We, as guest editors, are honored to have been given the opportunity to organize this mini special issue on “Modeling of Human-Policy-Technology Nexus and Simulation for Disaster Response and Business Continuity” for the Journal of Disaster Research. This mini special issue aims to contribute to the overcoming of challenges around better design, operation, and management for disaster response and business continuity, where various factors, including human behavior and decision-making, policy and prioritization, monetary and humanitarian costs, and technological and infrastructural constraints, are intertwined, making it difficult to comprehend the entire picture and find an optimal solution.

On the other hand, we firmly believe people have an essential role in disaster response and business continuity. However, unlike physical phenomena, there are no governing equations or laws for human behavior and decision-making; there are variations and inconsistencies in human modeling depending on whether what was done, what ought to be done, what can be done, etc. is being modeled. Sometimes, human behavior must be considered and the different modes of human models clearly distinguished when better socio-technical systems or other systems involving humans are discussed.

All the papers selected introduce the latest innovative research in modeling and simulation, training system design, and quantitative and qualitative data analysis, which are directly and indirectly applicable to a better design, operation, and the management of disaster response and business continuity. At the same time, these papers also focus on one or several aspects of human behavior modes, such as past and current evidence (descriptive model) and what persons or organizations are supposed to be (normative model). The collection of these papers is expected to give good insight and inspiration to the readers and those who may take on the challenges this special issue aims to provide and to help develop an understanding of the importance of a human-centric approach to disaster research.