27 Using Agent‐Based Models to Understand Health‐Related Social Norms

Gita Sukthankar1 and Rahmatollah Beheshti2

1 Department of Computer Science, University of Central Florida, Orlando, FL 32816, USA

2 School of Public Health, Johns Hopkins University, Baltimore, MD 21218, USA

Introduction

Agent‐based models (ABMs) have been shown to be valuable for many types of social simulation problems, including predicting the effects of geography, economic fluctuations, and public policy decisions on human populations. Understanding the influence of social norms on human behavior is an important aspect of performing accurate population‐level modeling, and forecasting norm emergence has attracted research attention in both the agent‐based social simulation and multi‐agent system communities.

In this chapter, we describe an agent‐based simulation that we constructed to model smoking cessation trends at University of Central Florida following the initiation of a smoke‐free campus policy. Since social norms have been shown to strongly affect health‐related habits such as overeating, binge drinking, and smoking, our simulation focuses on the social, rather than addictive, elements of the smoking cessation problem. Our lightweight normative architecture (LNA) (Beheshti and Sukthankar 2014b) models the impact of personal, social, and environmental factors on recognition, adoption, and compliance with campus smoking norms. When initialized with student survey data, it accurately ...

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