Pathways to reimagining commercial health insurance in India.

In this paper we explore how India's growing commercial health insurance (CHI) segment can be reformed to deliver adequate financial protection and good health outcomes. We lay out key issues in the demand- and supply-sides of the insurance market that need to be addressed for CHI to be more aligned toward universal health coverage (UHC). On the demand side, we identify a consumer who strays far from the rational actor paradigm and therefore one whose needs require a fundamentally different approach than the one that commercial health insurance in India has so far taken. We lay out precisely the different stages involved in bringing a consumer to the insurance market and the conditions under which that consumer is likely to purchase insurance. On the supply side, we describe the many concerns that a new entrant into the commercial health insurance market must grapple with. We conclude with a set of pathways that brings the two sides of the market together to shed light on possible pathways for reform in the commercial health insurance sector in India. Despite the many challenges that this sector faces in India, we believe that there is room for optimism, and with the right amount of regulatory foresight, even room for radical transformation.

Binary matrix factorization on special purpose hardware.

Many fundamental problems in data mining can be reduced to one or more NP-hard combinatorial optimization problems. Recent advances in novel technologies such as quantum and quantum-inspired hardware promise a substantial speedup for solving these problems compared to when using general purpose computers but often require the problem to be modeled in a special form, such as an Ising or quadratic unconstrained binary optimization (QUBO) model, in order to take advantage of these devices. In this work, we focus on the important binary matrix factorization (BMF) problem which has many applications in data mining. We propose two QUBO formulations for BMF. We show how clustering constraints can easily be incorporated into these formulations. The special purpose hardware we consider is limited in the number of variables it can handle which presents a challenge when factorizing large matrices. We propose a sampling based approach to overcome this challenge, allowing us to factorize large rectangular matrices. In addition to these methods, we also propose a simple baseline algorithm which outperforms our more sophisticated methods in a few situations. We run experiments on the Fujitsu Digital Annealer, a quantum-inspired complementary metal-oxide-semiconductor (CMOS) annealer, on both synthetic and real data, including gene expression data. These experiments show that our approach is able to produce more accurate BMFs than competing methods.

Caring Economics: A New Framework for Conceptualizing and Measuring Economic Activity.

This article introduces the reader to a new framework for conceptualizing and measuring economic activity called caring economics. Going beyond the conventional understanding of economic activity as that which unfolds in markets, caring economics highlights the work of care and caregiving that occurs within households and is often unpaid. This article also unveils a new set of measures based on the framework of caring economics that are urgently needed by policymakers and business leaders to foster personal, business, and national economic success.