Competing with Copycats When Customers Are Strategic
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this paper, we use a two-period game theoretical model to examine the decisions of a manufacturer and a copycat firm who are competing for strategic customers. The manufacturer decides on the amount of its market expansion advertising investment in the first period and on its pricing strategy in both periods. Advertising increases the “size of the pie,” but eventually the manufacturer may end up inadvertently sharing the benefits with the copycat. After the first period, the copycat makes a market-entry decision, and, if it opts to enter, it also decides on a pricing strategy. The customers are strategic, and they decide whether or not to buy, when to buy, and which product to buy. We find that, interestingly, lower quality levels of the manufacturer’s product may increase the manufacturer’s prices and profit. Moreover, the manufacturer may be worse off when customers are more likely to purchase its product immediately rather than wait for a price reduction or for the copycat’s product. Finally, the copycat may be worse off when customers withhold their purchases in the first period in anticipation of the possibility of copycat product becoming available in a later period. The online appendix is available at https://doi.org/10.1287/msom.2016.0613 .
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it