Chapter 1 sets out the conceptual framework through which the book examines research evaluation and names the key players and processes involved. It begins by outlining The Evaluation Game’s key contention that research evaluation is a manifestation of a broader technology which the book refers to as “evaluative power.” Next, it describes how the evaluative power comes to be legitimized and how it introduces one of its main technologies: research evaluation systems. The chapter then defines games as top-down social practices and, on the basis of this conceptual framework, presents the evaluation game as a reaction to or resistance against the evaluative power. Overall, the chapter shows how the evaluation of both institutions and knowledge produced by researchers working in them have, unavoidably, become an integral element of the research process itself.

In this chapter, we describe the main goal of the book, its organization, course outline, and suggestions for instructions and self-study. The textbook material is aimed for a one-semester undergraduate/graduate course for mathematics and computer science students. The course might also be recommended for students of physics, interested in networks and the evolution of large systems, as well as engineering students, specializing in telecommunication. Our textbook aims to give a gentle introduction to the mathematical foundations of random graphs and to build a platform to understand the nature of real-life networks. The text is divided into three parts and presents the basic elements of the theory of random graphs and networks. To help the reader navigate through the text, we have decided to start with describing in the preliminary part (Part I) the main technical tools used throughout the text. Part II of the text is devoted to the classic Erdős–Rényi–Gilbert uniform and binomial random graphs. Part III concentrates on generalizations of the Erdős–Rényi–Gilbert models of random graphs whose features better reflect some characteristic properties of real-world networks.

Bayesian optimization is a methodology for optimizing expensive objective functions that has proven success in the sciences, engineering, and beyond. This timely text provides a self-contained and comprehensive introduction to the subject, starting from scratch and carefully developing all the key ideas along the way. This bottom-up approach illuminates unifying themes in the design of Bayesian optimization algorithms and builds a solid theoretical foundation for approaching novel situations.

The core of the book is divided into three main parts, covering theoretical and practical aspects of Gaussian process modeling, the Bayesian approach to sequential decision making, and the realization and computation of practical and effective optimization policies.

Following this foundational material, the book provides an overview of theoretical convergence results, a survey of notable extensions, a comprehensive history of Bayesian optimization, and an extensive annotated bibliography of applications.

The book opens with a discussion of the theoretical importance of the rise of social media, focusing on the way that decreases in transaction costs of communication change the potential for populations to solve their collective action problem. In addition, it highlights the historical importance of social media as a data source, with scholars having access to the communications of the public en masse for the first time. The cheapness and accessibility of this data democratizes data collection efforts, which changes the nature of research questions that can be asked by scholars.

This chapter first defines data science, its primary objectives, and several related terms. It continues by describing the evolution of data science from the fields of statistics, operations research, and computing. The chapter concludes with historical notes on the emergence of data science and related topics.

At the macroscale, thermodynamics rules the balances of energy and entropy. In nonisolated systems, the entropy changes due to the contributions from the internal entropy production, which is always nonnegative according to the second law, and the exchange of entropy with the environment. The entropy production is equal to zero at equilibrium and positive out of equilibrium. Thermodynamics can be formulated either locally for continuous media or globally for systems in contact with several reservoirs. Accordingly, the entropy production is expressed in terms of either the local or the global affinities and currents, the affinities being the thermodynamic forces driving the system away from equilibrium. Depending on the boundary and initial conditions, the system can undergo relaxation towards equilibrium or nonequilibrium stationary or time-dependent macrostates. As examples, thermodynamics is applied to diffusion, electric circuits, reaction networks, and engines.

Chapter 1 introduces the capacity challenge faced by modern wireless communication systems and presents ultra-dense wireless networks as an appealing solution to address it. Moreover, it provides background on the small cell concept – the fundamental building block of an ultra-dense wireless network – describing its main characteristics, benefits and drawbacks. This chapter also presents the structure of the book and the fundamental concepts required for its systematic understanding.

When thinking of city maps, we instinctively envision a network of links along which an ever-changing flow of traffic is carried. Such an idealised description, however, is not limited to the maps we are all familiar with. From the interactions between atoms and subatomic particles to the gravitational forces which act between the billions of galaxies stretching across the known universe, from the transmission of electrical signals in our brains to the complexity of social interactions between people, most if not all phenomena we encounter, consciously or not, find a natural representation in the form of networks. Indeed, it can be argued that the abstract notion of interacting objects resides at the very heart of our conceptual understanding of nature as it touches upon the very fabric of physical reality with its finite and discrete makeup. How can we leverage the mathematical study of interconnected objects, the theory of networks and graphs, in our quest of understanding nature, and what are its limitations?