Designing a Sustainable Two‐Tier Service System with Customer's Asymmetric Preference for Servers
Bibliographic record
Abstract
This study considers a public service system consisting of two service providers with different service capacities and qualities, such as healthcare or transportation systems. Such a system is called a two‐tier service system (TTS). The service provider with higher service capacity and quality charges a higher price and is called a “charge service provider,” denoted by CSP, while the other service provider charges a lower price and is called a “free service provider,” denoted by FSP. We study the TTS where customers prefer the CSP to the FSP in spite of the higher price of CSP. The strong preference of CSP to FSP, which can be due to bounded rationality, leads to imbalanced workload for the TTS which implies an inefficient use of limited resources. Although both can offer a common set of services, the CSP is overly demanded and highly congested, while the FSP is under‐utilized. We first study analytically an extreme case where the preference for CSP is so strong that customers choosing the CSP are delay insensitive and those choosing the FSP are delay sensitive. Then, we extend our analysis to more general cases where customers are delay sensitive in both channels and have asymmetric preferences for service providers. Using a stylized queueing model and equilibrium customer choice analysis, we show that under certain conditions, the two‐tier system may reduce congestion and the total social cost in the system. This study offers social planners the guidance in designing less congested, more cost‐efficient, and sustainable public service systems.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".