on “Emergent and Co-evolutionary Processes in Marketing ” edited by James Wiley. EXECUTIVE SUMMARY
Bibliographic record
Abstract
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
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.036 | 0.008 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".