In this chapter, we consider a joint sampling and scheduling problem for optimizing data freshness in multisource systems. Data freshness is measured by a nondecreasing penalty function of Age of Information, where all sources have the same age-penalty function. Sources take turns to generate update samples, and forward them to their destinations one-by-one through a shared channel with random delay. There is a scheduler, that chooses the update order of the sources, and a sampler, that determines when a source should generate a new sample in its turn. We aim to find the optimal scheduler–sampler pairs that minimize the total-average age-penalty (Ta-AP). We start the chapter by providing a brief explanation of the sampling problem in the light of single–source networks, as well as some useful insights and applications on age of information and its penalty functions. Then, we move on to the multisource networks, where the problem becomes more challenging. We provide a detailed explanation of the model and the solution in this case. Finally, we conclude this chapter by providing an open question in this area and its inherent challenges.

To further explore the issues discussed in previous chapters, this chapter uses the city of Bloomington, Indiana, and its open data portal as a case study. As open data portals are considered to be an instantiation of digital commons, it is assumed that its design and governance would support cooperation and community participation and at least some forms of communal ownership, co-creation, and use. To test these assumptions, the GKC framework and its concepts and guiding questions are applied to this specific case to understand the actions around the portal and their patterns and outcomes.

Smart city technology has its value and its place; it isn’t automatically or universally harmful. Urban challenges andopportunities addressed via smart technology demand systematic study, examining general patterns and local variations as smart city practices unfold around the world. Smart cities are complex blends of community governance institutions, social dilemmas that cities face, and dynamic relationships among information and data, technology, and human lives. Some of those blends are more typical and common. Some are more nuanced in specific contexts. This volume uses the Governing Knowledge Commons (GKC) framework to sort out relevant and important distinctions. The framework grounds a series of case studies examining smart technology deployment and use in different cities. This chapter briefly explains what that framework is, why and how it is a critical and useful tool for studying smart city practices, and what the key elements of the framework are. The GKC framework is useful both here and can be used in additional smart city case studies in the future.

America’s market for legal technology presents a puzzle. On the one hand, America’s market for legal services is among the most tightly regulated in the world, suggesting infertile ground for a legal technology revolution. On the other side of this puzzle is America’s advanced and free-wheeling market for legal tech, which is likely the most robust in the world. This chapter explains this seeming puzzle and then uses that explanation to make some predictions about where legal technology will continue to flourish in America and where legacy players—lawyers, law schools, and judges—will instead stymie its development. In order to predict the future we first must understand the present and the past, so the chapter presents a brief overview of lawyer regulation, the structure of the American market for lawyers and legal services, and the current state of legal tech. This more granular view of the innovation ecosystem can explain why some tech sectors are booming, while others remain stubbornly behind, and also where we’ll see continued and even accelerated legal tech growth and where we won’t.

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.

Should the justice system sustain remote operations in a post-pandemic world? Commentators are skeptical, particularly regarding online jury trials. Some of this skepticism stems from empirical concerns. This paper explores two oft-expressed concerns for sustaining remote jury trials: first, that using video as a communication medium will dehumanize parties to a case, reducing the human connection from in-person interactions and making way for less humane decision-making; and second, that video trials will diminish the ability of jurors to detect witness deception or mistake. Our review of relevant literature suggests that both concerns are likely misplaced. Although there is reason to exercise caution and to include strong evaluation with any migration online, available research suggests that video will neither materially affect juror perceptions of parties nor alter the jurors’ (nearly nonexistent) ability to discern truthful from deceptive or mistaken testimony. On the first point, the most credible studies from the most analogous situations suggest video interactions cause little or no effect on human decisions. On the second point, a well-developed body of social science research shows a consensus that human detection accuracy is only slightly above chance levels, and that such accuracy is the same whether the interaction is in person or virtual.

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.

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.

While age of Information (AoI) has gained importance as a metric characterizing the freshness of information in information-update systems and time-critical applications, most previous studies on AoI have been theoretical. In this chapter, we compile a set of recent works reporting AoI measurements in real-life networks and experimental testbeds, and investigating practical issues such assynchronization, the role of various transport layer protocols, congestion control mechanisms, application of machine learning for adaptation to network conditions, and device-related bottlenecks such as limited processing power.

What effect will potent new legal tech tools have on the civil litigation landscape, and what can or should we do about it? Recent trends in plaintiff win rates and damages awards suggest the American civil justice system is growing more slanted toward the “haves” at the expense of the “have-nots.” Some say that AI-fired legal tech tools will reverse this trend and democratize the system. We disagree. Potent new legal tech tools are surely coming. Many are already here. But these tools are, and will likely continue to be, unevenly distributed because of the privileged access to data and technical know-how of emerging consortia of corporations, law firms, and tech companies. As a result, legal tech will, at least over the near- to medium-term, further skew the litigation playing field, shaping not just the resolution of claims but also the evolution of substantive law. As the American civil justice system enters the digital age, the haves will be propelled yet further ahead.

The COVID-19 pandemic has powerfully disrupted the American legal system. Yet, as with so many other aspects of life, the pandemic was most powerful as an accelerant of trends already in motion. And nowhere has this been more evident in law than in the civil justice system’s uptake of new legal technologies. With “legal tech” tools of all shapes and sizes gaining traction, the system, long a bastion of stasis and tradition, has begun a profound transformation.

While a shift to virtual courts has been lauded by technological enthusiasts and reformers for decades, little research has examined how this technological change may affect vulnerable unrepresented persons and low-income people in the United States on the “have not” side of the digital divide. In this Chapter, we cast light on how virtual proceedings unfold for low-income unrepresented persons in the everyday. It is important to do so. To date, much of the conversation has lauded Zoom court proceedings as the future of civil justice, centering this praise on idealized forms of online proceedings and their conveniences, without interrogating the impact of the precarity that low-income people contend with or persistent digital divides. In marked departure, we examine how these new technologies affect the experiences of low-income unrepresented persons who encounter, and contend with, adversities within virtual court proceedings. We examine how these new technologies reconfigure the features, affordances, and barriers present within the civil justice system, and the impact of these new technologies on the psychology of judges, lawyers, and unrepresented persons, as well as the impact of these new technologies on the meaning of the judicial role and on a person’s unrepresented status.

In this chapter, we discuss the relationship between Age of Information and signal estimation error in real-time signal sampling and reconstruction. Consider a remote estimation system, where samples of a scalar Gauss–Markov signal are taken at a source node and forwarded to a remote estimator through a channel that is modeled as a queue. The estimator reconstructs an estimate of the real-time signal value from causally received samples. The optimal sampling policy for minimizing the mean square estimation error is presented, in which a new sample is taken once the instantaneous estimation error exceeds a predetermined threshold. When the sampler has no knowledge of current and history signal values, the optimal sampling problem reduces to a problem for minimizing a nonlinear Age of Information metric. In the AoI-optimal sampling policy, a new sample is taken once the expected estimation error exceeds a threshold. The threshold can be computed by low-complexity algorithms and the insights behind these algorithms are provided. These optimal sampling results were established (i) for general service time distributions of the queueing server, (ii) for both stable and unstable scalar Gauss–Markov signals, and (iii) for sampling problems both with and without a sampling rate constraint.

Faith in technology as a way to narrow the civil justice gap has steadily grown alongside an expanding menu of websites offering legal guides, document assembly tools, and case management systems. Yet little is known about the supply and demand of legal help on the internet. This chapter mounts a first-of-its-kind effort to fill that gap by measuring website traffic across the mix of commercial, court-linked, and public interest websites that vie for eyeballs online. Commercial sites, it turns out, dominate over the more limited ecosystem of court-linked and public interest online resources, and yet commercial sites often engage in questionable practices, including the baiting of users with incomplete information and then charging for more. Search engine algorithms likely bolster that dominance. Policy implications abound for a new generation of A2J technologies focused on making people’s legal journeys less burdensome and more effective. What role should search engines play to promote access to quality legal information? Could they, or should they, privilege trustworthy sources? Might there be scope for public-private partnerships, or even a regulatory role, to ensure that online searches return trustworthy and actionable legal information?

This chapter explores Age of Information (AoI) in the context of the timely source coding problem. In most of the existing literature, service (transmission) times are based on a given distribution. In the timely source coding problem, by using source coding schemes, we design the transmission times of the status updates. We observe that the average age minimization problem is different than the traditional source coding problem, as the average age depends on both the first and the second moments of the codeword lengths. For the age minimization problem, we first consider a greedy source coding scheme where all realizations are encoded. For this source coding scheme, we find the age-optimal real-valued code word lengths. Then, we explore the highest k selective encoding scheme, where instead of encoding all realizations, we encode only the most probable k realizations. For each source encoding scheme, we first determine the average age expressions and then, for a given pmf, characterize the age-optimal k value, and find the corresponding age-optimal codeword lengths. Through numerical results, we show that selective encoding schemes achieve lower average age than encoding all realizations.