Microsimulation framework for urban price-taker markets
Bibliographic record
Abstract
In the context of integrated transportation and other urban engineering infrastructure systems, there are many examples of markets, where consumers exhibit price-taking behavior. While this behavior is ubiquitous, the underlying mechanism can be captured in a single framework. Here, we present a microsimulation framework of a price-taker market that recognizes this generality and develop efficient algorithms for the associated market-clearing problem. By abstracting the problem as a specific graph theoretic problem (i.e., maximum weighted bipartite graph), we are first able to exploit algorithms that are developed in graph theory. We then explore their appropriateness in terms of large-scale integrated urban microsimulations. Based on this, we further develop a generic and efficient clearing algorithm that takes advantage of the features specific to urban price-taker markets. This clearing solution is then used to operationalize two price-taker markets, from two different contexts, within a microsimulation of urban systems. The initial validation of results against the observed data generally shows a close match.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".