Through simulation modeling, we are investigating how improved signal processing and guided reorganization of network relationships can enhance adaptive capacity under intensifying disturbance regimes.
To this end, we are deploying a first-of-its-kind dynamic social network simulation model (SNIP – Social Network Influence Propagation) within Envision, an agent-based model of landscape change that integrates social network adaptation with actor learning and other dynamic landscape models (e.g. wildfire, human population growth, vegetation succession, fuels management) in spatially explicit representations of real landscapes and their trajectories of change over time (Figure 1)
We are using this coupled modeling platform to explore and test how empirically-identified social network relationships could evolve over time under projected future disturbance regimes to alter adaptive capacity and potentially constrain risk.
Figure 1. The Envision SES modeling framework, with emphasis on the social network.