We explain these results by invocating the theory of self-concept maintenance and conclude with a discussion on potential applications of these results.
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.
How this classification was reachedexpand
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 | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".