# Computational Social Science of Disasters: Opportunities and Challenges
## 5 Computational Social Science of Disasters
### Interactions Among the Components of CSSD
Classic scientific methodologies use theories and observations to develop hypotheses for testing in further observations and experiments. [PERSON] et al. [222] developed an online experiment to test the relationship between team size and productivity (e.g., [223]) in a realistic crisis mapping task. Not only did their work use collected data to update the existing theory on complex tasks, but it also used the existing theory to inform their data collection. Turning to how computational models can be informed by theory, models are often built to test specific theories, and although they are not yet applied in the area of disasters, theory-based models are prevalent in the field of CSS and conflict crises. In an agent-based model of conflict in Sierra Leone, [PERSON] and [PERSON] [224] tested [PERSON]'s [225] theory that the spatial dispersion of a resource (in this case, diamonds) leads to warlorlism, secession, and mass rebellion. The theory dictated the selection of data collection from a variety of spatial data sources, including OpenStreetMap [226]. Their model subsequently provided confirmation of the theory using basic bottom-up processes operationalized in the model by enabling agents to choose whether to mine, rebel, or do nothing when varying the spatial dispersion of diamond mines and areas under government control. Traditional social science models often simplify the complex interactions of socio-economic dynamics with linear representations, e.g., increasing education and employment will improve the quality of life in a community. Although there is ample confirmation of these theories, real-world data paint a more complex picture of confounding relationships, such as global economic trade or group unrest. Exploration of these complex interactions and nonlinear relationships has been done in another agent-based model [227] that uses identity and social influence theory [228] to inform data collection and exploration of the emergence of riots. Otherapplications related to disasters and conflict apply the theory of planned behavior [229] to agent-based models of social-ecological systems, for example, [PERSON] et al. [230] and [PERSON] and [PERSON] [231], and these are currently being fit into a framework for mapping and comparing behavioral theories [232]. Traditionally, models were built to test a specific theory. However, with advancements in computation, models are now also used to develop theory, changing the relationship between theory and modeling [126; 135]. This is specifically evident in the utilization of agent-based models. Agent-based models have been shown to be suitable for capturing the heterogeneity and the complex interactions between agents and their environments and have made progress in the development and study of theories of complexity in disaster. Perhaps the most well-studied area of agent-based models of complex human behavior in disasters is that of evacuation during fires [122; 233; 234], earthquakes (e.g., [235; 236; 237]), tsunamis (e.g., [238]), hurricanes (e.g., [239]), volcanic eruptions (e.g., [128]), and floods (e.g., [240]). At a finer level of spatial resolution in predicting human behavior, [PERSON] et al. [241] modeled escape panic in the spirit of self-driven many-particle systems while [242] focused on more behavioral rich agents that lead to collective egress in evacuations. These and other agent-based modeling frameworks (see [243] for a review) have informed the development of theory on evacuation dynamics in emergencies and disaster. More current work builds on these efforts with further explorations of the complex dynamics in evacuation relating to second-order relationships. [PERSON] et al. [238] simulated evacuation for a near-field tsunami in an ABM, investigating the impacts of decision-time, modes of transportation, and the availability of evacuation paths on mortality rates. Just as models can inform theory, models can also inform data analysis.