Computational modeling of entrepreneurship grounded in Austrian economics: Insights for strategic entrepreneurship and the opportunity debate
Bibliographic record
Abstract
Research Summary This paper makes three key arguments: (a) that computational modeling is a key methodological tool that can aid in the development of formal economic foundations for entrepreneurship that are grounded in Austrian economics; (b) that computational modeling grounded in Austrian economics can serve to integrate models of competitive advantage in equilibrium (economic foundations of strategy) with models of the entrepreneurial function in disequilibrium (economic foundations of entrepreneurship), thereby providing an economic foundation for strategic entrepreneurship; and (c) that the mathematical precision of computational modeling grounded in Austrian economics can serve to clarify the logic behind differing perspectives that have led to debates fueled by the imprecision of natural languages, such as the opportunity debate in the entrepreneurship literature. Managerial Summary This paper asks: what are the implications for strategic entrepreneurship if we model it on the foundations of an economic logic developed by the Austrian economics school of thought in a way that integrates into existing economic logics of strategy and competitive advantage? The Austrian economics logic highlights the role of disequilibrium, creative imagination, time and uncertainty, while the traditional economic logic emphasizes the importance of structural sustainable advantages in equilibrium. In this integrative view, even if a firm does not enjoy any competitive advantage over rivals, opportunities for profit or development of competitive advantage still exist through entrepreneurial action. An entrepreneur adopting this view in practice would recognize their own agency, but also the limits to it and the role of external factors.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".