Cognition, Network Structure, and Learning in Top Managers’ Interpersonal Networks
Bibliographic record
Abstract
In an uncertain, complex and competitive business landscape, top managers’ ability to quickly and effectively learn from others in their interpersonal networks could give their companies a competitive edge. We propose an integrative cognitive framework to explain how their interpersonal learning outcomes might be shaped by the interplay between cognitive attributes, such as impatience and conservatism, and the degree to which extremely well-connected individuals, or hubs, is probable in their networks. Using agent-based simulation models, we find that impatience and conservatism may lead to poor interpersonal learning outcomes, particularly if top managers belong to less “hubby” networks. In addition, distorted information has the potential to compromise learning, regardless of the level of impatience or conservatism and network “hubbiness”. Moderate to high levels of initial knowledge variety may also hamper interpersonal learning if top managers are highly impatient or conservative, and operate in networks with a certain degree of “hubbiness”. Although the adoption of less-than-ideal standards for judging the accuracy of beliefs may expedite learning, it is generally costly in terms of low learning performance levels. However, more impatient or conservative top managers may improve their learning performance by primarily targeting hubs, or some mix of hubs and nearby contacts.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".