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Record W2005247542 · doi:10.2307/3505089

Law Games: Defeasible Rules and Revisable Rationality

2008· article· en· W2005247542 on OpenAlexaff
Bruce Chapman

Bibliographic record

VenueTSpace · 2008
Typearticle
Languageen
FieldNeuroscience
TopicFree Will and Agency
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsRationalityDefeasible reasoningDefeasible estateEpistemologyPrima facieEcological rationalityComputer scienceMathematical economicsLaw and economicsSociologyPhilosophyMathematics

Abstract

fetched live from OpenAlex

In this paper I try to argue that the special sort of structure that links legal rules to their exceptions, namely, the structure of defeasibility, provides an account of rule-based decision making which is not exactly equivalent to rule-bound decision making, that is, to invariably following the rules. While defeasibility provides conceptual space for the categorical guidance of rules, it also allows for their non-absolute character, or their revision in the face of countervailing factors. I have also argued that something like a defeasible conception of rationality is what is required for the theory of games. What is needed there is a conception of rationality that, like rules, is sufficiently strong to provide some prima facie guidance for conduct at various points of choice, while at the same time not so strong as to preclude (empirically or logically) the possibility of being at those points of choice at all. Both legal rules and the rules of rationality are neither wholly descriptive nor wholly prescriptive in nature. In this they are quite different from both scientific laws, which are taken to provide descriptions of what `is`, and moral laws, which are thought to provide prescriptions for what `ought` to be. Both legal rules and the rules of rationality seem to bridge this is-ought divide. It should not be surprising, therefore, that legal rules and the rules of rationality might have a common structure, and that this structure, a structure that needs to accommodate both regularity and (an independent) particularity, might be provided by the concept of defeasibility.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.554
Threshold uncertainty score0.259

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.092
GPT teacher head0.322
Teacher spread0.230 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2008
Admission routes1
Has abstractyes

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