Managing Service Systems with Unknown Quality and Customer Anecdotal Reasoning
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We consider service systems where customers do not know the distribution of uncertain service quality and cannot estimate it fully rationally. Instead, they form their beliefs by taking the average of several anecdotes, the size of which measures their level of bounded rationality. We characterize the customers’ joining behavior and the service provider's pricing, quality control, and information disclosure decisions. Bounded rationality induces customers to form different estimates of the service quality and leads the service provider to use pricing as a market segmentation tool, which is radically different from the full rationality setting. As customers gather more anecdotes, the service provider may first decrease and then increase price, and the revenue is U‐shaped. Interestingly, a larger sample size may harm consumer surplus, although it always benefits social welfare. When the service provider also has control over quality, we find that it may reduce both quality and price as customers gather more anecdotes. In addition, a high‐quality service provider may not disclose quality information if the sample size is small, while a low‐quality service provider may disclose if the sample size is large. Furthermore, as the expected waiting cost increases, information non‐disclosure is more attractive, thereby highlighting the importance of incorporating customer‐bounded rationality in congested settings.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it