This book concerns the area of automation in management science. Intended for fundamental researchers, it develops concepts related to advice. For development, it addresses political and economic considerations and substantiates the concepts by describing practical situations. It also provides a framework for modeling advice and a language for expressing advice-related concepts. It posits the problem of how to arrive at advice, presents solutions, and provides involved ontologies.

Chapter 1 explains what the book is about. Chapter 2 discusses advice with respect to coordination. It begins by discussing division of labor, autonomy, and coordination achieved through advice in autonomy. A detailed discussion on advice in the political system of democracy is discussed, ending with economic coordination, discussing both central planning and marketing.

Chapter 3 defines the term “advice” after a discussion on how to arrive at a definition of it. An instance x of advice must satisfy four identity criteria; for example, x must be performed by an individual called the advisor. An individual, called the recipient, who experiences x and can form reference relations on x, is also involved. Chapter 4 contains a detailed discussion on the interpretation of advice. The chapter also discusses misreferences and lost referents and provides a method to determine signs of misreference. It discusses how a recipient can make a rational choice in the context of the advice.

Chapter 5 focuses on kinds of advice. It presents an ontology of decision analysis and organizational choice and discusses in detail the role of probabilities. It then provides a model called decision information ontology, which is different from probability-based decision making. The ontology description consists of well-defined concepts of assumption, goal, evaluation, commitment, and capability. The chapter ends by presenting a taxonomy of advice. Chapter 6 defines and solves what it calls an advisor’s problem, which involves finding influencing advice. After addressing advice from simple explanations and predictions, it presents an overview of the framework for modeling advice. It describes the advice modeling language (AML) with grammar, features, and versions of the language. The features of the language include relations such as confidence relation, preference relation, mandatory relation, optional relation, inference relation, conflict relation, and consequence relation. The chapter ends by providing criteria for a robust solution, for a clear solution, and for empirical evidence. Chapter 7, the last chapter, summarizes various arguments in the book.

The back cover notes that the book is intended for graduate students and researchers in management science. The stress is on sociopolitical-economic analysis to derive the fundamental concepts. The initial chapters have meta-level discussions. The later chapters are more mathematical and computing oriented. To understand later chapters, one needs a background in logic [1] and ontologies [2]. Professionals working on automation in management will find the book to be a useful resource. It contains sufficient background for planning a tool for automated advice generation.