The Explore-Exploit Dilemma: Role of Time Horizon on Informant Selection
Bibliographic record
Abstract
The explore-exploit dilemma is a pervasive problem within the context of social information gathering faced by all individuals. The decision to exploit an old source of information or explore a new source of information involves a trade-off between acquiring new knowledge from the environment to reduce uncertainty (exploration) or immediately receiving rewards (exploitation) (Meder et al., 2020). An important factor influencing information gathering within the context of the explore-exploit dilemma is time. Research has shown that in shorter time horizons, adults are more likely to exploit old sources of information and in longer time horizons, adults are more likely to explore new sources of information. Thus, when given more opportunities to explore, adults perceive the benefits of exploring novel stimuli to outweigh the immediate reward of exploring an old source of information (Wilson et al., 2014). Prior accuracy of the informant has also been shown to influence the decision to explore or exploit sources of information. The current study aims to extend the work of Gutzin (2021) and will consist of two parts; study one will replicate the work of Gutzin (2021) directly, studying adults online. Study two will extend the work of Gutzin (2021) to accommodate an online platform with children. The previous studies examining this effect had small sample sizes and in turn low statistical power. To address this, the current study will be composed of larger sample sizes so that final conclusions regarding this effect can be made. In line with previous literature and studies, it hypothesized that both adults and children will tend to exploit the familiar informant in shorter time horizons and explore the novel informant in longer time horizons and prior accuracy of the familiar informant will affect exploration tendencies.
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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 | 0.016 | 0.128 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".