Abstract
We present a model of behavioral dynamics that combines a social network-based opinion dynamics model with behavioral mapping. The behavioral component is discrete and history-dependent to represent situations in which an individual’s behavior is initially driven by opinion and later constrained by physiological or psychological conditions that serve to maintain the behavior. Individuals are modeled as nodes in a social network connected by directed edges. Parameter sweeps illustrate model behavior and the effects of individual parameters and parameter interactions on model results. Mapping a continuous opinion variable into a discrete behavioral space induces clustering on directed networks. Clusters provide targets of opportunity for influencing the network state; however, the smaller the network the greater the stochasticity and potential variability in outcomes. This has implications both for behaviors that are influenced by close relationships verses those influenced by societal norms and for the effectiveness of strategies for influencing those behaviors.
Highlights
The investigation of health-related behaviors using social network analysis and modeling is a growing field that integrates behavioral, epidemiological, and computational research, and often utilizes models and methods of analysis from statistical physics
We describe a behavioral dynamics model that combines opinion dynamics in directed graph social networks, conflicting media messages, opinion-driven behavior and the effects of addiction
3.2.8.6 Advertising replace with education scenario In Figure 21, we show the results of an identical scenario, except this time removing the advertising node after the network has been driven to steady state under its influence
Summary
The investigation of health-related behaviors using social network analysis and modeling is a growing field that integrates behavioral, epidemiological, and computational research, and often utilizes models and methods of analysis from statistical physics. Opinion dynamics models are quantitative computational techniques for modeling the propagation of opinions between pairs of individuals This class of models derives from the Ising spin models employed in statistical physics, taking from those models the notion that the state of one element in a collection can, through interaction, affect the state of another [1,9]. Recognizing that individuals obtain and contextualize information largely from external sources, modeling that is inclusive of these externalities conforms to current thinking in social psychology and provides the flexibility to model diverse phenomena including the effects of different classes of interventions to influence opinions. This approach is an extension of research models that. Opinions approaching 0.0 are more negative towards smoking; those approaching 1.0 are more positive
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