Froeb/McCann's MANAGERIAL ECONOMICS: A PROBLEM SOLVING APPROACH, 2E covers traditional material using a problem-based pedagogy built around common business mistakes. Models are used sparingly, and then only to the extent that they help students figure out why mistakes are made, and how to fix them. This edition's succinct, fast-paced presentation and challenging, interactive applications place students in the role of a decision maker who has to identify mistakes that reduce profits, and propose solutions to bring profits back up. The lively book provides an excellent ongoing reference for students pursuing business careers. New chapters and updates highlight mistakes that precipitated the financial crisis. With MANAGERIAL ECONOMICS, 2E your students are taught to use economics to not only identify profitable decisions, but also how to implement them within an organization.

    They built a fleet of turbine cars--automobiles with jet engines--and loaned them out to members of the public. The fleet logged over a million miles; the exercise was a raging success.            These turbine engines would run on any flammable liquid--tequila, heating oil, Chanel #5, diesel, alcohol, kerosene. If the cars had been mass produced, we might have cars today that do not require petroleum-derived fuels. The engine was also much simpler than the piston engine--it contained one-fifth the number of moving parts and required much less maintenance. The cars had no radiators or fan belts and never needed oil changes.             Yet Chrysler crushed and burned most of the cars two years later; the jet car's brief glory was over. Where did it all go wrong? Controversy still follows the program, and questions about how and why it was killed have never been satisfactorily answered.            Steve Lehto has interviewed all the surviving members of the turbine car program--from the metallurgist who created the exotic metals for the interior of the engine to the test driver who drove it at Chrysler’s proving grounds for days on end. Lehto takes these first-hand accounts and weaves them into a great story about the coolest car Detroit ever produced.

    Now, in this lucid and accessibly written book, Philip Ball applies state-of-the-art scientific understanding from the fields of biology, chemistry, geology, physics, and mathematics to these ancient mysteries, revealing how nature's seemingly complex patterns originate in simple physical laws. Tracing the history of scientific thought about natural patterns, Ball shows how common presumptions--for example, that complex form must be guided by some intelligence or that form always follows function--are erroneous and continue to mislead scientists today. He investigates specific patterns in depth, revealing that these designs are self-organized and that simple, local interactions between component parts produce motifs like spots, stripes, branches, and honeycombs. In the process, he examines the mysterious phenomenon of symmetry and why it appears--and breaks--in similar ways in different systems. Finally, he attempts to answer this profound question: why are some patterns universal? Illustrations throughout the text, many in full color, beautifully illuminate Ball's ideas.

    This book covers relevant geometric principles and how to represent objects algebraically so they can be computed and applied. Recent major developments in the theory and practice of scene reconstruction are described in detail in a unified framework. Richard Hartley and Andrew Zisserman provide comprehensive background material and explain how to apply the methods and implement the algorithms. First Edition HB (2000): 0-521-62304-9

    Distinguishing agents (e.g., molecules, cells, animals, and species) from their interactions (e.g., chemical reactions, immune system responses, sexual reproduction, and evolution), Flake argues that it is the computational properties of interactions that account for much of what we think of as beautiful and interesting. From this basic thesis, Flake explores what he considers to be today's four most interesting computational topics: fractals, chaos, complex systems, and adaptation.Each of the book's parts can be read independently, enabling even the casual reader to understand and work with the basic equations and programs. Yet the parts are bound together by the theme of the computer as a laboratory and a metaphor for understanding the universe. The inspired reader will experiment further with the ideas presented to create fractal landscapes, chaotic systems, artificial life forms, genetic algorithms, and artificial neural networks.

    Much of the book takes the form of a discussion between a teacher and his students. They propose various solutions to some mathematical problems and investigate the strengths and weaknesses of these solutions. Their discussion (which mirrors certain real developments in the history of mathematics) raises some philosophical problems and some problems about the nature of mathematical discovery or creativity. Imre Lakatos is concerned throughout to combat the classical picture of mathematical development as a steady accumulation of established truths. He shows that mathematics grows instead through a richer, more dramatic process of the successive improvement of creative hypotheses by attempts to 'prove' them and by criticism of these attempts: the logic of proofs and refutations.

    However, these activities can be viewed as two facets of the same field, and together they have undergone substantial development over the past ten years. In particular, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic models. Also, the practical applicability of Bayesian methods has been greatly enhanced through the development of a range of approximate inference algorithms such as variational Bayes and expectation propagation. Similarly, new models based on kernels have had a significant impact on both algorithms and applications. This new textbook reflects these recent developments while providing a comprehensive introduction to the fields of pattern recognition and machine learning. It is aimed at advanced undergraduates or first-year PhD students, as well as researchers and practitioners, and assumes no previous knowledge of pattern recognition or machine learning concepts. Knowledge of multivariate calculus and basic linear algebra is required, and some familiarity with probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.

    Aimed at undergraduate students in mathematics, physics, and engineering, the book's intuitive explanations, lack ofadvanced prerequisites, and consciously user-friendly prose style will help students to master the subject more readily than was previously possible. The key to this is the book's use of new geometric arguments in place of the standard calculational ones. These geometric arguments are communicatedwith the aid of hundreds of diagrams of a standard seldom encountered in mathematical works. A new approach to a classical topic, this work will be of interest to students in mathematics, physics, and engineering, as well as to professionals in these fields.

    Masterfully combining the results of years of teaching microeconomics at Harvard University, Andreu Mas-Colell, Michael Whinston, and Jerry Green have filled that conspicuous vacancy with their groundbreaking text, Microeconomic Theory.The authors set out to create a solid organizational foundation upon which to build the effective teaching tool for microeconomic theory. The result presents unprecedented depth of coverage in all the essential topics, while allowing professors to tailor-make their course to suit personal priorities and style. Topics such as noncooperative game theory, information economics, mechanism design, and general equilibrium under uncertainty receive the attention that reflects their stature within the discipline. The authors devote an entire section to game theory alone, making it free-standing to allow instructors to return to it throughout the course when convenient. Discussion is clear, accessible, and engaging, enabling the student to gradually acquire confidence as well as proficiency. Extensive exercises within each chapter help students to hone their skills, while the text's appendix of terms, fully cross-referenced throughout the previous five sections, offers an accessible guide to the subject matter's terminology. Teachers of microeconomics need no longer rely upon scattered lecture notes to supplement their textbooks. Deftly written by three of the field's most influential scholars, Microeconomic Theory brings the readability, comprehensiveness, and versatility to the first-year graduate classroom that has long been missing.

    Starting with a "macro" view of accounting information, the authors present real financial statements. They establish how a financial statement communicates the financing, investing, and operating activities of a business to users of accounting information. Kimmel, Weygandt and Kieso motivate students by grounding the discussion in the real world, showing them the relevance of the topics covered to their future.

    By staying current, remaining relevant, and adapting to emerging course needs, this market-leading text has continued to define the operating systems course. This Seventh Edition not only presents the latest and most relevant systems, it also digs deeper to uncover those fundamental concepts that have remained constant throughout the evolution of today's operation systems. With this strong conceptual foundation in place, students can more easily understand the details related to specific systems. New Adaptations * Increased coverage of user perspective in Chapter 1. * Increased coverage of OS design throughout. * A new chapter on real-time and embedded systems (Chapter 19). * A new chapter on multimedia (Chapter 20). * Additional coverage of security and protection. * Additional coverage of distributed programming. * New exercises at the end of each chapter. * New programming exercises and projects at the end of each chapter. * New student-focused pedagogy and a new two-color design to enhance the learning process.

    But while many people support transit in the abstract, it's often hard to channel that support into good transit investments.  Part of the problem is that transit debates attract many kinds of experts, who often talk past each other.  Ordinary people listen to a little of this and decide that transit is impossible to figure out. Jarrett Walker believes that transit can be simple, if we focus first on the underlying geometry that all transit technologies share. In Human Transit, Walker supplies the basic tools, the critical questions, and the means to make smarter decisions about designing and implementing transit services. Human Transit explains the fundamental geometry of transit that shapes successful systems; the process for fitting technology to a particular community; and the local choices that lead to transit-friendly development. Whether you are in the field or simply a concerned citizen, here is an accessible guide to achieving successful public transit that will enrich any community.

    It is a ‘ must’ book for those in every branch of science . . . in addition, economists, politicians, statesmen, and businessmen cannot afford to overlook cybernetics and its tremendous, even terrifying implications. "It is a beautifully written book, lucid, direct, and despite its complexity, as readable by the layman as the trained scientist." -- John B. Thurston, "The Saturday Review of Literature" Acclaimed one of the "seminal books . . . comparable in ultimate importance to . . . Galileo or Malthus or Rousseau or Mill," "Cybernetics" was judged by twenty-seven historians, economists, educators, and philosophers to be one of those books published during the "past four decades", which may have a substantial impact on public thought and action in the years ahead." -- Saturday Review

    Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications.Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives when interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications. The only necessary mathematical background is familiarity with elementary concepts of probability.The book is divided into three parts. Part I defines the reinforcement learning problem in terms of Markov decision processes. Part II provides basic solution methods: dynamic programming, Monte Carlo methods, and temporal-difference learning. Part III presents a unified view of the solution methods and incorporates artificial neural networks, eligibility traces, and planning; the two final chapters present case studies and consider the future of reinforcement learning.

    Highlights of the book include realistic problems and examples from a wide range of application areas. New to this edition are: much sharper pedagogy; an increase in the number of examples; more thorough development of the concepts; a greater range of homework problems; a greater number and variety of worked out examples; expanded coverage of dynamics modelling and Laplace transform topics; and integration of MATLAB, including many examples that are formatted in MATLAB.