Case study 02 · Coupled simulation
Research prototype · archived
FLOODABM
A 52,141-household agent-based model coupled to catastrophe, insurance, and financial mechanics across 27 New Jersey census tracts.
- 52,141 households
- 27 census tracts
- 2011–2023
- 50 stochastic runs per scenario
Problem
Why this problem matters
Flood adaptation decisions change losses unevenly across households. A useful model must connect behavioral pathways to hazard, property, insurance, and out-of-pocket outcomes over time.
System artifact
Inspect the research logic
The AGU 2025 poster and archived repository document the coupled architecture and aggregate outcomes.
Illustrative system architecture · not a reported study result
- 01Household state
- 02Adaptation choice
- 03Flood event
- 04Damage, payout, and out-of-pocket update
- 05Next annual step
What changes in this lensOwner state includes property adaptation, insurance, damage, payout, and out-of-pocket feedback.
Role & method
What I built
I designed and fielded the 937-household survey, translated pathways into agent parameters, built the household simulation, and coupled it to catastrophe and NFIP mechanics.
- Survey-ground behavioral parameters through SEM and Bayesian calibration.
- Advance household adaptation and insurance decisions over annual time steps.
- Couple agents to flood hazard, property damage, NFIP premium/payout/deductible, and financial state.
- Run 50 stochastic realizations for each scenario and report aggregate distributions.
Validation and limitations
Validation
- Survey-grounded behavioral parameters
- Scenario ensembles
- Multi-level diagnostics
- Archived code and citation metadata
Limitations
- Calibration and survey evidence are place- and period-specific.
- Household agents simplify decision processes and social context.
- Hazard and financial outputs inherit uncertainty from coupled models.
- Scenario results should not be treated as forecasts for an individual household.
What changed
Coupling behavior to physical and financial feedback makes assumptions auditable and exposes where a seemingly reasonable action creates uneven consequences.
Contact
Build evaluations that connect model behavior to real evidence
I am seeking a full-time Summer 2027 internship from approximately late May through mid-August. I am especially interested in LLM evaluation, agent systems, behavioral simulation, and AI for science.