Risk Aversion, the Value of Information, and Traffic Equilibrium
Bibliographic record
Abstract
Information about traffic conditions has traditionally been conveyed to drivers via radio, variable message signs, and, more recently, the Internet and advanced traveler information systems. This has spurred research on how travelers respond to information, how much they are willing to pay for it, and how much they are likely to benefit from it collectively. In this paper, we analyze the decisions of drivers on whether to acquire information and which routes to take on simple congested road networks. Drivers vary in their degrees of risk aversion with respect to travel time. Four information regimes are considered: no information, free information (publicly available at no cost), costly information (publicly available for a fee), and private information (available free to single individuals). Private information is shown to be individually more valuable than either free or costly information while the benefits from free and costly information cannot be ranked in general. Free or costly information can decrease the expected utility of drivers who are very risk averse; with sufficient risk aversion in the population, the aggregate compensating variation for information can be negative.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".