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Record W2155176222 · doi:10.3389/fnins.2012.00129

Optimal Short-Sighted Rules

2012· article· en· W2155176222 on OpenAlexaff
Sacha Bourgeois‐Gironde

Bibliographic record

VenueFrontiers in Neuroscience · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicExperimental Behavioral Economics Studies
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsForagingRelevance (law)Profitability indexOptimal foraging theoryComputer scienceControl (management)Intertemporal choiceCognitive psychologyEcologyEconomicsMicroeconomicsArtificial intelligencePsychologyBiology

Abstract

fetched live from OpenAlex

The aim of this paper is to assess the relevance of methodological transfers from behavioral ecology to experimental economics with respect to the elicitation of intertemporal preferences. More precisely our discussion will stem from the analysis of Stephens and Anderson's (2001) seminal article. In their study with blue jays they document that foraging behavior typically implements short-sighted choice rules which are beneficial in the long run. Such long-term profitability of short-sighted behavior cannot be evidenced when using a self-control paradigm (one which contrasts in a binary way sooner smaller and later larger payoffs) but becomes apparent when ecological patch-paradigms (replicating economic situations in which the main trade-off consists in staying on a food patch or leaving for another patch) are implemented. We transfer this methodology in view of contrasting foraging strategies and self-control in human intertemporal choices.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0020.003
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.046
GPT teacher head0.330
Teacher spread0.284 · 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 source (direct Gemma or distilled Codex), 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

Citations6
Published2012
Admission routes1
Has abstractyes

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