Research Conducted At University Of Miami Has Updated Our Knowledge About Engineering (insuragent: A Large Language Model-empowered Agent For Simulating Individual Behavior In Purchasing Flood Insurance): Engineering

2026 SEP 08 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- Investigators publish new report on Engineering. According to news reporting originating from Coral Gables, Florida, by NewsRx correspondents, research stated, “Flood insurance is an effective strategy for individuals to mitigate disaster-related losses. However, participation rates among at-risk populations in the United States remain strikingly low.”

Funders for this research include National Science Foundation (NSF), NSF-Simons SkAI Institute, NSF CAREER, National Science Foundation (NSF), National Institutes of Health (NIH) - USA, Google Research Scholar Award, Cisco gift grant.

Our news editors obtained a quote from the research from the University of Miami, “This gap underscores the need to understand and model the behavioral mechanisms underlying insurance decisions. Large language models (LLMs) have recently exhibited human-like intelligence across wide-ranging tasks, offering promising tools for simulating human decision-making. This study constructs a benchmark data set to capture insurance purchase probabilities across factors. Using this data set, the capacity of LLMs is evaluated: While LLMs exhibit a qualitative understanding of factors, they fall short in estimating quantitative probabilities. To address this limitation, InsurAgent, an LLM-empowered agent comprising five modules, including perception, retrieval, reasoning, action, and memory, is proposed. The retrieval module leverages retrieval-augmented generation to ground decisions in empirical survey data, achieving accurate estimation of marginal and bivariate probabilities. The reasoning module leverages LLM common sense to extrapolate beyond survey data, capturing contextual information that is intractable for traditional models. The memory module supports the simulation of temporal decision evolutions, illustrated through a roller coaster life trajectory.”

According to the news editors, the research concluded: “Overall, InsurAgent provides a valuable tool for behavioral modeling and policy analysis.”

This research has been peer-reviewed.

For more information on this research see: Insuragent: a Large Language Model-empowered Agent for Simulating Individual Behavior In Purchasing Flood Insurance. Asce-asme Journal of Risk and Uncertainty In Engineering Systems Part A-civil Engineering, 2026;12(3). Asce-asme Journal of Risk and Uncertainty In Engineering Systems Part A-civil Engineering can be contacted at: Asce-amer Soc Civil Engineers, 1801 Alexander Bell Dr, Reston, VA 20191-4400, USA.

The news editors report that additional information may be obtained by contacting Minghui Cheng, University of Miami, Dept. of Civil and Architectural Engineering, Coral Gables, FL 33146, United States. Additional authors for this research include Ziheng Geng, Jiachen Liu, Ran Cao, Lu Cheng and Dan M. Frangopol.

The direct object identifier (DOI) for that additional information is: https://doi.org/10.1061/AJRUA6.RUENG-1899. This DOI is a link to an online electronic document that is either free or for purchase, and can be your direct source for a journal article and its citation.

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