Is competition sufficient to drive observed retail location and revenue patterns? An agent-based case study.
Bibliographic record
Abstract
Agent-based models (ABMs) have been widely used to represent and investigate complex systems and are a contemporary modelling approach used in the study of land-use and land-cover change. While many ABMs have been constructed to address research questions associated with residential land development and human choices, agricultural land transition and farmer decision-making, and transportation networks and planning, less attention has been given to improving our understanding about the drivers and agent behaviours associated with commercial and retail competition, which subsequently affects land-use change. Among existing ABMs that represent the retail system, the focus has been on understanding consumer behaviours, but the inclusion of the store competition is lacking, and most retail competition models still use a top-down modelling framework. The thesis herein provides a new contribution to retail competition literature through the development and use of a retail-competition agent-based model (RC-ABM). Utilizing previous empirical research on consumer expenditures and retail location site selection, competition for home-improvement expenditures is simulated within the home-improvement retail system in the Region of Waterloo, Ontario, Canada. Results exhibit a high level of alignment between the RC-ABM and a traditional Location-Allocation Model (LAM) in estimating a market capture and store revenue acquisition. In addition, while modelled competition itself cannot reproduce the observed spatial pattern of home-improvement stores in our study area, results from the model can be used to identify path dependencies associated with retail success generated by competition and factors affecting retail store survival. Lastly, the presented RC-ABM provides the potential to enrich future land-use and land-cover change models by better representing commercial development.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
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".