Entrepreneurial Game Theoretic Approach to Planning Flexibility and Environmental Scanning
Bibliographic record
Abstract
This study focuses on planning flexibility and environmental scanning from the standpoint of corporate entrepreneurship, as represented by Top management. Game theory was applied in this study. In doing this, the problem was viewed from a “worst case design concept” which can be summarised as follows: The intrapreneur (the entrepreneur within a firm or corporate entrepreneurs) wishes to minimise the cost of operation while the environment tries to maximize the cost of operation. Hence, we introduce a cost functional, which is the cost of risk faced by the intrapreneur. We assume that the entrepreneur can apply flexibility in planning, hence denote this as control, while the environment that is needed to be scanned is denoted as uncertainty. The model of game is applied to allow for saddle point (a point where there is no ‘gain’ no ‘loss’) solution. The implication of this is that for a saddle point to exist in a “worst case” situation, the intrapreneur is likely to breaking-even. Thus, a proper mathematical treatment is given for such a game problem.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".