Rewarded for unreliability: how the feedback of audiences advantages fast learners
Bibliographic record
Abstract
Actors who learn slowing are less sensitive to feedback from recent actions. Such slow learning is particularly beneficial in environments where it is not clear if an action will lead to a global maximum. We argue that fast learning can outperform slow learning when the role of audience is incorporated. Relative to slow learners, the performance samples offered by fast learners are more unreliable because fast learners tend to prematurely converge to alternatives that are likely inferior in the long run. Nevertheless, audiences may give favorable feedback to fast rather than slow learners if this sampling bias is not corrected for. Using simulation modeling, we demonstrate that the differential sampling process between fast and slow learners determines how audiences form expectations and react to performance deviations, and potentially reward fast learners for unreliability. We then empirically examine our argument using the Google Search data regarding the Canadian software firms from 2004 to 2013. The results support that fast learners tend to attract more (less) attention when outperforming (underperforming) the expectations than slow learners. More generally, we present a sampling account for why fast learning is not necessarily a suboptimal strategy because of the way bounded rational audiences respond to the performance differences among bounded rational actors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".