Minimum wages exist in more than one hundred countries, both industrialized and developing. The United States passed a federal minimum wage law in 1938 and has increased the minimum wage and its coverage at irregular intervals ever since; in addition, as of the beginning of 2008, thirty-two states and the District of Columbia had established a minimum wage higher than the federal level, and numerous other local jurisdictions had in place "living wage" laws. Over the years, the minimum wage has been popular with the public, controversial in the political arena, and the subject of vigorous debate among economists over its costs and benefits. In this book, David Neumark and William Wascher offer a comprehensive overview of the evidence on the economic effects of minimum wages. Synthesizing nearly two decades of their own research and reviewing other research that touches on the same questions, Neumark and Wascher discuss the effects of minimum wages on employment and hours, the acquisition of skills, the wage and income distributions, longer-term labor market outcomes, prices, and the aggregate economy. Arguing that the usual focus on employment effects is too limiting, they present a broader, empirically based inquiry that will better inform policymakers about the costs and benefits of the minimum wage. Based on their comprehensive reading of the evidence, Neumark and Wascher argue that minimum wages do not achieve the main goals set forth by their supporters. They reduce employment opportunities for less-skilled workers and tend to reduce their earnings; they are not an effective means of reducing poverty; and they appear to have adverse longer-term effects on wages and earnings, in part by reducing the acquisition of human capital. The authors argue that policymakers should instead look for other tools to raise the wages of low-skill workers and to provide poor families with an acceptable standard of living.

From the Internet to networks of friendship, disease transmission, and even terrorism, the concept - and the reality - of networks has come to pervade modern society. But what exactly is a network? What different types of networks are there? Why are they interesting, and what can they tell us? In recent years, scientists from a range of fields - including mathematics, physics, computer science, sociology, and biology - have been pursuing these questions and building a new 'science of networks.' This book brings together, for the first time, a set of seminal articles representing research from across these disciplines. It is an ideal sourcebook for the key research in this fast-growing field. The book is organized into four sections, each preceded by an editors' introduction summarizing its contents and general theme. The first section sets the stage by discussing some of the historical antecedents of contemporary research in the area. From there the book moves to the empirical side of the science of networks before turning to the foundational modeling ideas that have been the focus of much subsequent activity. The book closes by taking the reader to the cutting edge of network science - the relationship between network structure and system dynamics. From network robustness to the spread of disease, this section offers a potpourri of topics on this rapidly expanding frontier of the new science.

Networks are everywhere, from the Internet, to social networks, and the genetic networks that determine our biological existence. Illustrated throughout in full colour, this pioneering textbook, spanning a wide range of topics from physics to computer science, engineering, economics and the social sciences, introduces network science to an interdisciplinary audience. From the origins of the six degrees of separation to explaining why networks are robust to random failures, the author explores how viruses like Ebola and H1N1 spread, and why it is that our friends have more friends than we do. Using numerous real-world examples, this innovatively designed text includes clear delineation between undergraduate and graduate level material. The mathematical formulas and derivations are included within Advanced Topics sections, enabling use at a range of levels. Extensive online resources, including films and software for network analysis, make this a multifaceted companion for anyone with an interest in network science.
Uses an interdisciplinary perspective with examples from across scientific and social science fields making it accessible to students of various subjects
Supported by a fully interactive online version with numerous multimedia resources to assist students in their learning
The first textbook of its kind in a rapidly expanding field

The philosophical theory of scientific explanation proposed here involves a radically new treatment of causality that accords with the pervasively statistical character of contemporary science. Wesley C. Salmon describes three fundamental conceptions of scientific explanation - the epistemic, modal, and ontic. He argues that the prevailing view (a version of the epistemic conception) is untenable and that the modal conception is scientifically out-dated. Significantly revising aspects of his earlier work, he defends a causal/mechanical theory that is a version of the ontic conception. Professor Salmon's theory furnishes a robust argument for scientific realism akin to the argument that convinced twentieth-century physical scientists of the existence of atoms and molecules. To do justice to such notions as irreducibly statistical laws and statistical explanation, he offers a novel account of physical randomness. The transition from the 'reviewed view' of scientific explanation (that explanations are arguments) to the causal/mechanical model requires fundamental rethinking of basic explanatory concepts.

Counterfactuals is David Lewis's forceful presentation of and sustained argument for a particular view about propositions which express contrary-to-fact conditionals, including his famous defense of realism about possible worlds. Since its original publication in 1973, it has become a classic of contemporary philosophy, and is essential reading for anyone interested in the logic and metaphysics of counterfactuals. The book also includes an appendix of related writings by Lewis.

Emerging as a hot topic in the mid-twentieth century, causality is one of the most frequently discussed issues in contemporary philosophy. Causality has been a central concept in philosophy as well as in the sciences, especially the natural sciences, dating back to its beginning in Greek thought. David Hume famously claimed that causality is the cement of the universe. In general terms, it links eventualities, predicts the consequences of action, and is the cognitive basis for the acquisition and the use of categories and concepts in the child. Indeed, how could one answer why-questions, around which early rational thought begins to revolve, without hitting on the relationships between reason and consequence, cause and effect, or without drawing these distinctions? But a comprehensive definition of causality has been notoriously hard to provide, and virtually every aspect of causation has been subject to much debate and analysis. "Thinking about Causes" brings together top philosophers from the United States and Europe to focus on causality as a major force in philosophical and scientific thought. Topics addressed include: ancient Stoicism and moral philosophy; the case of sacramental causality; traditional causal concepts in Descartes; Kant on transcendental laws; the influence of J. S. Mill's politics on his concept of causation; plurality in causality; causality in modern physics; causality in economics; and the concept of free will. Taken together, the essays in this collection provide the best current thinking about causality, especially as it relates to the philosophy of science.

Only human beings have a rich conceptual repertoire with concepts like tort, entropy, Abelian group, mannerism, icon and deconstruction. How have humans constructed these concepts? And once they have been constructed by adults, how do children acquire them? While primarily focusing on the second question, in The Origin of Concepts , Susan Carey shows that the answers to both overlap substantially.
Carey begins by characterizing the innate starting point for conceptual development, namely systems of core cognition. Representations of core cognition are the output of dedicated input analyzers, as with perceptual representations, but these core representations differ from perceptual representations in having more abstract contents and richer functional roles. Carey argues that the key to understanding cognitive development lies in recognizing conceptual discontinuities in which new representational systems emerge that have more expressive power than core cognition and are also incommensurate with core cognition and other earlier representational systems. Finally, Carey fleshes out Quinian bootstrapping, a learning mechanism that has been repeatedly sketched in the literature on the history and philosophy of science. She demonstrates that Quinian bootstrapping is a major mechanism in the construction of new representational resources over the course of childrens cognitive development.
Carey shows how developmental cognitive science resolves aspects of long-standing philosophical debates about the existence, nature, content, and format of innate knowledge. She also shows that understanding the processes of conceptual development in children illuminates the historical process by which concepts are constructed, and transforms the way we think about philosophical problems about the nature of concepts and the relations between language and thought.

Machine learning allows computational systems to adaptively improve their performance with experience accumulated from the observed data. Its techniques are widely applied in engineering, science, finance, and commerce. This book is designed for a short course on machine learning. It is a short course, not a hurried course. From over a decade of teaching this material, we have distilled what we believe to be the core topics that every student of the subject should know. We chose the title `learning from data' that faithfully describes what the subject is about, and made it a point to cover the topics in a story-like fashion. Our hope is that the reader can learn all the fundamentals of the subject by reading the book cover to cover. ---- Learning from data has distinct theoretical and practical tracks. In this book, we balance the theoretical and the practical, the mathematical and the heuristic. Our criterion for inclusion is relevance. Theory that establishes the conceptual framework for learning is included, and so are heuristics that impact the performance of real learning systems. ---- Learning from data is a very dynamic field. Some of the hot techniques and theories at times become just fads, and others gain traction and become part of the field. What we have emphasized in this book are the necessary fundamentals that give any student of learning from data a solid foundation, and enable him or her to venture out and explore further techniques and theories, or perhaps to contribute their own. ---- The authors are professors at California Institute of Technology (Caltech), Rensselaer Polytechnic Institute (RPI), and National Taiwan University (NTU), where this book is the main text for their popular courses on machine learning. The authors also consult extensively with financial and commercial companies on machine learning applications, and have led winning teams in machine learning competitions.

How do groups of neurons interact to enable the organism to see, decide, and move appropriately? What are the principles whereby networks of neurons represent and compute? These are the central questions probed by The Computational Brain. Churchland and Sejnowski address the foundational ideas of the emerging field of computational neuroscience, examine a diverse range of neural network models, and consider future directions of the field. The Computational Brain is the first unified and broadly accessible book to bring together computational concepts and behavioral data within a neurobiological framework.Computer models constrained by neurobiological data can help reveal how -networks of neurons subserve perception and behavior - bow their physical interactions can yield global results in perception and behavior, and how their physical properties are used to code information and compute solutions. The Computational Brain focuses mainly on three domains: visual perception, learning and memory, and sensorimotor integration. Examples of recent computer models in these domains are discussed in detail, highlighting strengths and weaknesses, and extracting principles applicable to other domains. Churchland and Sejnowski show how both abstract models and neurobiologically realistic models can have useful roles in computational neuroscience, and they predict the coevolution of models and experiments at many levels of organization, from the neuron to the system.The Computational Brain addresses a broad audience: neuroscientists, computer scientists, cognitive scientists, and philosophers. It is written for both the expert and novice. A basic overview of neuroscience and computational theory is provided, followed by a study of some of the most recent and sophisticated modeling work in the context of relevant neurobiological research. Technical terms are clearly explained in the text, and definitions are provided in an extensive glossary. The appendix contains a precis of neurobiological techniques.Patricia S. Churchland is Professor of Philosophy at the University of California, San Diego, Adjunct Professor at the Salk Institute, and a MacArthur Fellow. Terrence J. Sejnowski is Professor of Biology at the University of California, San Diego, Professor at the Salk Institute, where he is Director of the Computational Neurobiology Laboratory, and an Investigator of the Howard Hughes Medical Institute.

Graphical models, a marriage between probability theory and graph theory, provide a natural tool for dealing with two problems that occur throughout applied mathematics and engineering--uncertainty and complexity. In particular, they play an increasingly important role in the design and analysis of machine learning algorithms. Fundamental to the idea of a graphical model is the notion of modularity: a complex system is built by combining simpler parts. Probability theory serves as the glue whereby the parts are combined, ensuring that the system as a whole is consistent and providing ways to interface models to data. Graph theory provides both an intuitively appealing interface by which humans can model highly interacting sets of variables and a data structure that lends itself naturally to the design of efficient general-purpose algorithms.This book presents an in-depth exploration of issues related to learning within the graphical model formalism. Four chapters are tutorial chapters--Robert Cowell on Inference for Bayesian Networks, David MacKay on Monte Carlo Methods, Michael I. Jordan et al. on Variational Methods, and David Heckerman on Learning with Bayesian Networks. The remaining chapters cover a wide range of topics of current research interest.

An illustrated and comprehensive account of the history of Chinese painting from prehistoric times to the 21st century. It should be of interest to students and general readers who wish to gain an in-depth knowledge of Chinese painting.

What happens to our conception of mind and rational agency when we take seriously future-directed intentions and plans and their roles as inputs into further practical reasoning? The author's initial efforts in responding to this question resulted in a series of papers that he wrote during the early 1980s. In this book, Bratman develops further some of the main themes of these essays and also explores a variety of related ideas and issues. He develops a planning theory of intention. Intentions are treated as elements of partial plans of action. These plans play basic roles in practical reasoning, roles that support the organization of our activities over time and socially. Bratman explores the impact of this approach on a wide range of issues, including the relation between intention and intentional action, and the distinction between intended and expected effects of what one intends.

American Gods is Neil Gaiman's best and most ambitious novel yet, a scary, strange, and hallucinogenic road-trip story wrapped around a deep examination of the American spirit. Gaiman tackles everything from the onslaught of the information age to the meaning of death, but he doesn't sacrifice the razor-sharp plotting and narrative style he's been delivering since his Sandman days.
Shadow gets out of prison early when his wife is killed in a car crash. At a loss, he takes up with a mysterious character called Wednesday, who is much more than he appears. In fact, Wednesday is an old god, once known as Odin the All-father, who is roaming America rounding up his forgotten fellows in preparation for an epic battle against the upstart deities of the Internet, credit cards, television, and all that is wired. Shadow agrees to help Wednesday, and they whirl through a psycho-spiritual storm that becomes all too real in its manifestations. For instance, Shadow's dead wife Laura keeps showing up, and not just as a ghost--the difficulty of their continuing relationship is by turns grim and darkly funny, just like the rest of the book.
Armed only with some coin tricks and a sense of purpose, Shadow travels through, around, and underneath the visible surface of things, digging up all the powerful myths Americans brought with them in their journeys to this land as well as the ones that were already here. Shadow's road story is the heart of the novel, and it's here that Gaiman offers up the details that make this such a cinematic book--the distinctly American foods and diversions, the bizarre roadside attractions, the decrepit gods reduced to shell games and prostitution. "This is a bad land for Gods," says Shadow.
More than a tourist in America, but not a native, Neil Gaiman offers an outside-in and inside-out perspective on the soul and spirituality of the country--our obsessions with money and power, our jumbled religious heritage and its societal outcomes, and the millennial decisions we face about what's real and what's not. --Therese Littleton, Amazon.com