Intuitive Optimizing: Experimental Findings on Time Allocation Decisions with Newly Formed Ventures
Bibliographic record
Abstract
Decision making in an entrepreneurial context is explored using theoretical and experimental analysis. How individuals use their leisure and work time and how they distribute their work time between their regular wage job and a new venture is also investigated. Data were acquired from two questionnaires given to 112 German business and economics students to test behavioral decision theory. This methodology was used because of the likelihood of difficulty to control in a field study for the broad selection of factors believed to influence risky new venture decisions in natural settings. Findings indicate that economic predictions and behaviors are dependent on whether the venture dominates the wage job, the wage job dominates the venture, or neither one dominates the other. Conclusions show that, under experimental conditions where expected utility was maximized from allocating all working time to the venture, risk aversion moved downward with the number of hours allocated to the venture. When intermediate time allotments to the venture were optimal, risk aversion did not affect the number of hours allocated to wage jobs and the total number of working hours. (JSD)
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 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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".