A game-theoretic model for roadway performance management
Bibliographic record
Abstract
Purpose – The purpose of this paper is to present a socio-technical approach to modeling the behavior of roadway users, asset managers, and politicians toward roadway performance and asset management. This approach models the complex interactions that occur between these agents in a complex system. Most modeling approaches in the domain of infrastructure asset management take a purely asset-centric approach and fail to address these socio-technical interactions. Design/methodology/approach – Interactions among political decision makers, asset management strategy developers, and road users are modeled using a game-theoretic approach. The interactions are modeled as a non-cooperative game in which politicians, asset managers, and road users are the main players. Each player is autonomous and aims to come up with the set of moves to maximize their respective level of satisfaction in response to other players’ moves. Multi-attribute utility theory is used to deal with multitude of players’ goals, and the Nash equilibria of the game are south out to develop appropriate strategies for different players. Findings – An illustrative example for a road network of a Canadian city is used to demonstrate the developed methodology. The developed methodology demonstrates how behaviors of various agents involved in the sphere of asset management impacts their collective decision-making behavior. Originality/value – The developed framework provides asset managers and political decision makers with a valuable tool to evaluate the impact of public policy decisions related to asset managers on road performance and the overall satisfaction of road users.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".