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- W2929126672 abstract "Social norms have been demonstrated to strongly affect people's health choices, yet they are often not included in health models due to the complex interdependencies of reasoning about norm adoption over a large population. This article introduces two agent-based modeling frameworks designed explicitly for reasoning about the influence of social norms, lightweight normative architecture (LNA) and cognitive social learners (CSL), and illustrates their usage for modeling smoking cessation trends. LNA models the impact of personal, social, and environmental factors on recognition, adoption, and compliance with a single smoking norm, whereas CSL is capable of reasoning about multiple social norms. By incorporating a more complex normative reasoning model, CSL cannot only predict smoking trends but also accurately forecasts population-level responses to surveys on the social acceptability of smoking. These social models are an important complement to existing biological models of human health and wellness." @default.
- W2929126672 created "2019-04-11" @default.
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- W2929126672 date "2019-03-29" @default.
- W2929126672 modified "2023-09-30" @default.
- W2929126672 title "Using Agent-Based Models to Understand Health-Related Social Norms" @default.
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- W2929126672 doi "https://doi.org/10.1002/9781119485001.ch27" @default.
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