How can agent‐based modelling provide new insights into the impact of minimum unit pricing in Scotland?

Author:

Boyd Jennifer12ORCID,Holmes John3ORCID,Gibbs Naomi34,Buckley Charlotte5ORCID,Purshouse Robin5,Meier Petra2ORCID

Affiliation:

1. Salvation Army Centre for Addictions Services and Research, Faculty of Social Sciences University of Stirling Stirling UK

2. MRC/CSO Social and Public Health Sciences Unit School of Health and Wellbeing, University of Glasgow Glasgow UK

3. School of Medicine and Population Health, University of Sheffield Sheffield UK

4. Centre for Health Economics, University of York York UK

5. Department of Automatic Control and Systems Engineering University of Sheffield Sheffield UK

Abstract

AbstractIn recent years we have gained insight into the impact of minimum unit pricing (MUP)—a legal floor price below which a given volume of alcohol cannot be sold—on population‐level reductions in alcohol sales, consumption and harm. However, several questions remain unanswered including how individual‐level purchasing changes impact the local economy (e.g., balance between on‐licence and off‐licence outlets), lead to long‐term population‐level trends (e.g., youth drinking) and social harms (e.g., violence). Agent‐based modelling captures heterogeneity, emergence, feedback loops and adaptive and dynamic features, which provides an opportunity to understand the nuanced effects of MUP. Agent‐based models (ABM) simulate heterogeneous agents (e.g., individuals, organisations) often situated in space and time that interact with other agents and/or with their environment, allowing us to identify the mechanisms underlying social phenomena. ABMs are particularly useful for theory development, and testing and simulating the impacts of policies and interventions. We illustrate how ABMs could be applied to generate novel insights and provide best estimates of social network effects, and changes in purchasing behaviour and social harms, due to the implementation of MUP. ABMs like other modelling approaches can simulate alternative implementations of MUP (e.g., policy intensity [£0.50, £0.60] or spatial scales [local, national]) but can also provide an understanding of the potential impact of MUP on different population groups (e.g., alcohol exposure of young people who are not yet drinking). Using ABMs to understand the impact of MUP would provide new insights to complement those from traditional epidemiological and other modelling methods.

Funder

Chief Scientist Office

Medical Research Council

Publisher

Wiley

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