Traffic Studies and Mobility

Abstract

Despite the widespread use of discrete choice models to understand individual decision-making, a critical gap remains in accurately capturing the influence of social norms and peer effects within such frameworks. Traditional models often assume independence of irrelevant alternatives and neglect the subtle yet powerful role that social context and interpersonal influence play in shaping preferences. This research highlights the methodological challenges of incorporating social influence variables into discrete choice estimations and discusses emerging approaches—such as the estimation formulae of Opinion-Responsive Multinomial Logit Structures (ORMS)—designed to integrate dynamic social feedback into individual utility functions. In today’s highly interconnected and data-driven society, where opinions are rapidly shaped and disseminated through digital and physical social networks, accurately modelling social influence is more important than ever. Addressing this gap is vital for improving predictive accuracy and designing effective policies in areas such as transport, health, and technology adoption.

kEYWORDS: Discrete Choice Models, Social Norms, Peer Influence, Opinion-Responsive Models, Behavioural Modelling, Utility Estimation, ORMS, Social Networks, Choice Modelling, Decision-Making, Modern Consumer Behaviour, Social Influence in Economics, Interpersonal Effects, Agent-Based Preferences, Digital Society