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Record W2890357351 · doi:10.15381/pc.v23i1.15102

Modelo Computacional Aplicado al Comportamiento de Agentes Financieros Mediante Autómatas Celulares (Cell-DEVS)

2018· article· es· W2890357351 on OpenAlexaff
Jesús Barrantes Limahuaya, Neisser Pino Romero, Gabriel Wainer

Bibliographic record

VenuePensamiento Crítico · 2018
Typearticle
Languagees
FieldEconomics, Econometrics and Finance
TopicBusiness, Innovation, and Economy
Canadian institutionsCarleton University
Fundersnot available
KeywordsPhysics

Abstract

fetched live from OpenAlex

En el presente trabajo se realizará la simulación el efecto de la interdependencia y el contagio en las decisiones de compra y venta de los inversionistasen un mercado financiero de tipo Black-Schole,donde existen dos activos financieros: acciones (activo riesgoso) y bonos (activo libre de riesgo); y dos estados del inversionista: amante y averso al riesgo; bajo distintas probabilidades de contagio. Este fenómeno económico-financiero podría ser modelado por un sistema de ecuaciones estocásticas; sin embargo, no todas las ecuaciones estocásticas tienen una solución cerrada por lo que se opta por realizar simulaciones computacionales para analizar su comportamiento numérico, en este sentido, se realizará una simulación computacional mediante los autómatas celulares.

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)0.001
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.012
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.246
Teacher spread0.212 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

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