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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 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 categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.419
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.005

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; both teacher heads agree on what is shown here.

Study designObservational
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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