Novel Models Accounting for Social Interactions on Individual Choice Behavior

OBJECTIVES: Individual decisions are often influenced by social factors, especially in health-related contexts where choices impact both healthcare users and those around them. Current models in microeconomic choice theory typically overlook these social influences, treating decision makers as isolated entities driven solely by personal preferences. This significant oversight is particularly relevant in health-related decision-making, where choices are frequently made within social groupings.

METHODS: This study develops a model grounded in the goal-balancing paradigm, a theoretical framework that complements traditional econometric choice models. We examined scenarios where a single decision maker is influenced by identifiable individuals or roles when selecting among various personal healthcare options. Our approach involves formulating tractable models that incorporate social influences on preferences and screening behavior.

RESULTS: Our choice models encompass two primary types:

• Choice Behavior Mimicry Models: These models capture the impact of observed or recommended behaviors from single or multiple influencers. They also account for how direct knowledge of others' preferences can shape the decision-making process.
• Screening Behavior Models: These models elucidate the trade-offs between personal tastes and social influences that determine the availability or acceptability of alternatives.

For each model type, we discuss potential empirical applications, different social influence roles and mechanisms, appropriate measurement approaches, and the conditions necessary for statistical identification.

CONCLUSIONS: By integrating social context into the microeconomic framework of individual decision-making, our models offer a more realistic depiction of behavior within the utility maximization paradigm. They enhance the explanation and prediction of healthcare user behavior, providing valuable insights for researchers and policymakers. This integration supports the design, selection, and implementation of more effective health policies. We strongly encourage the empirical testing of these models in diverse health contexts to validate their robustness and applicability.