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Infrastructure and commons are not typically thought to be related to one another. Both concepts have rich histories and varied meaning, and both involve complex phenomena that are the subject of study in various disciplines, including engineering, economics, political science, and law. There is no separate field of infrastructure study or commons study, and there are no settled universal theories or even definitions of infrastructure or commons. This chapter develops the foundation for bringing the concepts together. After a brief introduction to the modern conception of infrastructure and its traditional roots in large-scale, human-made physical resource systems, it discusses a few observations about traditional infrastructure resources, including the important observation that traditional infrastructures are generally “managed as commons”.

This chapter explains how cybersecurity increasingly connects to consumer safety and critical industrial infrastructure, as well as the digital economy and systems of democracy. Thus, the stakes of cyber-physical security have never been higher. From attacks on the energy sector to the attacks on the consumer Internet of things and democracy, cybersecurity governance is an existential concern in society. Regrettably, security is woefully inadequate. Market incentives privilege rapid product introduction rather than strong security. The chapter then suggests baseline recommendations, across all stakeholders, necessary for improving the cyber-physical ecosystem. It also looks at how cyber-physical systems complicate and increasingly shape already-difficult global cybersecurity governance questions such as when governments choose to stockpile knowledge of software vulnerabilities for cyber offense, rather than disclose them to secure critical infrastructure.

This chapter addresses the critical area of cyber-physical system privacy. Cyber-physical system privacy concerns encroach into intimate spaces in and around the body and in material spaces of industry, the home and society that were once distinctly bounded from the digital sphere. Privacy problems are also concerns about discrimination, such as using collected data for employment, insurance, and law enforcement decisions. Privacy problems in digital-physical spaces also raise a host of national security concerns. The chapter then explains some of the constraints that complicate privacy and recommends a baseline privacy-protection framework to address this extraordinary policy challenge. Transparency and notice to consumers about data gathering and sharing practices should represent absolute minimal standards of practice. But even this minimal standard is difficult to attain.

This chapter develops the wave mechanics formalism. The emphasis here is on symmetries and conservation laws: parity, linear and angular momentum, and the electromagnetic interaction. The only specific physical application is the completion of the study of an isolated hydrogen atom, with some discussion of the motion of a particle in a magnetic field. The chapter also outlines the general assumptions of quantum wave mechanics, which may be summarized as follows: the state of a physical system is represented by a wave function and each measurable attribute of the system is represented by a linear self-adjoint operator in the space of functions. To apply these general assumptions to a given physical system, one must give a specific prescription for the observables and their algebra, and one must adopt a definite form for the Hamiltonians as a function of the observables.

This chapter addresses how discourses around Internet freedom have served a variety of interests and ideologies. However, all of the various conceptions of Internet freedom have to be challenged in light of technological change. Traditional notions of Internet freedom are disconnected from actual technical, political, and market conditions. Internet freedom has a long history, but all incarnations center on the transmission and free flow of content, from John Perry Barlow's “A Declaration of the Independence of Cyberspace” and calls for freedom from regulation to the United States Department of State's Internet freedom foreign-policy campaign. Normative frameworks should adjust both to the realities of information control from private ordering and authoritarian power and the rising human rights challenges of cyber-physical systems.

This chapter examines randomness and how it applies (or does not) in both abstract mathematics and in physical systems. There are many different ways in which a sequence of events could be said to be ‘random’. The mathematical theory of random processes, sometimes called stochastic processes, depends on being able to construct joint probabilities of large sequences of random variables, which can be very tricky to say the least. There are, however, some kinds of random processes where the theory is relatively straightforward. One class is when the sequence has no memory at all; this type of sequence is sometimes called white noise. Random processes can be either stationary or ergodic. The chapter also discusses predictability in principle and practice, and explains why pulling numbers out of an address book leads to a distribution of first digits that is not at all uniform. Aside from sequences of variables, other manifestations of randomness include points, patterns, and Poisson distribution.

This chapter examines applications drawn from perturbation theory. The main topic in perturbation theory is the energy and spontaneous decay rate of the 21-cm hyperfine line in atomic hydrogen. Before there were electronic computers, people had quite an accurate theoretical understanding of the energy levels in helium and more complicated systems. The trick was (and is) to find approximation schemes that treat unimportant parts of a physical system in quite crude approximations while reducing the interesting parts to a problem simple enough that it is feasible to compute but yet detailed enough to yield accurate results. The approximation methods in the chapter deal with the effects of small changes in the Hamiltonian, resulting for example from the application of a static or time variable electric or magnetic field. This may cause small changes in energy levels, and it may induce transitions among eigenstates of the original Hamiltonian.

This chapter looks at the current state of physical systems controlled by an onboard computer. Typically this covers transportation systems such as cars, aircraft, railway systems, space systems, or even medical devices, all of them either for the expected harmfulness for people, or for the huge cost associated with their failure. The chapter shows how the increase of computer use in those systems has led to huge benefits, but also an exponential growth in complexity. Furthermore, the drawback of this massive introduction of computers to control systems is the lack of predictability for both computer and software. This chapter shows how the aerospace industry, and more generally critical embedded systems industries, is now facing a huge increase in the software size in their systems. This in turn creates a greater system complexity increase because of safety or performance objectives. Moreover, this complexity leads to the need to integrate even more advanced algorithms to sustain autonomy and energy efficiency.

Brain events often cause mental events, and mental events often cause brain events. Despite Wegner and Libet no scientific work does or could show that mental events do not cause brain events. This is because it would need evidence about when the mental events occur, and unless mental events cause brain events it could not have that evidence. It follows that there cannot be a well-justified deterministic theory of the brain as a closed physical system. There follows an analysis of the extent to which quantum theory allows for indeterminism in the brain.

This chapter explores some fundamental consequences of the correspondence between physical process and computations. Most physical questions may be answerable only through irreducible amounts of computation. Those that concern idealized limits of infinite time, volume, or numerical precision can require arbitrarily long computations, and so be considered formally undecidable. The behavior of a physical system may always be calculated by simulating explicitly each step in its evolution. Much of theoretical physics has, however, been concerned with devising shorter methods of calculation that reproduce the outcome without tracing each step. Computational irreducibility is common among the systems investigated in mathematics and computation theory, but it may well be the exception rather than the rule, since most physical questions may be answerable only through irreducible amounts of computation.

Orderly arrangements are characterized by internal relationships, and an important property shared by all physical systems is that the overwhelming majority of imaginable configurations exhibit no internal relationships. For large systems order is unexpected and calls out for explanation. This chapter addresses the following questions: How do we reconcile great improbability with existence? Where do instructions come from? How do instructions change our view of object probability? Is life the outcome of a computation? It discusses the evolution of life, the best-studied and most famous example of the engine of complexity in action. It argues that the engine of complexity is more general than the standard biological theory of evolution because it is not limited to life. It may operate in any system capable of carrying out the computation.

The two most frequently asked questions of students who are first learning about evolution are: How did life start? And, what is its future? The engine-of-complexity concept provides added perspective to both questions. This chapter first discusses the origins of the engines of complexity. Four examples of the engine of complexity operating today on Earth are: life, the mammalian adaptive immune system, social change (with religion, science and technology, and the economy as subexamples), and evolutionary computer algorithms. Every implementation of the engine requires a physical system to encode information and carry out the computation. The chapter compares the origin of each with the goal of finding common features. It then discusses what means to be in the middle of a computation.

This chapter provides general introductory information on the book as whole, offering the reader an overview of and orientation within the book. It is deliberately short to avoid redundancies, and it briefly outlines the book’s six parts. The book’s focus is on selected concepts, methods, and mathematical techniques in the area of strongly interacting quantum matter systems, especially the various Bethe ansatz techniques, and also aims to provide guidance for the understanding of other strongly interacting systems.

This chapter examines the power of quantum computing, as well as the related concepts of quantum cryptography and teleportation. In 1982, the Nobel prize-winning physicist Richard Feynman noticed there was no simple way of simulating quantum physical systems using digital computers. He turned this problem into an opportunity—perhaps a computational device based on quantum mechanics could solve problems more efficiently than more traditional computers. In the decades that followed, computer scientists and physicists, often working together, showed in theory that quantum computers can solve certain problems, such as factoring numbers, much faster. Whether one can actually build large or even medium-scale working quantum computers and determine exactly what these computers can or cannot do still remain significant challenges.