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Record W7096613274

on “Emergent and Co-evolutionary Processes in Marketing ” edited by James Wiley. EXECUTIVE SUMMARY

2015· article· en· W7096613274 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicComplex Systems and Time Series Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsExecutive summaryCompetition (biology)Extension (predicate logic)Order (exchange)Aggregate (composite)Work (physics)Aggregate data
DOInot available

Abstract

fetched live from OpenAlex

I describe an extension to a recently reported interacting-agent model of retail competition and innovation, which displays a subtle type of order known as self-organized criticality, or SOC. SOC is a stochastic steady state that is “poised ” so that a small exogenous perturbation may result in anything from no response, to a system-wide avalanche-like response. Such poised systems have been shown to be superior on some measure (such as “fitness ” in ecology) for the system as a whole. The extension is designed to validate the model by generating falsifiable predictions about retail industry dynamics. Specifically, the extension allows firms to exit, while preserving the self-organized critical state of the entire system. The model predicts that exit data should show the power-law behavior that is the footprint of a critical system. Drawing on work that relates the characteristics of individual events in a SOC system to an aggregate time series, I predict the functional form of the power spectrum of exits for firms in a spatially competing industry. I then show that Canadian retail bankruptcies follow this form, implying that the Canadian retail industry can indeed be described as a critical system—neither stable and in equilibrium, nor completely unpredictable. One of the requirements to generate SOC behaviour in the model is spatially localized competition, a feature which is much more predominant in retailing than in other industries. An important and

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.510
Threshold uncertainty score0.545

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.025
GPT teacher head0.221
Teacher spread0.196 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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
Published2015
Admission routes1
Has abstractyes

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