Determining social mechanisms for sequential decision-making in a virtual pedestrian route choice experiment

TL;DR

This study investigates how humans use social information in sequential route choices, finding that following the majority of previous decisions is more common than weighting recent decisions, informing pedestrian movement models.

Contribution

It provides empirical evidence that humans predominantly follow majority decisions in social route choice, challenging previous assumptions about recent decision weighting.

Findings

Majority-following is the dominant social decision mechanism.

Little evidence supports recent decision weighting.

Participants' self-reports align with observed behaviors.

Abstract

Moving groups are routinely faced with a choice of different routes as part of their daily lives, such as choosing between exits from a building. Differences in moving speeds and environmental constraints often lead to individuals being able to observe the choices of others ahead. This social information can inform their decision-making, but exactly how this is being used remains an open question. Previous theoretical studies on animal groups have demonstrated that simple heuristics are plausible and accurate mechanisms, with some further predicting that more recent decisions are more heavily weighted. Experiments with fish corroborate the importance of more recent decisions; however, experimental work is limited. Here, we conduct an online survey with human participants to identify which social decision-making mechanism individuals follow. Contrary to previous experimental work, we find little indication that recent decisions are weighted more heavily; instead, our results suggest that following the majority of previous decisions is the dominant behaviour. Furthermore, self-reported decision-making mechanisms correlate with our experimental findings despite their variability, suggesting that on average individuals can recognize their behavioural tendencies. Our findings give insight into social sequential decision-making, and provide an empirical foundation for integrating realistic social decision mechanisms into pedestrian movement models.