A Typology for Characterizing Human Action in MultiSector Dynamics Models

The role of individual and collective human action is increasingly recognized as a prominent and arguably paramount determinant in shaping the behavior, trajectory, and vulnerability of multisector systems. This human influence operates at multiple scales: from short-term (hourly to daily) to long-term (annually to centennial) timescales, and from the local to the global, pushing systems toward either desirable or undesirable outcomes. However, the effort to represent human systems in multisector models has been fragmented across philosophical, methodological, and disciplinary lines. To cohere insights across diverse modeling approaches, we present a new typology for classifying how human actors are represented in the broad suite of coupled human-natural system models that are applied in MultiSector Dynamics (MSD) research. The typology conceptualizes a “sector” as a system-of-systems that includes a diverse group of human actors, defined across individual to collective social levels, involved in governing, provisioning, and utilizing products, goods, or services toward some human end. We trace the salient features of modeled representations of human systems by organizing the typology around two key questions: (a) Who are the actors in MSD systems and what are their actions? (b) How and for what purpose are these actors and actions operationalized in a computational model? We use this typology to critically examine existing models and chart the frontier of human systems modeling for MSD research

An Agent-Based Exploration of the Hurricane Forecast-Evacuation System Dynamics

In the mainland US, the hurricane-forecast-evacuation system is uncertain, dynamic, and complex. As a result, it is difficult to know whether to issue warnings, implement evacuation management strategies, or how to make forecasts more useful for evacuations. This dissertation helps address these needs, by holistically exploring the system’s complex dynamics from a new perspective. Specifically, by developing – and using – an empirically informed, agent-based modeling framework called FLEE (Forecasting Laboratory for Exploring the Evacuation-system). The framework represents the key, interwoven elements to hurricane evacuations: the natural hazard (hurricane), the human system (information flow, evacuation decisions), the built environment (road infrastructure), and connections between systems (forecasts and warning information, traffic). The dissertation’s first article describes FLEE’s conceptualization, implementation, and validation, and presents proof-of-concept experiments illustrating its behaviors when key parameters are modified. In the second article, sensitivity analyses are conducted on FLEE to assess how evacuations change with evacuation management strategies and policies (public transportation, contraflow, evacuation order timing), evolving population characteristics (population growth, urbanization), and real and synthetic forecast scenarios impacting the Florida peninsula (Irma, Dorian, rapid-onset version of Irma). The third article begins to explore how forecast elements (e.g., track and intensity) contribute to evacuation success, and whether improved forecast accuracy over time translates to improved evacuations outcomes. In doing so, we demonstrate how coupled natural-human models – including agent-based models –can be a societally-relevant alternative to traditional metrics of forecast accuracy. Lastly, the fourth article contains a brief literature review of inequities in transportation access and their implication on evacuation modeling. Together, the articles demonstrate how modeling frameworks like FLEE are powerful tools capable of studying the hurricane-forecast-evacuation system across many real and hypothetical forecast-population-infrastructure scenarios. The research compliments, and builds-upon empirical work, and supports researchers, practitioners, and policy-makers in hazard risk management, meteorology, and related disciplines, thereby offering the promise of direct applications to mitigate hurricane losses

Exploring Public Perceptions Of The Recovery Response As A Result Of Hurricane Michael’s Landfall

Hurricanes remain among the most frequent and costliest natural disasters to impact the United States both in terms of loss of property and life (Rudden, 2022; NOAA, 2021a; NOAA, 2022a, 2022b, 2022c). Hurricanes Harvey, Irma, and Maria brought renewed attention to the subject of disaster recovery as they collectively cost the nation over $373 billion dollars in damage and over 3,200 lives lost in the 2017 hurricane season (NOAA, 2022a, 2022b; Reguero et al., 2018; USNHC, 2018). Property and lives are at most risk during the first 72 hours following a major hurricane (Col, 2007; Kohn et al., 2012; Dourandish, Zumel, & Manno, 2007; Harris et al., 2018). While previous research focuses on communities’ long-term recovery, limited data has been collected involving the roles of government in immediate recovery efforts. Major hurricanes become a focal point in the lives of those affected, and through these events they shape public expectations, assessments, and attitudes toward government leadership (Darr, Cate, and Moak, 2019). The qualitative study solicited the perceptions and opinions of the survivors of Hurricane Michael in Bay County, Florida to expose previously unknown phenomena related to the storm’s effects on the community and its work towards recovery. Recommendations to shorten immediate recovery time include continuous pre-storm collaborative planning, pre-storm public education campaign, improvements in communication, increase in personnel, and linear research into immediate recovery

Understanding the Socio-infrastructure Systems During Disaster from Social Media Data

Our socio-infrastructure systems are becoming more and more vulnerable due to the increased severity and frequency of extreme events every year. Effective disaster management can minimize the damaging impacts of a disaster to a large extent. The ubiquitous use of social media platforms in GPS enabled smartphones offers a unique opportunity to observe, model, and predict human behavior during a disaster. This dissertation explores the opportunity of using social media data and different modeling techniques towards understanding and managing disaster more dynamically. In this dissertation, we focus on four objectives. First, we develop a method to infer individual evacuation behaviors (e.g., evacuation decision, timing, destination) from social media data. We develop an input output hidden Markov model to infer evacuation decisions from user tweets. Our findings show that using geo-tagged posts and text data, a hidden Markov model can be developed to capture the dynamics of hurricane evacuation decision. Second, we develop evacuation demand prediction model using social media and traffic data. We find that trained from social media and traffic data, a deep learning model can predict well evacuation traffic demand up to 24 hours ahead. Third, we present a multi-label classification approach to identify the co-occurrence of multiple types of infrastructure disruptions considering the sentiment towards a disruption—whether a post is reporting an actual disruption (negative), or a disruption in general (neutral), or not affected by a disruption (positive). We validate our approach for data collected during multiple hurricanes. Fourth, finally we develop an agent-based model to understand the influence of multiple information sources on risk perception dynamics and evacuation decisions. In this study, we explore the effects of socio-demographic factors and information sources such as social connectivity, neighborhood observation, and weather information and its credibility in forming risk perception dynamics and evacuation decisions

Understanding Social Equity in Infrastructure Systems Resilience

Natural hazards, such as flooding, hurricanes, wildfires, pose a threat to the well-being of communities by disrupting the infrastructure services people use in their day-to-day lives. The impacts of infrastructure service disruptions are not equal for all the affected households. In particular, socially vulnerable populations tend to suffer more from disaster-induced disruptions in infrastructure systems. Infrastructure resilience has been given growing attention for improving the performance of these systems in the face of disasters. However, current approaches fail to capture the disproportionate impacts and integrate social considerations in resilience assessments. Despite the advancements in the resilience and vulnerability assessment of infrastructure systems, there is a knowledge gap in understanding the societal impacts of infrastructure service disruptions and developing equitable approaches for resilience assessment of these systems. These limitations in considering the social equity considerations in resilience assessment of infrastructure systems are mainly due to 1) lack of empirical findings and theoretical frameworks for examining the societal impacts and 2) limited integrated modeling approaches to capture the emergent societal risks in the interconnected complex network of human-hazard-infrastructure. To overcome these challenges, I developed theoretical frameworks, provided empirical models, and created integrated computational models to understand the societal impacts of infrastructure service disruptions and enable equitable resilience assessment of infrastructure systems. In the first study, I proposed and tested a theoretical framework to examine the disproportionate impact of infrastructure service disruptions on the communities and conducted an exploratory analysis by analyzing household survey data from affected households to identify the determinants of risk disparity due to infrastructure service disruptions. The second study develop empirical survival models to provide an understanding of social susceptibility to infrastructure service disruptions and creates the survival curves for determining the extent of hardship communities face if the duration of service disruptions exceeds different levels. Lastly, the third study implements the empirical findings from the first two studies in addition to theoretical decision-making models to develop a multi-agent simulation model to capture the complex mechanisms leading to the societal impacts and develop equitable approaches for infrastructure resilience assessment. The created theories, findings, methodologies, and models in this research have significant contributions to the resilience and sustainability of infrastructure systems by enabling integration of societal needs, expectations, and social equity in infrastructure planning and prioritizations