Books published by MIT Press Ltd

An integrated framework to study the theoretical and quantitative properties of economies with frictions in labor, financial, and goods markets.This book offers an integrated framework to study the theoretical and quantitative properties of economies with frictions in multiple markets. Building on analyses of markets with frictions by 2010 Nobel laureates Peter A. Diamond, Dale T. Mortensen, and Christopher A. Pissarides, which provided a new theoretical approach to search markets, the book applies this new paradigm to labor, finance, and goods markets. It shows, in particular, how frictions in different markets interact with each other.The book first covers the main developments in the analysis of the labor market in the presence of frictions, offering a systematic analysis of the dynamics of this environment and explaining the notion of macroeconomic volatility. Then, building on the generality and simplicity of the search analysis, the book adapts it to other markets, developing the tools and concepts to analyze friction in these markets. The book goes beyond the traditional general equilibrium analysis of markets, which is often frictionless. It begins with the standard analysis of a single market, and then sequentially integrates more markets into the analysis, progressing from labor to financial to goods markets. Along the way, the book provides a number of useful results and insights, including the existence of a direct link between search frictions and the degree of volatility in the economy.

Concise introductions to the main issues in energy policy and their interaction with environmental policies in the EU.

A concise and self-contained introduction to causal inference, increasingly important in data science and machine learning.The mathematization of causality is a relatively recent development, and has become increasingly important in data science and machine learning. This book offers a self-contained and concise introduction to causal models and how to learn them from data.After explaining the need for causal models and discussing some of the principles underlying causal inference, the book teaches readers how to use causal models: how to compute intervention distributions, how to infer causal models from observational and interventional data, and how causal ideas could be exploited for classical machine learning problems. All of these topics are discussed first in terms of two variables and then in the more general multivariate case. The bivariate case turns out to be a particularly hard problem for causal learning because there are no conditional independences as used by classical methods for solving multivariate cases. The authors consider analyzing statistical asymmetries between cause and effect to be highly instructive, and they report on their decade of intensive research into this problem. The book is accessible to readers with a background in machine learning or statistics, and can be used in graduate courses or as a reference for researchers. The text includes code snippets that can be copied and pasted, exercises, and an appendix with a summary of the most important technical concepts.

Physicians, philosophers, and theologians consider how to address death and dying for a diverse population in a secularized century.

An economist's perspective on the nuts and bolts of economic policymaking, based on his experience as the Chief Economic Adviser in India.

How big data is transforming the creative industries, and how those industries can use lessons from Netflix, Amazon, and Apple to fight back.

The work of art's mattering and materialization in a globalized world, with close readings of works by Takahashi Murakami, Andreas Gursky, Thomas Hirschhorn, and others.

Why our brains aren't built for media multitasking, and how we can learn to live with technology in a more balanced way.