An information cascade is a situation in which an agent who observes others chooses the same action irrespective of value agent’s private signal. Theoretical models have found that cascades result poor aggregation, inaccurate decisions, and fragility mass behaviors. We review theory social learning. Our goal to describe relatively integrated accessible way more important themes, insights applic...

We deene a (chaotic) deterministic variant of random multiplicative cascade models of turbulence. It preserves the hierarchical tree structure , thanks to the addition of innnitesimal noise. The zero-noise limit can be handled by Perron-Frobenius theory, just as the zero-diiusivity limit for the fast dynamo problem. Random multiplicative models do not possess Kolmogorov 1941 (K41) scaling becau...

Multiplicative processes and multifractals proved useful in various applications ranging from hydrodynamic turbulence to computer network traffic, to name but two. It was recently shown and explained how and why multifractal analysis could be interestingly and fruitfully placed in the more general framework of infinitely divisible cascades. The aim of this contribution is to design processes, c...

This paper combines behavioral economics and social learning. Overconfident agents overweigh their private information relative to the public information revealed by the decisions of others. Therefore, when following a herd, they broadcast more of the information available to them. However, overconfidence trades the additional information revealed by overconfident decisions against more informa...

A famous instance is a picture of a receipt taken by a customer at a hunting and fishing chain store called Cabela. The receipt listed a 2.3%“medical excise tax,” which was supposedly levied as part of the Affordable Care Act. This was seen as evidence that there is a hidden tax that consumers are paying for the ACA. The store later cleared this up as being a “software glitch” on their part. Th...

Structured prediction tasks pose a fundamental trade off between the need for model complexity to increase predictive power and the limited computational resources for inference in the exponentially-sized output spaces such models require. We formulate and develop structured prediction cascades: a sequence of increasingly complex models that progressively filter the space of possible outputs. W...