Competitor Orientation and the Evolution of Business Markets
Bibliographic record
Abstract
Competitor orientation, i.e., the focus on beating the competition rather than maximizing profits, seems to thrive in business situations despite being, by definition, suboptimal for profit-maximizing firms. Our research explains how a competitor orientation can persist and even thrive in equilibrium in markets that reward only profits. We apply evolutionary game theory to business markets where reputation matters. We use three games that represent classic interactions in business marketing: Chicken (to illustrate competition for product adoption), the Battle of the Sexes (channel negotiations), and the Prisoners' Dilemma (pricing battles). Initial populations are assumed to have both profit-maximizing managers and competitor-oriented managers (i.e., those who gain additional utility from beating others). We demonstrate that a competitor orientation can survive in equilibrium despite selection that is based solely on profits. Using Chicken, we show that a competitor orientation thrives and can even overrun the population. We use the Battle of the Sexes to show that a competitor orientation will overrun one population in a two-sided negotiation (e.g., all retailers in a retailer/manufacturer dyad). Last, using the Prisoners' Dilemma, we show that competitor orientation is not selected against. We conclude that evolutionary profit-driven selection pressures cannot be assumed to eliminate nonprofit-maximizing behavior even when selection is based purely on profitability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".