Dynamic Programming: Finite States

Dynamic Programming: Finite States

Dynamic programming (DP) is a sub-field of optimization concerned with sequential decision making over time. The essential ideas of DP have been adopted in many applications, from robotics and AI to the sequencing of DNA. It is used around the world to control aircraft, route shipping, test products, recommend information on media platforms and solve major research problems. Dynamic Programming: Finite States treats the theory of dynamic programming and its applications in economics, finance, and operations research. It contains classical results on dynamic programming as well as extensions created by researchers and practitioners as they wrestle with formulating and solving dynamic models that can explain patterns observed in data. Adopting an abstract framework that provides great generality, this book facilitates rapid progress to the research frontier by combining rigorous theory with numerous applications, many solved exercises, and detailed open-source computer code. Provides a rigorous and unified presentation of recent advances in dynamic programming, along with code, algorithms, and many applications. Thomas J. Sargent is a Nobel prize winning economist and Professor of Economics at NYU. He has held positions at Stanford, Minnesota, Chicago, and Princeton, and served as President of the Econometric Society and of the American Economic Association. He is renowned for his influential research on macroeconomics, rational expectations, and policy analysis. John Stachurski is a Professor at the Australian National University specializing in mathematical and computational economics. As an economist he has made influential contributions to the study of Markov models and dynamic optimization. He is also a co-founder of QuantEcon, a popular platform for open source economic modelling.