Non-strategic Econometrics (for Initial Play)
Bibliographic record
Abstract
Modelling agent preferences has applications in a range of fields including economics and increasingly, artificial intelligence. These preferences are not always known and thus may need to be estimated from observed behavior, in which case a model is required to map agent preferences to behavior, also known as structural estimation. Traditional models are based on the assumption that agents are perfectly rational: that is, they perfectly optimize and behave in accordance with their own interests. Work in the field of behavioral game theory has shown, however, that human agents often make decisions that are imperfectly rational, and the field has developed models that relax the perfect rationality assumption. We apply models developed for predicting behavior towards estimating preferences and show that they outperform both traditional and commonly used benchmark models on data collected from human subjects. In fact, Nash equilibrium and its relaxation, quantal response equilibrium (QRE), can induce an inaccurate estimate of agent preferences when compared against ground truth. A key finding is that modelling non-strategic behavior, conventionally considered uniform noise, is important for estimating preferences. To this end, we introduce quantal-linear4, a rich non-strategic model. We also propose an augmentation to the popular quantal response equilibrium with a non-strategic component. We call this augmented model QRE+L0 and find an improvement in estimating values over the standard QRE. QRE+L0 allows for alternative models of non-strategic behavior in addition to quantal-linear4.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.069 | 0.017 |
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".