Managing retrial queueing systems with boundedly rational customers
Bibliographic record
Abstract
Bounded rationality includes potential cognitive deficits that limit human rational behaviour. In this paper, we allow for estimation errors of their utility and consider bounded rationality in queueing models with retrials. Upon arrival, customers decide whether to join the system based on their perceived utility, and their choice behaviour is characterised by a logit choice model. For a revenue-seeking server and a social planner, we investigate corresponding optimal pricing strategies. We find that the revenue-optimal price is no longer socially efficient in general but depends on the retrial rate. There exists a threshold such that the socially optimal price is greater than the revenue-optimal one when the retrial rate is below this threshold; otherwise, the revenue-optimal price is greater. A larger bounded rationality widens the gap between social welfare under the revenue- and socially optimal prices. Furthermore, the server’s revenue may increase or first decrease and then increase with respect to customers’ rationality levels, and the server may find it beneficial to reveal waiting time information when customers’ rationality is high. Finally, failing to account for customers’ bounded rationality can lead a significant revenue loss for the server and the proportion of such a revenue loss is close to 1 as customers become fully boundedly rational.
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How this classification was reachedexpand
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.042 |
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; both teacher heads agree on what is shown here.
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".