A serious gaming approach to understanding household flood risk mitigation decisions
Bibliographic record
Abstract
Abstract Voluntary household decisions about whether or not to structurally mitigate or insure can directly and indirectly influence the flood vulnerability of a community. We look to understand the factors that influence flood risk mitigation decisions using a serious game experiment. Serious games can augment existing data collection methods used to understand flood risk mitigation by tracking decisions over multiple turns within an experimental research framework. In this game, participants choose where to live and how to distribute income given information about flood risks. We analysed data using a generalised linear mixed model that accounted for repeated‐measures effects. Experiencing an in‐game flood had a strong positive association with mitigation decisions, compared to a much weaker effect of a participant having experienced a flood in real‐life. We find that real‐life low‐income individuals were no less likely to implement in‐game mitigation measures than their higher‐income counterparts, suggesting that low income and/or cost is a practical barrier to risk mitigation. Our findings also suggest that incentivising flood risk mitigation should be done quickly following a flood.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".