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Record W3180096179 · doi:10.31234/osf.io/qtcrp

A cognitive model of economics

2021· preprint· en· W3180096179 on OpenAlexaff
Lorin Friesen

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicComplex Systems and Time Series Analysis
Canadian institutionsAbbotsford Veterinary Clinic
Fundersnot available
KeywordsPerspective (graphical)NeuroeconomicsCognitionRealmBehavioral economicsPositive economicsEconomicsPsychologyCognitive scienceCognitive psychologyComputer scienceMicroeconomicsPolitical scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Economics is typically regarded as a collection of partially related theories. However, if economics is analyzed from a cognitive perspective, then it becomes evident that these theories form a cognitively natural, coherent package. The theory of mental symmetry is a cognitive model based in seven interacting cognitive modules. This theory is used to analyze most of the fundamental concepts of micro- and macroeconomics, as well as behavioral economics and neuroeconomics. Economics can be explained primarily as a focus upon the interaction between the cognitive module that provides motivation and the cognitive module that emphasizes choice. Economics recognizes the existence of other cognitive modules but regards them as outside of the realm of economics. A fuller cognitive picture can be gained by including the activity of these other cognitive modules. Going further, a cognitive basis for the institutions of macroeconomics can be found by viewing these institutions from the perspective of abstract technical thought.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0010.006
Scholarly communication0.0050.006
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0080.002

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 head0.086
GPT teacher head0.227
Teacher spread0.142 · 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 designSimulation or modeling
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

Citations2
Published2021
Admission routes1
Has abstractyes

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