Preferences and constraints: the value of economic games for studying human behaviour
Bibliographic record
Abstract
As economic games have spread from experimental economics to other social sciences, so too have critiques of their usefulness for drawing inferences about the ‘real world’. What these criticisms often miss is that games can be used to reveal individuals' private preferences in ways that observational and interview data cannot; furthermore, economic games can be designed such that they do provide insights into real-world behaviour. Here, we draw on our collective experience using economic games in field contexts to illustrate how researchers can strategically alter the framing or design of economic games to draw inferences about private-world or real-world preferences. A detailed case study from coastal Colombia provides an example of the subtleties of game design and how games can be combined fruitfully with self-report data. We close with a list of concrete recommendations for how to modify economic games to better match particular research questions and research contexts.
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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 | 0.026 | 0.087 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.023 |
| Scholarly communication | 0.007 | 0.016 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".