Название: Sequential Decision Analytics and Modeling: Modeling with PythonАвтор: Warren B Powell
Издательство: Now Publishers
Год: 2022
Страниц: 316
Язык: английский
Формат: pdf (true)
Размер: 10.2 MB
Sequential decision problems arise in virtually every human process. They span finance, energy, transportation, health, e-commerce, and supply chains and include pure learning problems that arise in laboratory or field experiments. They even cover search algorithms to maximize uncertain functions. An important dimension of every problem setting is the need to make decisions in the presence of different forms of uncertainty and evolving information processes.
Warren B. Powell's work in sequential decision problems started in the 1980s and spanned rail, energy, health, finance, e-commerce, supply chain management, and even learning for materials science. His work on a wide range of problems highlighted the importance of using a variety of methods. In the process, he came to realize that any sequential decision problem can be modeled using a single universal framework that involves searching over methods for making decisions.
The goal of this book is to enable readers to understand how to approach, model and solve a sequential decision problem. To that end, it uses a teach-by-example style to illustrate a modeling framework that can represent any sequential decision problem. It tackles the challenge of designing methods, called policies, for making decisions and describes four classes of policies that are universal in that they span any method that might be used; whether from the academic literature or heuristics used in practice. While this does not mean that every problem can be solved immediately, the framework helps avoid the tendency in the academic literature of focusing on narrow classes of methods.
This book is aimed at undergraduate or masters level students who have taken a course in probability and statistics (a knowledge of linear programming is not necessary, although we have an example which requires solving a linear program). All the chapters are built around specific examples, with the exception of Chapter 1, which provides an overview of the entire modeling framework, and Chapter 7, where we pause and use the first six chapters to illustrate some important principles.
The presentation should not require mathematics beyond what would be expected in a first course on probability and statistics. This said, the book is centered on showing how to describe sequential decision problems using notation that is precise enough that it can be the basis of computer software. Python modules accompany most of the chapters; these modules were written around the modeling framework that runs throughout the book. At the same time, any software package that simulates a sequential decision problem, regardless of how it is being solved, can be translated directly into the modeling framework we use. For this reason, we encourage readers to look at any piece of notation as a variable in a computer program.
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