Countering Bounded Rationality: Cognitive Integration As Basis of Superior Performance
Bibliographic record
Abstract
The notion of bounded rationality and the behavioral theories, such as the Behavioral Theory of Strategy (BTS) that rest on it, have been the dominant premise of research on strategic decision making. This paper counters that premise by introducing an alternative, integrative theory of epistemology, labeled Cognitive Integration. It is described and contrasted systematically with the BTS to offer a more explicit explanation of the mental processes of strategic decision makers. This description and contrast support the argument that a strategic decision maker’s cognitive integrations-their validity and extent-are the key drivers of superior long-term performance of firms. Unlike the associative reasoning approach proposed by some BTS theorists, hierarchical integration from perceptual observations to abstract principles and contextual integration of newly acquired knowledge offer a way to condense and use a wealth of information without omitting any. I argue that such cognitive integration and application of principles by strategic decision makers are particularly advantageous when decisions are complex and projecting long-term consequences is difficult, and facilitate superior performance. These conceptual arguments about cognitive integration are illustrated with the example of John Allison, CEO of the above average-performing Fortune 500 bank BB&T for 20 years.
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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 | 0.010 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.013 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".