Need Second-hand Advice? The Timing of When People Seek Algorithmic Recommendations
Bibliographic record
Abstract
Abstract Algorithmic recommendations have drastically expanded in recent years to aid human decision-making. In this paper, we seek to understand the users of these tools and when, where, and why they obtain algorithmic advice. We do so examining data from two behavioural decision-making experiments ( N = 216) and applying the Timed Racing Diffusion Model (TRDM) across choices and response times. Our experiments find that people are sensitive to when algorithmic advice is worthwhile obtaining. Notably, our results privilege experience and show that opportunities to test the recommendation accuracy can be as useful as descriptive information stating the same. Our main finding, however, centers on the time-course of when individuals choose to obtain a recommendation. We find that over time, algorithmic advice is sought as a means to terminate difficult decisions that one cannot derive on one’s own. The TRDM proposes a unifying cognitive mechanism for this pattern of recommendation seeking based on decision urgency though our individual differences analyses identify a diversity of strategies adapted to the same decision environment. Overall, our findings characterise decision-makers as adept users of decision aid tools, and that despite the possibility of recommendation errors, individuals are capable of appreciating the utility of helpful, albeit imperfect, recommendations.
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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 | 0.006 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".