Bibliographic record
Abstract
In this paper, we investigate the problem of a dominant company facing entry of a “competitive fringe” (smaller competitor or fringe of smaller competitors). We seek to identify pricing and advertising (or other promotional strategies) that maximize long-term profits for the dominant firm, under possible reactions of the competitive fringe. Two main situations are considered: •The firms in the fringe are price-takers, but they advertise. •The firms in the fringe are not price-takers and advertise. The possibility of a passive reaction, in the case of a very small fringe, is considered as a particular case. We assume that the rate of change of fringe sales is dynamically related to the current sales, price and advertising efforts of both the dominant firm and the fringe. The higher the dominant firm price, the faster fringe entry. The higher dominant firm advertising effort, the slower fringe entry. Fringe advertising and pricing may counterbalance these effects. Formulating a dynamic game, with the dominant firm as a leader and the fringe as a follower, we present a new methodology for providing time-invariant feedback Stackelberg equilibrium. The methodology relies on finding the relationship between the co-state variables and the state variable. The equilibrium solution is obtained in an implicit form by solving a set of two backward differential equations. To show the applicability of our solution to real situations, we use data from the U.S. long-distance market and find optimal decision rules for AT&T facing the entry of MCI and Sprint during the 1980-1990 period. The feedback equilibrium indicates that while AT&T's price is decreasing when fringe (MCI and Sprint) sales increase, the fringe price is increasing. AT&T's advertising is increasing with fringe sales while the fringe's advertising increases and then decreases. The comparison with actual behavior indicates that AT&T has adhered closer to the optimal solution in both price and advertising than the fringe.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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; both teacher heads agree on what is shown here.
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".