Microloan markets allow individual borrowers to raise funding from multiple individual lenders. We use a unique panel dataset which tracks the funding dynamics of borrower listings on Prosper.com, the largest microloan market in the United States. We find evidence of rational herding among lenders. Well-funded borrower listings tend to attract more funding after we control for unobserved listin...

This paper establishes a direct link between (anti) herding behavior in currency markets and investor sentiment, proxied by social media based happiness index built on Twitter feed data. Our analysis of daily data for nine developed market currencies suggests that the foreign exchange is generally characterized strong anti-herding behavior. Utilizing quantile-on-quantile (QQ) approach, Sim Zhou...

Herding is the process of bringing individuals (e.g. animals) together into a group. More specifically, we consider self– organized herding as the process of moving a set of individuals to a given number of locations (cluster centers) without any external control. We formally describe the relation between herding and clustering and show that any clustering model can be used to control herding p...

We incorporate strategic complementarities into a multi-agent sequential choice model with observable actions and private information. In this framework agents are concerned with learning from predecessors, signalling to successors, and coordinating their actions with those of others. Coordination problems have hitherto been studied using static coordination games which do not allow for learnin...

Herding and kernel herding are deterministic methods of choosing samples which summarise a probability distribution. A related task is choosing samples for estimating integrals using Bayesian quadrature. We show that the criterion minimised when selecting samples in kernel herding is equivalent to the posterior variance in Bayesian quadrature. We then show that sequential Bayesian quadrature ca...

OF THE DISSERTATION Herding: Driving Deterministic Dynamics to Learn and Sample Probabilistic Models By Yutian Chen Doctor of Philosophy in Computer Science University of California, Irvine, 2013 Professor Max Welling, Chair The herding algorithm was recently proposed as a deterministic algorithm to learn Markov random fields (MRFs). Instead of obtaining a fixed set of model parameters, herding...

We introduce a new mean field kinetic model for systems of rational agents interacting in a game-theoretical framework. This model is inspired from noncooperative anonymous games with a continuum of players and Mean-Field Games. The large time behavior of the system is given by a macroscopic closure with a Nash equilibrium serving as the local thermodynamic equilibrium. An application of the pr...

Abstract Herding is a deterministic algorithm used to generate data points regarded as random samples satisfying input moment conditions. This based on high-dimensional dynamical system and rooted in the maximum entropy principle of statistical inference. We propose an extension, entropic herding, which generates sequence distributions instead points. derived herding from optimization problem o...