A Continuous Model for Multistore Competitive Location
Bibliographic record
Abstract
This paper presents a simple model to determine the location strategies of two retail firms planning to open a number of stores in a geographical market. Firms try to maximize their profit under a leader-follower type competition in which the number of stores is made endogenous by the introduction of fixed costs. A novel methodology is developed in which firms’ strategies are defined in terms of their location densities. This methodology leads to a model that is solvable analytically, and to several results on competitive location strategies. First, it is shown that if the follower decides to enter a market, he enters at least as strongly as the leader. Second, the leader can effectively deter entry even if she is severely cost-disadvantaged. However, in some cases the leader is better off by allowing the follower to enter the market. Third, the leader may also let the follower enter the market in some situations where she has a cost advantage. It is also shown that in situations where both firms enter the market, their location strategies are quite insensitive to model parameters.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.022 | 0.003 |
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".