Behavioral Economic Concepts for Funding Infrastructure Rehabilitation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Behavioral economics is a newly emerging field that examines the impact of psychological factors such as attitudes, biases, and behaviors on decision makers’ choices. Because many decisions in engineering and management involve subjective experience-based assessments of situations (e.g., bidding and fund-allocation), the psychological factors playing an important role in these decisions need to be considered. This paper thus introduces common behavioral economic concepts and examines the applicability of the most influential behavior loss aversion in the asset management domain, particularly infrastructure rehabilitation projects. Loss aversion refers to people’s tendency to strongly prefer avoiding loss more than acquiring gain. Using a pavement case study, a detailed life cycle cost analysis model has been developed and extensive optimization experiments were carried out to compare the traditional approach of maximizing gain from a limited rehabilitation budget against loss-aversion approaches. The results show that incorporating behavioral aspects into asset management decisions can better justify the decisions made and account for the varying preferences of stakeholders and thus can lead to higher public satisfaction and more justifiable spending of tax money.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it