Can Social Norms Explain Long-Term Trends in Alcohol Use? Insights from Inverse Generative Social Science.

Area of Science:

• Social Psychology
• Computational Social Science
• Behavioral Science

Background:

• Dual process theory explains behavior via intentional and unintentional processes.
• Existing models struggle to explain population-level differences in alcohol use between males and females.
• Generative sufficiency requires theories to explain observed behavioral patterns.

Purpose of the Study:

• To apply inverse generative social science (iGSS) methods to an agent-based model of dual process theory for alcohol use.
• To determine if a single parsimonious model can explain both male and female drinking patterns or if separate models are required.
• To identify interpretable model structures that fit New York State alcohol use trends.

Main Methods:

• Utilized inverse generative social science (iGSS) with a multi-objective, grammar-based genetic program.
• Searched model structure space to find optimal explanations for alcohol consumption.
• Validated identified model structures against reserved trend data for New York State.

Main Results:

• Identified a single, interpretable model structure explaining both male and female drinking patterns with high goodness-of-fit.
• The model successfully validated against independent alcohol use trend data.
• The model suggested a novel interpretation of norms in drinking intentions, positing low autonomy individuals may act against descriptive norms.

Conclusions:

• A parsimonious dual process model can explain population-level alcohol use differences between sexes.
• The findings highlight a potential tension between perceived norms and individual autonomy in shaping drinking behavior.
• Further research on autonomy distribution is needed to validate the model's implications regarding norm-behavior discrepancies.