Legal Analysis of Economics: Solving the Problem of Rational Commitment
Bibliographic record
Abstract
This Article offers a "legal analysis of economics" in contradistinction to the prevailing "economic analysis of law." The economic problem that forms the subject matter of the theoretical legal analysis is the problem of rational commitment. The difficulty here is that an agent can have a reason, or a preference, to commit to do something that he will have no reason actually to do, or which will be contrary to preference when the time comes actually to do it. Familiar examples include the problem of making credible threats or promises. \n \nThis Article develops an account of the rational actor that differs from that conventionally supplied by rational choice theory to handle this problem. Borrowing from some work by the philosopher-economist John Broome, the Article argues that rational decision-making involves more than acting on the balance of reasons, or all-things-considered preferences; it also consists in following the normative requirements of practical rationality. After articulating the logical distinction between reasons and normative requirements, the Article argues that this richer account of the rational actor is manifested in common law adjudication and, more particularly, in the special relationship that exists between decided cases and defeasible legal rules. The Article suggests that this is exactly the sort of rationality that the economist needs to comprehend, and solve, the problem of rational commitment.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.025 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".