Individual differences in exploring versus exploiting and links to delay discounting
Bibliographic record
Abstract
Abstract Sometimes, we must choose between obtaining an immediate reward or foregoing it in favor of searching for a better reward elsewhere. Such decisions have been characterized as involving exploration‐exploitation trade‐offs. Here, we studied the reliability and basis of individual differences in tasks involving choices between exploration and exploitation. In Studies 1, 2, and 4, we found little evidence for a stable individual difference in tendency to explore (vs. exploit). Additionally, we tested delay discounting as a potential predictor of individual differences in exploration. In Studies 3 and 4, we found that delay discounting was inconsistently predictive of exploration behavior. Our results support the claim that people adapt their exploration behavior to the environment in which they find themselves. This adaptation overrides any general preference to explore environments more or less than other people. Our results also suggest that predictors of exploration may be exclusively restricted to the particular environment in which they were observed. Implications for past and future research of exploration‐exploitation decision making are discussed.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".