Complex adaptive systems-of-systems are inherently multi-scale across several dimensions, including temporal, geographical, and organizational. We present a multi-model 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 virtual agents embedded in 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 behaviors. 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 educational policies. The outputs of the IBM, seen as changes in obesogenic (N&PA unhealthy) behavior prevalences, can be used as inputs to a SD model to calculate the resulting changes in mortality and morbidity over the ensuing decades. We analyze and compare effects of possible policy interventions, and illustrate a policy cocktail that addresses multiple aspects of the obesity problem, resulting in amplification of desirable results and a strong uncertainty reduction.

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Date created

• 2011 July 24-2011 July 28

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• Document

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• Processing documentation available at: https://wiki.albany.edu/display/SCA/Processing+Ingested+Digital+Files

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• Creative Commons BY-NC-ND Attribution-NonCommercial-NoDerivs 4.0 International

  
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28441c340962a2b363963713d1bba533

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e35e9d46c0556df862a8fbb9e32d2143

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System Dynamic Society Records

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• This collection is part of the University Archives