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Record W1979132471 · doi:10.4018/ijabe.2014040103

B is for Bias

2014· article· en· W1979132471 on OpenAlexaff
Călin Vâlsan

Bibliographic record

VenueInternational Journal of Applied Behavioral Economics · 2014
Typearticle
Languageen
FieldDecision Sciences
TopicDecision-Making and Behavioral Economics
Canadian institutionsBishop's University
Fundersnot available
KeywordsHeuristicsRationalityOrder (exchange)Bounded rationalityRational agentBehavioral economicsEconomicsEcological rationalityRational expectationsPositive economicsPsychologyComputer scienceMicroeconomicsEpistemologyNeoclassical economicsEconometricsFinance

Abstract

fetched live from OpenAlex

Standard economic theory assumes rational agents. Individuals are expected to have rational expectations and constantly optimize their choices. Modern economic and financial theory is build under the assumption of rationality. There is plenty of evidence from psychology, however, that individuals are biased and rely heavily on heuristics in order to make decisions. Yet, this is not a mere fluke, a behavioral oddity. Because the social and economic environment in which individuals evolve is complex, behavioral biases represent evolutionary adaptations allowing economic agents to deal with undecidability and computational irreducibility.

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)0.003
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.978
Threshold uncertainty score0.668

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0020.000
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 head0.255
GPT teacher head0.443
Teacher spread0.188 · 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 designOther design
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

Citations0
Published2014
Admission routes1
Has abstractyes

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