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Record W4387884307 · doi:10.4236/tel.2023.135071

Economic Natural Selection: Interpretation of Natural Selection as Economic Selection in the Evolutionary Process

2023· article· en· W4387884307 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueTheoretical Economics Letters · 2023
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Theory and Institutions
Canadian institutionsAlgoma University
Fundersnot available
KeywordsNatural selectionSelection (genetic algorithm)Process (computing)Natural resourceNatural (archaeology)Group selectionInterpretation (philosophy)EconomicsComputer scienceMicroeconomicsEcologyArtificial intelligenceBiology

Abstract

fetched live from OpenAlex

The paper proposes that natural selection hypothesis in the theory of evolution can properly be interpreted as economic selection in that the species that have adapted successfully to their changing environment through the optimizing economic behaviors resulting in efficient allocation of scarce resources have been selected by nature in the evolutionary process. It is the species’ optimizing rational economic behaviors rather than their superior intellectual or physical strength that are responsible for their being naturally selected. In that sense, natural selection may be interpreted as economic natural selection. The paper presents the evidence that the species that have survived the natural selection process in general including the human species behave rationally through constrained dynamic optimizing behaviors which result in efficient allocation of scarce resources through decentralized decision making without a central coordination or control with the result that such species are selected by nature over those that do not.

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.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.483
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.002

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 head0.007
GPT teacher head0.223
Teacher spread0.216 · 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