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Record W2945850595 · doi:10.7202/1059501ar

COSTLY FALSE BELIEFS: WHAT SELF-DECEPTION AND PRAGMATIC ENCROACHMENT CAN TELL US ABOUT THE RATIONALITY OF BELIEFS

2018· article· en· W2945850595 on OpenAlexvenueno aff
Mélanie Sarzano

Bibliographic record

VenueLes ateliers de l éthique · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicEpistemology, Ethics, and Metaphysics
Canadian institutionsnot available
FundersUniversité de FribourgUniversität BaselSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungNational Science Foundation
KeywordsIrrationalityRationalityDilemmaIrrational numberDeceptionSelf-deceptionPsychologySocial psychologyEpistemologyPositive economicsPhilosophyEconomicsMathematics

Abstract

fetched live from OpenAlex

In this paper, I compare cases of self-deception and cases of pragmatic encroachment and argue that confronting these cases generates a dilemma about rationality. This dilemma turns on the idea that subjects are motivated to avoid costly false beliefs, and that both cases of self-deception and cases of pragmatic encroachment are caused by an interest to avoid forming costly false beliefs. Even though both types of cases can be explained by the same belief-formation mechanism, only self-deceptive beliefs are irrational: the subjects depicted in high-stakes cases typically used in debates on pragmatic encroachment are, on the contrary, rational. If we find ourselves drawn to this dilemma, we are forced either to accept—against most views presented in the literature—that self-deception is rational or to accept that pragmatic encroachment is irrational. Assuming that both conclusions are undesirable, I argue that this dilemma can be solved. In order to solve this dilemma, I suggest and review several hypotheses aimed at explaining the difference in rationality between the two types of cases, the result of which being that the irrationality of self-deceptive beliefs does not entirely depend on their being formed via a motivationally biased process.

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)0.001
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.404
Threshold uncertainty score0.569

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.034
GPT teacher head0.270
Teacher spread0.236 · 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 designTheoretical or conceptual
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

Citations3
Published2018
Admission routes1
Has abstractyes

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