Understanding (Ir)rational Herding Online

TL;DR

This study empirically distinguishes between rational and irrational herding in online lending, showing that herding by experienced lenders improves loan outcomes, thus advancing understanding of collective decision-making online.

Contribution

It introduces empirical measures and network visualization methods to analyze herding behavior, revealing how herding by successful lenders influences loan performance.

Findings

Herding by expert lenders correlates with lower default rates.

Memory-based measures effectively quantify herding in online lending.

Network visualization illustrates the influence of herding on decision patterns.

Abstract

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.