From ethical dilemmas to ethical cascading failure in disaster evacuation planning
Bibliographic record
Abstract
Traditional disaster evacuation planning has long emphasised efficiency, typically by minimising evacuation time or cost. With increasing attention to equity and social sustainability, new conflicts have emerged, such as whether to prioritise vulnerable groups, protect minority interests, or respect individual autonomy while ensuring collective order. These conflicts represent critical ethical dilemmas. This article introduces the novel concept of Ethical Cascading Failure (ECF), which describes how the violation of a single ethical principle can propagate across multiple principles and stakeholders, eventually leading to systemic breakdowns of evacuation governance. To illustrate this, qualitative analyses of recent disaster cases including the 2025 Southern California wildfires and the 2022 New South Wales floods, demonstrate how budget cuts, delayed warnings and biased communication escalated into wider ethical failures. For the quantitative analysis, three methods are proposed: complex network simulation to capture cascading propagation, multi-objective optimisation to balance efficiency and fairness, and explainable deep reinforcement learning with agent-based models to reveal adaptive and interpretable strategies. Together, these approaches show how ECF can be systematically analysed and mitigated. The framework offers new theoretical foundations and practical guidance for designing evacuation models, evaluating decision-making, and strengthening policy formulation.
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How this classification was reachedexpand
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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.004 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".