Investigations of social influence in collective decision-making have become
possible due to recent technologies and platforms that record interactions in
far larger groups than could be studied before. Herding and its impact on
decision-making are critical areas of practical interest and research study.
However, despite theoretical work suggesting that it matters whether
individuals choose who to imitate based on cues such as experience or whether
they herd at random, there is little empirical analysis of this distinction. To
demonstrate the distinction between what the literature calls "rational" and
"irrational" herding, we use data on tens of thousands of loans from a
well-established online peer-to-peer (p2p) lending platform. First, we employ
an empirical measure of memory in complex systems to measure herding in
lending. Then, we illustrate a network-based approach to visualize herding.
Finally, we model the impact of herding on collective outcomes. Our study
reveals that loan performance is not solely determined by whether the lenders
engage in herding or not. Instead, the interplay between herding and the
imitated lenders' prior success on the platform predicts loan outcomes. In
short, herds led by expert lenders tend to pick loans that do not default. We
discuss the implications of this under-explored aspect of herding for platform
designers, borrowers, and lenders. Our study advances collective intelligence
theories based on a case of high-stakes group decision-making online