Bibliographic record
Abstract
This paper presents a novel decomposition approach for measuring deterrence motives in dynamic oligopoly games. Our approach yields a formalized, scale-free, and interpretable measure of deterrence motives that informs researchers about the proportion for which deterrence motives account of all entry motives. In addition, the decomposition leads to a set of conditions for counterfactual analysis where hypothetical scenarios with deterrence motives eliminated can be explored. We illustrate the use of our measure and counterfactual by conducting an empirical case study about the dynamics of coffee chain stores in Toronto, Canada. The inferred deterrence motives suggest that a noticeable proportion of entry motives can be attributed to deterrence; it can be as high as 43% for the increasingly dominant coffee chain, Starbucks, in certain types of markets. Finally, counterfactual analysis confirms that deterrence motives are indeed associated with Starbucks’ aggressive presence as the number of its outlets and its market share are markedly lower once these motives are eliminated. This paper was accepted by Matthew Shum, marketing. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2023.4864 .
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".