Flood-ABM: An agent-based model of differential flood effects on population groups and their decision-making processes
Bibliographic record
Abstract
Flooding is a global concern with wide-ranging impacts on communities, infrastructure, and ecosystems. Its effects are often unevenly distributed, influenced by complex interactions between environmental and social systems. In this paper, we present an agent-based model (ABM) that links hydrological and social systems through a differentiating lens. Our ABM captures individual and collective behaviors across pre-flood, during-flood, and post-flood phases, while differentiating population groups through a socio-economic index to examine how disparities in resources and vulnerability influence decisions related to preparedness, evacuation, coping, and adaptation. Our model innovatively integrates four decision-making theories—Protection Motivation Theory, the Theory of Planned Behavior, Cultural Risk Theory, and Social Capital Theory—in a complex framework that reflects diverse individuals strategies during floods. We find that agents facing intense flood threats respond rapidly with emergency measures—such as swift evacuations and immediate protective actions—even when constrained by resource limitations. In contrast, agents in less critical scenarios exhibit more measured responses, engaging in thorough pre-event planning and gradual evacuations that facilitate smoother adaptations post-flood. Economic impacts quantified by the model demonstrate that widespread business closures significantly reduce earnings, while increased spending on healthcare and evacuation drives up overall costs. During peak flooding, shelter agents suffer wealth declines due to the high costs of providing emergency services, and healthcare agent resources are strained by surging demand as health deteriorates among affected populations. Our results indicate that increased shelter capacity, expedited rescue responses, and enhanced healthcare provision collectively promote post-flood economic stabilization by reducing stranded individuals and mitigating peak-phase financial losses. • Differentiated groups by socio-economic index to model flood responses. • Combined four decision-making theories to model complex behaviors. • Simulated individual/group decisions across flood phases (pre/during/post). • Measured social, economic, and infrastructure impacts using spatial data.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".