The effects of ambiguity on entrepreneurship
Bibliographic record
Abstract
Abstract We incorporate ambiguity (Knightian uncertainty) into a classic model of entrepreneurship to analyze, among other things, its effects on the optimal level of business startups, the relation between total assets and the size of the entrepreneurial investment, the effects of increasing ambiguity on developing new ventures, and the decision to self‐select into entrepreneurship for an indifferent decision maker. We first show that, under the monotone‐likelihood ratio property, the introduction of ambiguity negatively affects the optimal entrepreneurial investment, something that is consistent with most experimental evidence about entrepreneurial choice under ambiguity. Then, we show that the classical explanations for the positive correlation between total assets and business startups based on decreasing absolute risk aversion preferences and prudent behavior can be challenged when ambiguity is incorporated into the analysis, and we provide the conditions that guarantee that the traditional comparative static result under risk is replicated under ambiguity. We also show that increases in ambiguity aversion reduce entrepreneurial activities. Finally, we discuss our results under alternative ways of modeling ambiguity.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".