Sequential Decision Analytics and Modeling: Modeling with Python - Part II

Sequential decision problems arise in virtually every human process, spanning finance, energy, transportation, health, e-commerce and supply chains. They include pure learning problems as might arise in laboratory (or field) experiments. It even covers 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. This book uses a teach-by-example style to illustrate a modeling framework that can represent any sequential decision problem. A major challenge is, then, designing methods (called policies) for making decisions. We describe 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 we can immediately solve any problem, the framework helps us avoid the tendency in the academic literature of focusing on narrow classes of methods.