The Equilibrating and Disequilibrating Effects of Entrepreneurship: Revisiting the Central Premises
Bibliographic record
Abstract
Research summary We review existing theoretical propositions on the equilibrating and disequilibrating effects of entrepreneurship in the market process. We then introduce a game theoretical model of the market process and employ computer simulation to analyze it through time. The formal analysis suggests that entrepreneurship as the creation of new opportunities may not always be disequilibrating, and entrepreneurship as the discovery and exploitation of existing opportunities may not always be equilibrating. We identify specific conditions that produce counterexamples to the generic equilibration and disequilibration propositions previously considered to be the central premises of entrepreneurship research. Managerial summary Many entrepreneurs advance society by building businesses around creative new ideas. Yet, other entrepreneurs start businesses by discovering opportunities to profit without necessarily innovative ideas. In reality, most entrepreneurship involves both creation and discovery. We run computer simulations of a small hypothetical economy to analyze the impact of creation and discovery actions on the extent to which the economy contains unexploited opportunities at any given time. Our results largely support previous ideas on how entrepreneurs help clear the markets by discovering opportunities or how innovations disrupt the market through creative destruction. Our results also highlight ways in which these ideas may be oversimplified and may have boundary conditions.
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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 | 0.010 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.014 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".