Credible Commitments and the Right to Bear Arms: Viewing the Second Amendment from a Game-Theoretic Perspective
Bibliographic record
Abstract
For most of its existence, the Second Amendment was largely ignored by Constitutional scholars. Recently, a veritable cottage industry has developed in which two distinct camps have surfaced: so-called “Standard Modelers,” who argue that individuals have a right to bear arms for self-defense, the defense of the state, and, in the most extreme examples, to overthrow the government should it become tyrannical, and those who view the Second Amendment as a collective right vested in the state militias for the purposes of law enforcement, to protect against foreign aggression, to quell domestic insurrection, and as a check against federal overreach. Despite the enormous gulf between them, both sides agree that the right to bear arms provides a counterbalance against the federal government. This paper uses insights from game theory to shed new light on the adoption of the Second Amendment. The states suffered a commitment problem. They wished to cooperate with each other by founding a new republic, but feared the consequences of doing so: losing their freedom to a powerful government. The Second Amendment militated against the need for a large federal army, acted to counterbalance federal forces, and created the offensive means with which to confront a tyrannical government.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.024 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".