亚当•斯密于1776年提出了一个观点，彻底颠覆了传统的经济学理论。他宣称，人们追逐私利的行为只受市场本身的调节，而不受政府的管控。这种追逐私利的行为就像一只看不见的手，将对市场进行自动的调节，从而使整个社会最大程度地获益。《捆住市场的手》这本书回顾了亚当•斯密之后两百年的经济学者是怎样挑战或再次肯定斯密的理论的。一些学者坚持认为，在市场不稳定的情况下，需要政府代替整个社会对市场进行干预。另一些学者则认为，政府的干预最终会给市场和社会造成损害。
斯蒂夫•G. 梅德玛探讨的这个主题，200年来，有可能是现代经济学领域中被人们争论最多的一个议题。梅德玛详尽地回顾了19世纪40年代到20世纪50年代市场失灵理论，以及随后对市场失灵理论进行批驳的芝加哥学派和弗吉尼亚学派的观点。从约翰•密尔到亨利•西季威克和庇古所代表的剑桥传统的福利经济学派，再到对剑桥学派提出挑战的罗纳德•科斯，最后是重新肯定亚当•斯密学说的批评家们，梅德玛向我们详细列举了关于市场控制的各种争议。他在书中向我们展示了，在边际革命之后——和亚当•斯密之前的前古典主义学者一样——新古典主义经济学家相信政府可以有效地缓解利己主义所带来的不良后果，芝加哥和弗吉尼亚学派则严厉地批驳了这种观点，证明利己主义同样能够影响政府，并在诸多并不完善的替代方案中为人们提供了一种相对合理的选择。
《捆住市场的手》还描写了政府是怎样一直致力于从经济上解决利己主义对公众利益造成的负面影响，及其采取的具体措施。
专家推荐
亚当•斯密认为市场机制相当于一只看不见的手，这种机制能成功地约束人们自利的行为，自18世纪后期以来，亚当•斯密的这种理论已经成为我们的政策争论的中心……梅德玛的《捆住市场的手》中详细描述了亚当•斯密时代至今200年间，人们就他的理论进行的争论……我向读者强烈推荐《捆住市场的手》。
——R.B.埃米特，《选择》杂志
《捆住市场的手》向读者介绍了自古希腊以来（特别是自亚当•斯密的时代以来）人类社会经济福利的演化情况，在某种程度上，这本书具有很强的专业性。但同时……我发现《捆住市场的手》也是一本发人深省的优秀著作。在福利、市场失灵和国家作用等诸多问题上，不同年代的人在不同角度上对福利的理解和评价的确能给当代人许多有益的启示。
——黛安•科伊尔，《开明经济学家》
这是一本出色的著作。它既涉及了经济学中的一个重要议题，文笔又引人入胜，让人丝毫不觉得枯燥。
——彼得•格鲁尼维根，《经济史回顾》杂志
这是一本引人入胜的好书，有趣、博学、公正，文字流畅。总之，我从这本书里学到了很多。
——丹尼斯•奥布莱恩，英国杜伦大学名誉教授
这是一部能使许多读者感兴趣的著作，简明易懂，观点清晰，它甚至能吸引一部分通常对历史不感兴趣的经济学家。梅德玛在书中详细讲述了不同时代的经济学家对利己主义的不同态度，以及它怎样改变了人们对于政府和市场的认知。
——罗杰.E.贝克豪斯，英国伯明翰大学

What makes people smarter than computers? These volumes by a pioneering neurocomputing group suggest that the answer lies in the massively parallel architecture of the human mind. They describe a new theory of cognition called connectionism that is challenging the idea of symbolic computation that has traditionally been at the center of debate in theoretical discussions about the mind. The authors' theory assumes the mind is composed of a great number of elementary units connected in a neural network. Mental processes are interactions between these units which excite and inhibit each other in parallel rather than sequential operations. In this context, knowledge can no longer be thought of as stored in localized structures; instead, it consists of the connections between pairs of units that are distributed throughout the network. Volume 1 lays the foundations of this exciting theory of parallel distributed processing, while Volume 2 applies it to a number of specific issues in cognitive science and neuroscience, with chapters describing models of aspects of perception, memory, language, and thought.

Drawing on his theory of the origins of the modern mind, Merlin Donald's thesis presents the forces, both cultural and neuronal, that power our distinctively human modes of awareness. Donald proposes that the human mind is a hybrid product of interweaving a supercomplex form of matter (the brain) with an invisible symbolic web (culture) to form a "distributed" cognitive network. This hybrid mind, Donald suggests, is our main evolutionary advantage, for it allowed humanity as a species to break free of the limitations of the mammalian brain.

Causality is central to the understanding and use of data. Without an understanding of cause effect relationships, we cannot use data to answer questions as basic as, “Does this treatment harm or help patients?” But though hundreds of introductory texts are available on statistical methods of data analysis, until now, no beginner-level book has been written about the exploding arsenal of methods that can tease causal information from data.
Causal Inference in Statistics fills that gap. Using simple examples and plain language, the book lays out how to define causal parameters; the assumptions necessary to estimate causal parameters in a variety of situations; how to express those assumptions mathematically; whether those assumptions have testable implications; how to predict the effects of interventions; and how to reason counterfactually. These are the foundational tools that any student of statistics needs to acquire in order to use statistical methods to answer causal questions of interest.
This book is accessible to anyone with an interest in interpreting data, from undergraduates, professors, researchers, or to the interested layperson. Examples are drawn from a wide variety of fields, including medicine, public policy, and law; a brief introduction to probability and statistics is provided for the uninitiated; and each chapter comes with study questions to reinforce the readers understanding.

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.

《现代经济学主要流派》一书是经济学思想史领域实至名归的经典之作，系统地梳理和评价了自1870年以来经济学思想的发展和演变。这一巨著到今天为止仍然是非常“现代”的，毫不过时，《现代经济学主要流派》在对约翰·斯图亚特·穆勒以来的古典经济学稍作回溯后，起旨一编便是“对形式主义的反叛”。古典主义者遭到了德国历史学派、马克思主义者和制度主义者的攻击。此后，通过由杰文斯、奥地利学派、约翰·贝茨·克拉克重新发现的边际主义，传统得以重申。由瓦尔拉斯和帕累托给出的均衡经济学分析，经希克斯和萨缪尔森等人的发展，成为经济学思想的主流。塞利格曼把现代经济学说的根本特征刻画出来了，那就是为技术而技术。
在熊彼特、凯恩斯、斯拉法，琼·罗宾逊夫人和张伯伦等经济学家共同强调市场竞争的不完全性之后，新古典学派经济学家重新提出了许多自足的模型，古典的形式主义回来，“复仇”了。
如今经济学受到技术的影响如此之深远，以至于我们的下一代也许不得不再来一次“重新发现”，才能找到让它冲开技术之樊篱的出路。塞利格曼这《现代经济学主要流派》正是一个非常好的开端。

弗洛依德勇敢的將科學帶進我們的想像世界後一百年，
科學到底對腦有些什麼認識？
他落伍了？還是誤導了？
跨越弗洛依德，夢到底告訴了我們什麼？
弗洛依德相信夢是了解人腦的關鍵，這是對的。他假設任何心理學的基礎都必須建立在腦上，也是對的。
但他沒有這個基礎，只好猜測；而且我發現，他對腦科學（seience of the mind）的貢獻，說好是落伍，說壞則是誤導。
我對他的企圖心敬佩有加，讓我不禁想像依據當代夢的科學他會說些什麼。
本書始於我對弗洛依德所做的一個惡作劇式的白日夢，但它是根據我身為神經科學家專業的路上所做的十三個真實的夢建構起來的。
這十三章中，每一章起頭都有個注釋，有時是草圖，是從夢中醒來盡快畫下的。
數十年前我便做過其中很多夢。對一些人會顯得特別有趣的是，我中風後最近所做的一些夢。中風引發各種對神經官能的微妙影響，以奇妙的方式表現在我的夢中。這些親身經驗結合研究頭腦數十年所得的知識，對弗洛依德勇敢將科學帶進我們的想像世界後一百年，科學到底對腦有些什麼認識，我希望讀者有新的理解。 -------本書作者 / J.Allan Hobson, M.D.

Manfred D. Laubichler, Science
[A] brilliant biography, one can hardly imagine a better summary of Karl Pearson's fascinating life and complicated persona. --This text refers to the Hardcover edition.
Review
John Aldrich American Scientist : Exceeds all expectations in recreating the intellectual worlds in which Pearson tried to find a home.
Manfred D. Laubichler Science : [A] brilliant biography, one can hardly imagine a better summary of Karl Pearson's fascinating life and complicated persona.
Peter J. Bowler Nature : Highlights the complex route by which [Pearson's] quest for emotional and intellectual satisfaction led him towards . . . modern statistics.
Jenny Marie Journal of the History of Biology : This book is a remarkable achievement.
Richard J. Cleary The American Statistician : Very effectively conveys . . . that . . . [statistics allows students] to see the world in a new and beautiful way.
Ramachandran Bharath MAA Reviews : Theodore Porter's Karl Pearson explores the fullness and richness of Pearson's intellectual and emotional life.

Review
Journal of Modern History : The book is a pleasure to read: the prose sparkles; the protagonists are vividly drawn; the illustrations are handsome and illuminating; the insights plentiful and sharp. This will remain the definitive work on the early development of mathematical statistics for some time to come.
--Lorraine J. Daston
Science : An exceptionally searching, almost loving, study of the relevant inspirations and aberrations of its principal characters James Bernoulli, de Moivre, Bayes, Laplace, Gauss, Quetelet, Lexis, Galton, Edgeworth, and Pearson, not neglecting a grand supporting cast...The definitive record of an intellectual Golden Age, an overoptimistic climb to a height not to be maintained.
--M. Stone
New York Times Book Review : One is tempted to say that the history of statistics in the nineteenth century will be associated with the name Stigler.
--Morris Kline
Contemporary Psychology : In this tour de force of careful scholarship, Stephen Stigler has laid bare the people, ideas, and events underlying the development of statistics...He has written an important and wonderful book...Sometimes Stigler's prose is so evocative it is almost poetic.
--Howard Wainer
Review
Stigler's book exhibits a rare combination of mastery of technical materials, sensitivity to conceptual milieu, and near exhaustive familiarity with primary sources. An exemplary study
--Lorraine Daston

工程师大卫·金（约翰·赵 饰）一直引以为傲的16岁乖女玛戈特突然失踪。前来调查此案的警探怀疑女儿离家出走。不满这一结论的父亲为了寻找真相，独自展开调查。他打开了女儿的笔记本电脑，用社交软件开始寻找破案线索。大卫必须在女儿消失之前，沿着她在虚拟世界的足迹找到她…

In the Origin of Wealth, Eric Beinhocker offers a thorough and convincing new way to think about economic growth and business management. The author begins by exploring the roots of modern economic theory and ultimately declares it outmoded and wrong. Instead, he suggests, markets and growth can best be explained by drawing on the emerging field of complexity economics: the study of markets and social systems as complex adaptive systems. Although biological metaphors in business have become familiar (i.e., organizations are living organisms), Beinhocker moves beyond metaphor to explain the revolutions in science that will inevitably change the way we think about economics, competition, and business. The Origin of Wealth raises important questions such as: How can one create strategy in uncertain and fast moving environments? Why is it hard for large organizations to be innovative and how should we organize for better results? What role should governments play in this new era?

This is a history of economic thought from Adam Smith to John Maynard Keynes--but it is a history with a difference. Firstly, it is history of economic theory, not of economic doctrines. Secondly, it includes detailed Reader's Guides to nine of the major texts of economics in the effort to encourage students to become acquainted at first hand with the writings of all the great economists. This fifth edition adds new Reader's Guides to Walras' Elements of Pure Economics and Keynes' General Theory of Employment, Interest and Money as well as major additions to the chapters on marginal productivity theory, general equilibrium theory and welfare economics.

The twenty-first century has seen a breathtaking expansion of statistical methodology, both in scope and in influence. 'Big data', 'data science', and 'machine learning' have become familiar terms in the news, as statistical methods are brought to bear upon the enormous data sets of modern science and commerce. How did we get here? And where are we going? This book takes us on an exhilarating journey through the revolution in data analysis following the introduction of electronic computation in the 1950s. Beginning with classical inferential theories - Bayesian, frequentist, Fisherian - individual chapters take up a series of influential topics: survival analysis, logistic regression, empirical Bayes, the jackknife and bootstrap, random forests, neural networks, Markov chain Monte Carlo, inference after model selection, and dozens more. The distinctly modern approach integrates methodology and algorithms with statistical inference. The book ends with speculation on the future direction of statistics and data science.
Clarifies both traditional methods and current, popular algorithms (e.g. neural nets, random forests)
Written by two world-leading researchers
Addressed to all fields that work with data

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.

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A classic since its publication in 1954, The Practice of Management was the first book to look at management as a whole and being a manager as a separate responsibility. The Practice of Management created the discipline of modern management practices. Readable, fundamental, and basic, it remains an essential book for students, aspiring managers, and seasoned professionals.
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管理的实践(珍藏版)

Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher François Chollet, this book builds your understanding through intuitive explanations and practical examples. You'll explore challenging concepts and practice with applications in computer vision, natural-language processing, and generative models. By the time you finish, you'll have the knowledge and hands-on skills to apply deep learning in your own projects.