Proceedings of the Annual Meeting of the Cognitive Science Society

Subway systems in large cities witness high volumes of commuter traffic, with crowded coaches and limited seats. In such scenarios, commuters often carefully position themselves in strategic locations with the aim of maximizing their chances of getting a place to sit. While user behavior in subways around the world have been the focus of multiple studies in the past, these everyday acts of ‘optimal decision making’ is of particular interest to the cognitive scientist. This paper inquires into commuter positioning choices in simulated subway coaches, within the framework of Bayesian probabilistic modelling. Data on preferred standing positions were collected across 20 subjects for 30 co-passenger configurations, through an interactive computer game. A generative model based on a Bayesian network involving three key spatial parameters was constructed, and used for inferring preferred positions conditioned on the specific configurations. The model was able to accurately simulate the quick and intuitive decisions made by the players under constraints of time, and also effectively capture noise in responses across subjects