Research & Academic Work
My research asks a question generative AI has made urgent: when can a simulated human decision-maker be trusted? I approach it by owning every link of the evidence chain. I designed and fielded a 937-household flood-adaptation survey in New Jersey’s Passaic River Basin, used structural equation modeling to identify how owners and renters actually decide among adaptation actions, and encoded those empirically identified pathways in a Bayesian-calibrated agent-based model of 52,141 households coupled to a catastrophe flood model with National Flood Insurance Program mechanics — including income-normalized equity analysis of who bears flood losses. With my advisor, I published a Water Resources Research article (62(6), e2025WR042111, 2026) substituting large language models as the behavioral engine of such simulations. That substitution is the contribution: large language models become the behavioral engine of the simulation. Because nothing guarantees a generative agent decides the way real households do, my current work pairs it with AI governance — checking LLM personas against the decision pathways identified in my own survey data, and developing governance methods that constrain agent behavior with physical constraints and behavioral theory — research software now in preparation for release. First-author manuscripts on the coupled model and on adaptation decision pathways are under review. The longer-term agenda is a governance standard for generative agents in consequential simulation.
The research program, as one arc
Survey
Designed and fielded a 937-household flood-adaptation survey (557 owners / 379 renters) in New Jersey’s Passaic River Basin.
Evidence Calibration files in the FLOODABM repository; the empirical grounding of the Water Resources Research paper.
Instrument feeding one published article and manuscripts now under review.
Decision pathways (SEM)
Identified flood-adaptation decision pathways through structural equation modeling — across owner and renter groups, and across marginalized and non-marginalized groups (the equity strand).
Evidence First-author manuscript targeting Sustainable Cities and Society.
Under review.
Coupled ABM–CAT model
Built a Bayesian-calibrated agent-based model of 52,141 households across 27 census tracts, coupled to a catastrophe flood model with National Flood Insurance Program premium, payout, and deductible mechanics and income-normalized equity analysis of who bears flood losses.
Evidence FLOODABM repository — Zenodo-archived with CITATION.cff, published seed lists, and a candid known-limitations register; first-author manuscript targeting the Journal of Hydrology.
Code: Research prototype, archived. Paper: Under review.
LLMs as the behavioral engine
Applied large language models as the behavioral engine of an agent-based model of a human–water system — the core contribution. Because nothing guarantees a generative agent decides the way real households do, the work also checks LLM personas against empirically identified human decision pathways.
Evidence Yang, Y. C. E., & Chiou, W. (2026). Leveraging Large Language Models for Agent-Based Simulation of Human–Water System Interactions. Water Resources Research, 62(6), e2025WR042111.
Published.
Governance methods
Validation architecture for generative agents — hard physical and financial constraints plus behavioral-theory coherence, fail-closed, with audit trails — generalized beyond the flood domain.
Evidence Research software in preparation for release; public echoes in the fail-closed design of the open-source tooling.
In preparation.
