This is a major, wide-ranging history of analytic philosophy since 1900, told by one of the tradition's leading contemporary figures. The first volume takes the story from 1900 to mid-century. The second brings the history up to date. As Scott Soames tells it, the story of analytic philosophy is one of great but uneven progress, with leading thinkers making important advances toward solving the tradition's core problems. Though no broad philosophical position ever achieved lasting dominance, Soames argues that two methodological developments have, over time, remade the philosophical landscape. These are (1) analytic philosophers' hard-won success in understanding, and distinguishing the notions of logical truth, a priori truth, and necessary truth, and (2) gradual acceptance of the idea that philosophical speculation must be grounded in sound prephilosophical thought. Though Soames views this history in a positive light, he also illustrates the difficulties, false starts, and disappointments endured along the way. As he engages with the work of his predecessors and contemporaries--from Bertrand Russell and Ludwig Wittgenstein to Donald Davidson and Saul Kripke--he seeks to highlight their accomplishments while also pinpointing their shortcomings, especially where their perspectives were limited by an incomplete grasp of matters that have now become clear. Soames himself has been at the center of some of the tradition's most important debates, and throughout writes with exceptional ease about its often complex ideas. His gift for clear exposition makes the history as accessible to advanced undergraduates as it will be important to scholars. Despite its centrality to philosophy in the English-speaking world, the analytic tradition in philosophy has had very few synthetic histories. This will be the benchmark against which all future accounts will be measured.

This book explores the ways in which statistical models, methods, and research designs can be used to open new possibilities for APC analysis. Within a single, consistent HAPC-GLMM statistical modeling framework, the authors synthesize APC models and methods for three research designs: age-by-time period tables of population rates or proportions, repeated cross-section sample surveys, and accelerated longitudinal panel studies. They show how the empirical application of the models to various problems leads to many fascinating findings on how outcome variables develop along the age, period, and cohort dimensions.

This volume is devoted to some of the most biologically significant control problems governed by continuous age-dependent population dynamics. It investigates the existence, uniqueness, positivity, and asymptotic behaviour of the solutions of the continuous age-structured models. Some comparison results are also established. In the optimal control problems the emphasis is on first order necessary conditions of optimality. These conditions allow the determination of the optimal control or the approximation of the optimal control problem. The exact controllability for some models with diffusion and internal control is also studied. These subjects are treated using new concepts and techniques of modern optimal control theory, such as Clarke's generalized gradient, Ekeland's variational principle, Hamilton-Jacobi equations, and Carleman estimates. A background in advanced calculus and partial differential equations is required. Audience: This work will be of interest to students in mathematics, biology, and engineering, and researchers in applied mathematics, control theory, and biology.

This book examines intelligence analysis in the digital age and demonstrates how intelligence has entered a new era. While intelligence is an ancient activity, the digital age is a relatively new phenomenon. This volume uses the concept of the "digital age" to highlight the increased change, complexity, and pace of information that is now circulated, as new technology has reduced the time it takes to spread news to almost nothing. These factors mean that decision-makers face an increasingly challenging threat environment, which in turn increases the demand for timely, relevant, and reliable intelligence to support policymaking. In this context, the book demonstrates that intelligence places greater demands on analysis work, as the traditional intelligence cycle is no longer adequate as a process description. In the digital age, it is not enough to accumulate as much information as possible to gain a better understanding of the world. To meet customers’ needs, the intelligence process must be centred around the analysis work – which in turn has increased the demand for analysts. Assessments, not least predictions, are now just as important as revealing someone else’s secrets. This volume will be of much interest to students of intelligence studies, security studies, and international relations.

Paleodemography is the field of enquiry that attempts to identify demographic parameters from past populations (usually skeletal samples) derived from archaeological contexts, and then to make interpretations regarding the health and well-being of those populations. However, paleodemographic theory relies on several assumptions that cannot easily be validated by the researcher, and if incorrect, can lead to large errors or biases. In this book, physical anthropologists, mathematical demographers and statisticians tackle these methodological issues for reconstructing demographic structure for skeletal samples. Topics discussed include how skeletal morphology is linked to chronological age, assessment of age from the skeleton, demographic models of mortality and their interpretation, and biostatistical approaches to age structure estimation from archaeological samples. This work will be of immense importance to anyone interested in paleodemography, including biological and physical anthropologists, demographers, geographers, evolutionary biologists and statisticians.

The creation, accumulation, and use of copious amounts of data are driving rapid change across a wide variety of industries and academic disciplines. This ‘Big Data’ phenomenon is the result of recent developments in computational technology and improved data gathering techniques that have led to substantial innovation in the collection, storage, management, and analysis of data. Real Estate Analysis in the Information Age: Techniques for Big Data and Statistical Modeling focuses on the real estate discipline, guiding researchers and practitioners alike on the use of data-centric methods and analysis from applied and theoretical perspectives. In it, the authors detail the integration of Big Data into conventional real estate research and analysis. The book is process-oriented, not only describing Big Data and associated methods, but also showing the reader how to use these methods through case studies supported by supplemental online material. The running theme is the construction of efficient, transparent, and reproducible research through the systematic organization and application of data, both traditional and 'big'. The final chapters investigate legal issues, particularly related to those data that are publicly available, and conclude by speculating on the future of Big Data in real estate.