A combinatorial theory of institutional invention
Bibliographic record
Abstract
Abstract From climate change to disruptive technologies, policymakers constantly face new problems calling for unprecedented institutional solutions. Yet, we still poorly understand the inventive process leading to the emergence of new institutional forms. Existing theories argue that exogenous changes provide incentives and opportunities for institutional invention. However, they fail to explain how the inventive process endogenously structures their emergence. Drawing from complexity theory and Brian Arthur's work on technological inventions, we develop a structural theory recasting the process of inventing new institutions as the combination of pre-existing institutions. Building on three assumptions related to this combinatorial process, we argue that the distance between institutions shapes the emergence of new institutional forms and their regime's trajectory. Following the initial take-off in the number of institutional inventions at the creation of a regime, we expect the rate of institutional inventions over replications will slow down as nearby institutions are combined and accelerate as distant ones are combined. We illustrate these expectations by looking at three regimes: data privacy, climate governance, and investment protection. Together, they showcase how our combinatorial theory can help make sense of the emergence of unprecedented institutions and, more generally, the pace of unfolding complexity in various international regimes.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".