Learning About the Unknown: How Fast Do Entrepreneurs Adjust Their Beliefs?
Bibliographic record
Abstract
The extent to which entrepreneurs adjust theirbeliefs in light of new information instead of relying on past experience ismeasured. Using data from 1999 and 2000 on 700 self-employed Britons collectedby the British Household Panel Survey, a model was created in whichentrepreneurs continually receive valuable but noisy market signals about thetrue but unobserved productivity of their efforts, and then use thisinformation to update their expectations of unobserved productivity. Results show that entrepreneurs do exploit new information, but they givemuch more weight to their previous beliefs when forming expectations. Youngerentrepreneurs were found to respond more sensitively to new information thandid older entrepreneurs. There were no differences found with respect to menversus women entrepreneurs, employers versus nonemployers, and experiencedversus less experienced entrepreneurs. Overall, the rate of exploitation of newinformation was found to be relatively modest. Government provision ofinformation, education, and training can be tailored to be more effective atimproving entrepreneurs' responsiveness than grants or subsidies wouldbe. (LKB)
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".