A Review of Urban Residential Choice Models Using Agent-Based Modeling
Bibliographic record
Abstract
Urban land-use modeling methods have experienced substantial improvements in the last several decades. With the advancement of urban land-use change theories and modeling techniques, a considerable number of models have been developed. The relatively young approach, agent-based modeling, provides urban land-use models with some new features and can help address the challenges faced by traditional models. Applications of agent-based models to study urban dynamics have increased steadily over the last twenty years. To offer a retrospective on the developments in agent-based models (ABMs) of urban residential choices, we review fifty-one relevant models that fall into three general categories: (i) urban land-use models based on classical theories; (ii) different stages of the urbanization process; and (iii) integrated agent-based and microsimulation models. We summarize and compare the main features of these fifty-one models within each category. This review focuses on three fundamental new features introduced byABMs. The first is agent heterogeneity with particular attention to the method of introducing heterogeneity in agents' attributes and behaviors. The second is the representation of land-market processes, namely preferences, resources constraints, competitive bidding, and endogenous relocation. The third is the method of measuring the extensive model outputs. In addition, we outline accompanying challenges to, and open questions for, incorporating these new features. We conclude that, by modeling agent heterogeneity and land markets, and by exploiting a much broader dimension of output, we will enhance our understanding of urban land-use change and are hopefully able to improve model fitness and robustness.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".