Strategic Choices Based on Precision Service During the Construction of Unmanned Pharmaceutical Micro-Warehouses: A Dynamic Evolutionary Game Approach
Bibliographic record
Abstract
Unmanned pharmaceutical micro-warehouses (UPMWs) automate operations such as warehousing, sorting and selling by integrating the Internet of Things, artificial intelligence and other technologies. In pharmaceutical retail settings, UPMWs help to solve problems such as purchasing medication at night, enhancing consumers' purchasing experience and reducing pharmaceutical enterprises' costs. However, the construction feasibility of UPMWs is not well researched. To bridge this gap, this paper considers the precise service capabilities of UPMWs. It builds a tripartite evolutionary game model involving pharmaceutical enterprises, consumers and suppliers. It also conducts extensive robustness check using the stability test of equilibrium points, numerical simulation, and scenario expansion verification. Our major findings are as follows. First, managers of pharmaceutical enterprises exhibit a non-profit-oriented nature, and the preference of pharmaceutical enterprise managers plays a decisive role. Second, consumers opt to use UPMWs only when the difference in utility between an UPMW and the traditional model exceeds the difference between the attention cost of consumers and added utility. Suppliers' choices depend on the disparity between upgrade costs and benefits, and their acceptable cost threshold increases as both the pharmaceutical enterprise's construction willingness and consumers' usage intention increase. Third, the precise service ability, sales proportion and acceptance of UPMWs influence the evolution rate and direction of the modelled game. Specifically, when these values are excessively low, the system evolves from ‘building UPMWs’ to ‘not building UPMWs’. Overall, our findings provide a new analytical framework and practical insights for resource allocation and collaborative management of UPMW Construction.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".