This staggering amount of information—unprecedented in history—can tell us a great deal about who we are—the fears, desires, and behaviors that drive us, and the conscious and unconscious decisions we make. From the profound to the mundane, we can gain astonishing knowledge about the human psyche that less than twenty years ago, seemed unfathomable.Everybody Lies offers fascinating, surprising, and sometimes laugh-out-loud insights into everything from economics to ethics to sports to race to sex, gender and more, all drawn from the world of big data. What percentage of white voters didn’t vote for Barack Obama because he’s black? Does where you go to school effect how successful you are in life? Do parents secretly favor boy children over girls? Do violent films affect the crime rate? Can you beat the stock market? How regularly do we lie about our sex lives and who’s more self-conscious about sex, men or women?Investigating these questions and a host of others, Seth Stephens-Davidowitz offers revelations that can help us understand ourselves and our lives better. Drawing on studies and experiments on how we really live and think, he demonstrates in fascinating and often funny ways the extent to which all the world is indeed a lab. With conclusions ranging from strange-but-true to thought-provoking to disturbing, he explores the power of this digital truth serum and its deeper potential—revealing biases deeply embedded within us, information we can use to change our culture, and the questions we’re afraid to ask that might be essential to our health—both emotional and physical. All of us are touched by big data everyday, and its influence is multiplying. Everybody Lies challenges us to think differently about how we see it and the world.

    He then jets forward to the nineteenth century, where systems we can easily recognize as modern bureaucracies come into being. In some areas of life—like with the modern postal systems of Germany and France—these bureaucracies have brought tremendous efficiencies to modern life. But Graeber argues that there is a much darker side to modern bureaucracy that is rarely ever discussed. Indeed, in our own “utopia of rules,” freedom and technological innovation are often the casualties of systems that we only faintly understand.Provocative and timely, the book is a powerful look and history of bureaucracy over the ages and its power in shaping the world of ideas.

    Friedman shows that we have entered an age of dizzying acceleration--and explains how to live in it. Due to an exponential increase in computing power, climbers atop Mount Everest enjoy excellent cell-phone service and self-driving cars are taking to the roads. A parallel explosion of economic interdependency has created new riches as well as spiraling debt burdens. Meanwhile, Mother Nature is also seeing dramatic changes as carbon levels rise and species go extinct, with compounding results.How do these changes interact, and how can we cope with them? To get a better purchase on the present, Friedman returns to his Minnesota childhood and sketches a world where politics worked and joining the middle class was an achievable goal. Today, by contrast, it is easier than ever to be a maker (try 3-D printing) or a breaker (the Islamic State excels at using Twitter), but harder than ever to be a leader or merely "average." Friedman concludes that nations and individuals must learn to be fast (innovative and quick to adapt), fair (prepared to help the casualties of change), and slow (adept at shutting out the noise and accessing their deepest values). With vision, authority, and wit, Thank You for Being Late establishes a blueprint for how to think about our times.

    This "old power" was out of reach for the vast majority of people. But our ubiquitous connectivity makes possible a different kind of power. "New power" is made by many. It is open, participatory, and peer-driven. It works like a current, not a currency--and it is most forceful when it surges. The battle between old and new power is determining who governs us, how we work, and even how we think and feel. New Power shines fresh light on the cultural phenomena of our day, from #BlackLivesMatter to the Ice Bucket Challenge to Airbnb, uncovering the new power forces that made them huge. Drawing on examples from business, activism, and pop culture, as well as the study of organizations like Lego, NASA, Reddit, and TED, Heimans and Timms explain how to build new power and channel it successfully. They also explore the dark side of these forces: the way ISIS has co-opted new power to monstrous ends, and the rise of the alt-right's "intensity machine."In an era increasingly shaped by new power, this groundbreaking book offers us a new way to understand the world--and our role in it.

    Reflecting the need for even minor programming in today's model-based statistics, the book pushes readers to perform step-by-step calculations that are usually automated. This unique computational approach ensures that readers understand enough of the details to make reasonable choices and interpretations in their own modeling work.The text presents generalized linear multilevel models from a Bayesian perspective, relying on a simple logical interpretation of Bayesian probability and maximum entropy. It covers from the basics of regression to multilevel models. The author also discusses measurement error, missing data, and Gaussian process models for spatial and network autocorrelation.By using complete R code examples throughout, this book provides a practical foundation for performing statistical inference. Designed for both PhD students and seasoned professionals in the natural and social sciences, it prepares them for more advanced or specialized statistical modeling.Web ResourceThe book is accompanied by an R package (rethinking) that is available on the author's website and GitHub. The two core functions (map and map2stan) of this package allow a variety of statistical models to be constructed from standard model formulas.

    But all too often, these numbers are wrong. This book is a lively guide to spotting bad statistics and learning to think critically about these influential numbers. Damned Lies and Statistics is essential reading for everyone who reads or listens to the news, for students, and for anyone who relies on statistical information to understand social problems.Joel Best bases his discussion on a wide assortment of intriguing contemporary issues that have garnered much recent media attention, including abortion, cyberporn, homelessness, the Million Man March, teen suicide, the U.S. census, and much more. Using examples from the New York Times, the Washington Post, and other major newspapers and television programs, he unravels many fascinating examples of the use, misuse, and abuse of statistical information.In this book Best shows us exactly how and why bad statistics emerge, spread, and come to shape policy debates. He recommends specific ways to detect bad statistics, and shows how to think more critically about "stat wars," or disputes over social statistics among various experts. Understanding this book does not require sophisticated mathematical knowledge; Best discusses the most basic and most easily understood forms of statistics, such as percentages, averages, and rates.This accessible book provides an alternative to either naively accepting the statistics we hear or cynically assuming that all numbers are meaningless. It shows how anyone can become a more intelligent, critical, and empowered consumer of the statistics that inundate both the social sciences and our media-saturated lives.

    The insurance industry’s actuarial tables and the gambler’s roulette wheel both yield to the tools of probability theory. Most situations in life, however, involve a deeper kind of uncertainty, a radical uncertainty for which historical data provide no useful guidance to future outcomes. Radical uncertainty concerns events whose determinants are insufficiently understood for probabilities to be known or forecasting possible. Before President Barack Obama made the fateful decision to send in the Navy Seals, his advisers offered him wildly divergent estimates of the odds that Osama bin Laden would be in the Abbottabad compound. In 2000, no one—not least Steve Jobs—knew what a smartphone was; how could anyone have predicted how many would be sold in 2020? And financial advisers who confidently provide the information required in the standard retirement planning package—what will interest rates, the cost of living, and your state of health be in 2050?—demonstrate only that their advice is worthless.The limits of certainty demonstrate the power of human judgment over artificial intelligence. In most critical decisions there can be no forecasts or probability distributions on which we might sensibly rely. Instead of inventing numbers to fill the gaps in our knowledge, we should adopt business, political, and personal strategies that will be robust to alternative futures and resilient to unpredictable events. Within the security of such a robust and resilient reference narrative, uncertainty can be embraced, because it is the source of creativity, excitement, and profit.

    Out of Control chronicles the dawn of a new era in which the machines and systems that drive our economy are so complex and autonomous as to be indistinguishable from living things.

    However, most discussions of Bayesian inference rely on intensely complex mathematical analyses and artificial examples, making it inaccessible to anyone without a strong mathematical background. Now, though, Cameron Davidson-Pilon introduces Bayesian inference from a computational perspective, bridging theory to practice-freeing you to get results using computing power. Bayesian Methods for Hackers illuminates Bayesian inference through probabilistic programming with the powerful PyMC language and the closely related Python tools NumPy, SciPy, and Matplotlib. Using this approach, you can reach effective solutions in small increments, without extensive mathematical intervention. Davidson-Pilon begins by introducing the concepts underlying Bayesian inference, comparing it with other techniques and guiding you through building and training your first Bayesian model. Next, he introduces PyMC through a series of detailed examples and intuitive explanations that have been refined after extensive user feedback. You'll learn how to use the Markov Chain Monte Carlo algorithm, choose appropriate sample sizes and priors, work with loss functions, and apply Bayesian inference in domains ranging from finance to marketing. Once you've mastered these techniques, you'll constantly turn to this guide for the working PyMC code you need to jumpstart future projects. Coverage includes - Learning the Bayesian "state of mind" and its practical implications - Understanding how computers perform Bayesian inference - Using the PyMC Python library to program Bayesian analyses - Building and debugging models with PyMC - Testing your model's "goodness of fit" - Opening the "black box" of the Markov Chain Monte Carlo algorithm to see how and why it works - Leveraging the power of the "Law of Large Numbers" - Mastering key concepts, such as clustering, convergence, autocorrelation, and thinning - Using loss functions to measure an estimate's weaknesses based on your goals and desired outcomes - Selecting appropriate priors and understanding how their influence changes with dataset size - Overcoming the "exploration versus exploitation" dilemma: deciding when "pretty good" is good enough - Using Bayesian inference to improve A/B testing - Solving data science problems when only small amounts of data are available Cameron Davidson-Pilon has worked in many areas of applied mathematics, from the evolutionary dynamics of genes and diseases to stochastic modeling of financial prices. His contributions to the open source community include lifelines, an implementation of survival analysis in Python. Educated at the University of Waterloo and at the Independent University of Moscow, he currently works with the online commerce leader Shopify.

    Part 1 is about the purpose and importance of having principles in general, having nothing to do with mine. Part 2 explains my most fundamental life principles that apply to everything I do. Part 3, explains my management principles as they are being lived out at Bridgewater. Since my management principles are simply my most fundamental life principles applied to management, reading Part 2 will help you to better understand Part 3, but it’s not required—you can go directly to Part 3 to see what my management principles are and how Bridgewater has been run. One day I’d like to write a Part 4 on my investment principles. If you are looking to get the most bang for your buck (i.e., understanding for the effort), I suggest that you read Parts 1 and 2, and the beginning of Part 3 (through the Summary and Table of Principles) which will give you nearly the whole picture. It’s only about 55 pages of a normal size book. Above all else, I want you to think for yourself—to decide 1) what you want, 2) what is true and 3) what to do about it. I want you to do that in a clear-headed thoughtful way, so that you get what you want. I wrote this book to help you do that. I am going to ask only two things of you—1) that you be open-minded and 2) that you honestly answer some questions about what you want, what is true and what you want to do about it. If you do these things, I believe that you will get a lot out of this book. If you can’t do these things, you should reflect on why that is, because you probably have discovered one of your greatest impediments to getting what you want out of life.

     If you've been on social media lately, or turned on your TV, you may have noticed a lot of dumb ideas floating around."We know when history will repeat and when it won't.""We can tell the difference between evidence and coincidences.""The simplest explanation is usually true."Wrong, wrong, and dangerous!If we're not careful, loserthink would have us believe that every Trump supporter is a bigoted racist, addicts should be responsible for fixing the opioid epidemic, and that your relationship fell apart simply because you chewed with your mouth open.Even the smartest people can slip into loserthink's seductive grasp. This book will teach you how to spot and avoid it--and will give you scripts to respond when hollow arguments are being brandished against you, whether by well-intentioned friends, strangers on the internet, or political pundits. You'll also learn how to spot the underlying causes of loserthink, like the inability to get ego out of your decisions, thinking with words instead of reasons, failing to imagine alternative explanations, and making too much of coincidences.Your bubble of reality doesn't have to be a prison. This book will show you how to break free--and, what's more, to be among the most perceptive and respected thinkers in every conversation.

     For decades, technology encouraged people to squander their time and intellect as passive consumers. Today, tech has finally caught up with human potential. In Cognitive Surplus, Internet guru Clay Shirky forecasts the thrilling changes we will all enjoy as new digital technology puts our untapped resources of talent and goodwill to use at last. Since we Americans were suburbanized and educated by the postwar boom, we've had a surfeit of intellect, energy, and time-what Shirky calls a cognitive surplus. But this abundance had little impact on the common good because television consumed the lion's share of it-and we consume TV passively, in isolation from one another. Now, for the first time, people are embracing new media that allow us to pool our efforts at vanishingly low cost. The results of this aggregated effort range from mind expanding-reference tools like Wikipedia-to lifesaving-such as Ushahidi.com, which has allowed Kenyans to sidestep government censorship and report on acts of violence in real time. Shirky argues persuasively that this cognitive surplus-rather than being some strange new departure from normal behavior-actually returns our society to forms of collaboration that were natural to us up through the early twentieth century. He also charts the vast effects that our cognitive surplus- aided by new technologies-will have on twenty-first-century society, and how we can best exploit those effects. Shirky envisions an era of lower creative quality on average but greater innovation, an increase in transparency in all areas of society, and a dramatic rise in productivity that will transform our civilization. The potential impact of cognitive surplus is enormous. As Shirky points out, Wikipedia was built out of roughly 1 percent of the man-hours that Americans spend watching TV every year. Wikipedia and other current products of cognitive surplus are only the iceberg's tip. Shirky shows how society and our daily lives will be improved dramatically as we learn to exploit our goodwill and free time like never before.

     The more tools you have at your disposal, the more likely you'll use the right tool for the job — and get it done right. The same is true when it comes to your thinking. The quality of your outcomes depends on the mental models in your head. And most people are going through life with little more than a hammer. Until now. The Great Mental Models: General Thinking Concepts is the first book in The Great Mental Models series designed to upgrade your thinking with the best, most useful and powerful tools so you always have the right one on hand. This volume details nine of the most versatile, all-purpose mental models you can use right away to improve your decision making, productivity, and how clearly you see the world. You will discover what forces govern the universe and how to focus your efforts so you can harness them to your advantage, rather than fight with them or worse yet— ignore them. Upgrade your mental toolbox and get the first volume today!

    So why do we know so little about how to get them right?Big, life-altering decisions matter so much more than the decisions we make every day, and they're also the most difficult: where to live, whom to marry, what to believe, whether to start a company, how to end a war. There's no one-size-fits-all approach for addressing these kinds of conundrums. Steven Johnson's classic Where Good Ideas Come From inspired creative people all over the world with new ways of thinking about innovation. In Farsighted, he uncovers powerful tools for honing the important skill of complex decision-making. While you can't model a once-in-a-lifetime choice, you can model the deliberative tactics of expert decision-makers. These experts aren't just the master strategists running major companies or negotiating high-level diplomacy. They're the novelists who draw out the complexity of their characters' inner lives, the city officials who secure long-term water supplies, and the scientists who reckon with future challenges most of us haven't even imagined. The smartest decision-makers don't go with their guts. Their success relies on having a future-oriented approach and the ability to consider all their options in a creative, productive way. Through compelling stories that reveal surprising insights, Johnson explains how we can most effectively approach the choices that can chart the course of a life, an organization, or a civilization. Farsighted will help you imagine your possible futures and appreciate the subtle intelligence of the choices that shaped our broader social history.

    With the sophisticated algorithms in this book, you can write smart programs to access interesting datasets from other web sites, collect data from users of your own applications, and analyze and understand the data once you've found it.Programming Collective Intelligence takes you into the world of machine learning and statistics, and explains how to draw conclusions about user experience, marketing, personal tastes, and human behavior in general -- all from information that you and others collect every day. Each algorithm is described clearly and concisely with code that can immediately be used on your web site, blog, Wiki, or specialized application. This book explains:Collaborative filtering techniques that enable online retailers to recommend products or media Methods of clustering to detect groups of similar items in a large dataset Search engine features -- crawlers, indexers, query engines, and the PageRank algorithm Optimization algorithms that search millions of possible solutions to a problem and choose the best one Bayesian filtering, used in spam filters for classifying documents based on word types and other features Using decision trees not only to make predictions, but to model the way decisions are made Predicting numerical values rather than classifications to build price models Support vector machines to match people in online dating sites Non-negative matrix factorization to find the independent features in a dataset Evolving intelligence for problem solving -- how a computer develops its skill by improving its own code the more it plays a game Each chapter includes exercises for extending the algorithms to make them more powerful. Go beyond simple database-backed applications and put the wealth of Internet data to work for you. "Bravo! I cannot think of a better way for a developer to first learn these algorithms and methods, nor can I think of a better way for me (an old AI dog) to reinvigorate my knowledge of the details."-- Dan Russell, Google "Toby's book does a great job of breaking down the complex subject matter of machine-learning algorithms into practical, easy-to-understand examples that can be directly applied to analysis of social interaction across the Web today. If I had this book two years ago, it would have saved precious time going down some fruitless paths."-- Tim Wolters, CTO, Collective Intellect