The New Industrial Organization: Ecosystem Competition
Bibliographic record
Abstract
This paper characterizes a new industrial organization framework for analyzing ecosystem formation and competition by recognizing the Schumpeterian force of creative destruction. Economists’ framework of profit maximization is replaced by a Welfare Enhancing framework (WEF)as a more pragmatic and realistic characterization of reality. Consumers are not fish in the ocean waiting to be preyed upon; they have free choice and broad lifestyle choices. The supply and demand framework is still relevant even though profit maximization in the theoretical sense that it has been technically crafted by economists may not. Firms as epistemic communities are more fitting as the behavioral assumption that can be more pragmatically applied. By using graphs and examples, three types of ecosystems are discussed, each sharing the commonality of data management as a driver for its respective ecosystem. The first two types of data management, coupled with pricing, bundling, and various industrial organization conducts, help to promote the welfare-enhancing growth of their respective ecosystems in an innocuous manner. The third type has an electrifying component resembling features of “two-sided” markets that may require Antitrust regulation. The key difference between the third and the first two types of competition is that the third type could lock in data with a specific investment of productivity less than the ideal optimal, thus reducing welfare rather than enhancing welfare.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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 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".