Thinking strategically about thinking strategically: the computational structure and dynamics of managerial problem selection and formulation
Bibliographic record
Abstract
Abstract A new model of managerial problem formulation is introduced and developed to answer the question: ‘What kinds of problems do strategic managers engage in solving and why?’ The article proposes that a key decision metric for choosing among alternative problem statements is the computational complexity of the solution algorithm of alternative statements. Managerial problem statements are grouped into two classes on the basis of their computational complexity: P‐type problems (canonically easy ones) and NP‐type problems (hard ones). The new model of managerial cognitive choice posits that managers prefer to engage with and solve P‐type problems over solving NP‐type problems. The model explains common patterns of managerial reasoning and decision making, including many documented ‘biases’ and simplifying heuristics, and points the way to new effects and novel empirical investigations of problem solving‐oriented thinking in strategic management and types of generic strategies, driven by predictions about the kinds of market‐ and industry‐level changes that managers will or will not respond to. Copyright © 2009 John Wiley & Sons, Ltd.
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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 | 0.007 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".