Shared Mental Models: Insights and Perspectives on Ideologies and Institutions
Bibliographic record
Abstract
SUMMARY This article leads off a special symposium comprised of a select group of public choice economists and political scientists that assembled to reflect on the important contribution that Arthur T. Denzau and Douglass C. North’s seminal piece on Shared Mental Models (1993) has made over the last quarter of a century. Relatedly, we apply concepts from Denzau and North’s Shared Mental Models to suggest a modified model of the Nash equilibrium used in non‐cooperative game theory to help us operationalize the “learning path” by which we can move from “siloed” thinking to a wider “systems” view of organizations, our environment, and indeed, the world. Our model has implications for the way we respond to economic crises, financial meltdowns, and global health epidemics, such as the COVID‐19 pandemic.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.029 |
| Scholarly communication | 0.013 | 0.021 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".