Complex adaptive systems-of-systems are inherently multi-scale across several dimensions, including temporal, geographical, and organizational. We present a multimodel paradigm integrating a localized community-scale individual-based model (IBM) with a population scale system dynamics (SD) model to analyze long term results of potential policy interventions for obesity prevention.
The IBM uses a social network to simulate the spread of opinions relating to nutrition and physical activity (N&PA) behaviors such as dieting and exercise, and the effects of these opinions on individual actions.
The network structure uses a mixture of scale-free and uniformly random connections to represent a social network of relationships and interactions within a local community.
The N&PA related health behaviors of individuals change dynamically relative to endogenous influences within their social network and exogenous influences from industry-based advertising and public health-related counter-marketing and educational campaigns.
The outputs of the IBM, seen as changes in obesogenic behaviors, can be used as inputs to a SD model to calculate the resulting changes in mortality and morbidity over the ensuing decades