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(Ed.)
Neighborhood eects have an important role
in evacuation decision-making by a family. Owing to
peer influence, neighbors evacuating can motivate a family to evacuate. Paradoxically, if a lot of neighbors evacuate, then the likelihood of an individual or family deciding to evacuate decreases, for fear of crime and looting. Such behavior cannot be captured using standard models of contagion spread on networks, e.g., threshold, independent cascade, and linear threshold models. Here, we propose a new threshold-based graph dynamical system model, 2mode-threshold, which captures this dichotomy. We study theoretically the dynamical properties of 2mode-threshold in different
networks, and find significant differences from a standard threshold model. We build and characterize small
world networks of Virginia Beach, VA, where nodes
are geolocated families (households) in the city and
edges are interactions between pairs of families. We
demonstrate the utility of our behavioral model through
agent-based simulations on these small world networks.
We use it to understand evacuation rates in this region, and to evaluate the effects of modeling parameters on evacuation decision dynamics. Specifically, we quantify the effects of (i) network generation parameters, (ii) stochasticity in the social network generation
process, (iii) model types (2mode-threshold vs. stan-
dard threshold models), (iv) 2mode-threshold model
parameters, (v) and initial conditions, on computed
evacuation rates and their variability. An illustrative
example result shows that the absence of looting eect
can overpredict evacuation rates by as much as 50%.
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