Decisions Under Uncertainty

In real life, as well as in economic models, individuals often make decisions in an uncertain environment. In many cases, a problem which an optimizing agent faces can be formulated or reformulated as a problem of optimal timing of a certain irreversible or partially reversible action or optimal stopping problem. In this book, the authors present an alternative approach to optimal stopping problems.

How to sharpen your decision-making skills and get ahead of your competitors

This book addresses an intriguing question: are our decisions rational? It explains seemingly irrational human decision-making behavior by taking into account our limited ability to process information. It also shows with several examples that optimization under granularity restriction leads to observed human decision-making.

Bayesian Networks, Markov Chains, Hidden Markov Models

This book shows, on numerous examples, how to make decisions in realistic situations when we have both uncertainty and constraints. In most these situations, the book's emphasis is on the why-question, i.e., on a theoretical explanation for empirical formulas and techniques. Such explanations are important: they help understand why these techniques work well in some cases and not so well in others, and thus, help practitioners decide whether a technique is appropriate for a given situation.

This book describes new techniques for making decisions in situations with uncertainty and new applications of decision-making techniques.