Thomas Sargent
Department of Economics, New York University, Senior Fellow, Hoover Institution, Stanford University
Book Recommendations:
Recommended by Thomas Sargent
“This book is the product of years of Cochrane’s groundbreaking research on interrelated topics central to modern macroeconomics and finance. In addition to providing stern but fair criticisms of a vast technical literature, Cochrane shows by example how enlightening good data, econometrics, and economic theory can be when in the right hands.” (from Amazon)
by John H. Cochrane·You?
by John H. Cochrane·You?
A comprehensive account of how government deficits and debt drive inflation Where do inflation and deflation ultimately come from? The fiscal theory of the price level offers a simple answer: Prices adjust so that the real value of government debt equals the present value of taxes less spending. Inflation breaks out when people don’t expect the government to fully repay its debts. The fiscal theory is well suited to today’s economy: Financial innovation undermines money demand, and central banks don’t control the money supply or aggressively change interest rates, invalidating classic theories, while large debts and deficits threaten inflation and constrain monetary policy. This book presents a comprehensive account of this important theory from one of its leading developers and advocates. John Cochrane aims to make fiscal theory useful as a conceptual framework and modeling tool, and for analyzing history and policy. He merges fiscal theory with standard models in which central banks set interest rates, giving a novel account of monetary policy. He generalizes the theory to explain data and make realistic predictions. For example, inflation decreases in recessions despite deficits because discount rates fall, raising the value of debt; specifying that governments promise to partially repay debt avoids classic puzzles and allows the theory to apply at all times, not just during periods of high inflation. Cochrane offers an extensive rethinking of monetary doctrines and institutions through the eyes of fiscal theory, and analyzes the era of zero interest rates and post-pandemic inflation. Filled with research by Cochrane and others, The Fiscal Theory of the Price Level offers important new insights about fiscal and monetary policy.
Recommended by Thomas Sargent
“Expected utility theory underlies most of statistics, economics, and finance. But are utility functions and probabilities all that we need to formulate wise decisions? And where do utility functions and probabilities come from? Written by the distinguished creator of new decision theories Itzhak Gilboa, Theory of Decision under Uncertainty is a beautifully written critical account of decision theory that answers these and other important questions. Gilboa’s work opens doors for both theorists and applied workers.” (from Amazon)
by Itzhak Gilboa·You?
by Itzhak Gilboa·You?
This book describes the classical axiomatic theories of decision under uncertainty, as well as critiques thereof and alternative theories. It focuses on the meaning of probability, discussing some definitions and surveying their scope of applicability. The behavioral definition of subjective probability serves as a way to present the classical theories, culminating in Savage's theorem. The limitations of this result as a definition of probability lead to two directions – first, similar behavioral definitions of more general theories, such as non-additive probabilities and multiple priors, and second, cognitive derivations based on case-based techniques.
Recommended by Thomas Sargent
“Its remarkable clarity, range, and depth make this a magnificent book both to learn from and to teach. It opens the door to so many modern techniques while firmly grounding them in the statistical and mathematical theory given us by the founders. t is a wonderful book—truly exceptional.” (from Amazon)
by Mykel J. Kochenderfer, Tim A. Wheeler, Kyle H. Wray·You?
by Mykel J. Kochenderfer, Tim A. Wheeler, Kyle H. Wray·You?
A broad introduction to algorithms for decision making under uncertainty, introducing the underlying mathematical problem formulations and the algorithms for solving them. Automated decision-making systems or decision-support systems—used in applications that range from aircraft collision avoidance to breast cancer screening—must be designed to account for various sources of uncertainty while carefully balancing multiple objectives. This textbook provides a broad introduction to algorithms for decision making under uncertainty, covering the underlying mathematical problem formulations and the algorithms for solving them. The book first addresses the problem of reasoning about uncertainty and objectives in simple decisions at a single point in time, and then turns to sequential decision problems in stochastic environments where the outcomes of our actions are uncertain. It goes on to address model uncertainty, when we do not start with a known model and must learn how to act through interaction with the environment; state uncertainty, in which we do not know the current state of the environment due to imperfect perceptual information; and decision contexts involving multiple agents. The book focuses primarily on planning and reinforcement learning, although some of the techniques presented draw on elements of supervised learning and optimization. Algorithms are implemented in the Julia programming language. Figures, examples, and exercises convey the intuition behind the various approaches presented.