A Bayesian Model of Social Influence under Risk and Uncertainty
Year: 2020
Type: preprint
Abstract: Humans live in an uncertain world and often rely on social information in order to reduce uncertainty. However, the relationship between uncertainty and social information use is not yet fully understood. In this work we argue that previous studies have often neglected different degrees of uncertainty that need to be accounted for when studying social information use. We introduce a novel experimental paradigm to measure risky decision making, wherein social information and uncertainty are manipulated. We also developed a Bayesian model of social information use. We show that across different levels of uncertainty; social influence follows similar principles. Social information is more impactful when individuals are more uncertain. Notably, this relationship holds for experimental manipulations of uncertainty but also for subjective uncertainty within experimental conditions. We conclude with discussing that social influence can be better understood when paying credit to subjective uncertainties and preferences.
Authors: Simon Kai Ciranka, Wouter van den Bos
Language: English
Subfield: General Decision Sciences
Field: Decision Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W3135595355