Random Experimentation and Exceptional Outcomes in Entrepreneurship
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper explores a new paradigm in entrepreneurship, characterized by random experimentation and exemplified by indie makers and solopreneurs like Pieter Levels and Daniel Vassallo. In contexts where uncertainty is extremely high and outcomes follow a heavy-tailed distribution, entrepreneurship can begin to resemble gambling. In response, indie entrepreneurs adopt deliberate experimentation strategies to manage this randomness based on their understanding of power-law dynamics. This approach emphasizes diversification and uncertainty-hedging value of experimentation over the learning and adaptation value of experimentation, which is emphasized in other theories such as the lean startup. Random experimentation focuses on breadth over depth and accepts that outcomes are often shaped more by chance than by effort. Through the lens of order statistics, we adopt a modeling approach that allows us to calculate a baseline function for the value of experimentation. Entrepreneurs may find this useful in designing their experimentation strategy as it allows them to calculate the optimal number of experiments when the probability distribution of outcomes is known. We showcase the predictive power of our modeling approach by illustrating the ability of our model to predict the frequency of outliers in the real-world context of crowdfunding campaigns.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it