The Half-life of Facts: Why Everything We Know Has an Expiration Date Hardcover – Sep 27 2012
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—David A. Shaywitz, The Wall Street Journal
“The Half-Life of Facts is easily one of the best books of the year on science. It would be a lovely irony were it to prove one of the best books on politics, too.”
—Stephen L. Carter, Bloomberg
“Absorbing and approachable treatise on the nature of facts: what they are, how and why they change and how they sometimes don’t (despite being wrong)…Facts matter. But when they change—as they seem today to do with alarming frequency, we begin to lose that control. In his debut, Arbesman…advises us not to worry: While we can’t stop facts from changing, we can recognize that what we know ‘changes in understandable and systematic ways.’… With this, he introduces ‘scientometrics,’ the science of science. With scientometrics, we can measure the exponential growth of facts, how long it will take, exponentially, for knowledge in any field to be disproved—say, 45 years for medical knowledge…like a good college professor, Arbesman’s enthusiasm and humor maintains our interest in subjects many readers may not have encountered before…[The Half-Life of Facts] does what popular science should do—both engages and entertains.”
“How many chromosomes do we have? How high is Mount Everest? Is spinach as good for you as Popeye thought—and what scientific blunder led him to think so in the first place? The Half-life of Facts is fun and fascinating, filled with wide-ranging stories and subtle insights about how facts are born, dance their dance, and die. In today’s world, where knowledge often changes faster than we do, Samuel Arbesman’s new book is essential reading.”
—Steven Strogatz, professor of mathematics, Cornell University, and author of The Joy of X
“What does it mean to live in a world drowning in facts? Consider The Half-life of Facts the new go-to book on the evolution of science and technology.”
—Tyler Cowen, professor of economics, George Mason University, and author of An Economist Gets Lunch
“The Half-life of Facts is a rollicking intellectual journey. Samuel Arbesman shares his extensive knowledge with infectious enthusiasm and entertaining prose. Even if the facts around us are ever changing, the lessons and fun in this book will have a very long half-life!”
—Michael J. Mauboussin, chief investment strategist, Legg Mason Capital Management, and author of The Success Equation
“The Half-life of Facts teaches you that it is possible, in fact, to drink from a firehose. Samuel Arbesman, an extremely creative scientist and storyteller, explores the paradox that knowledge is tentative in particularly consistent ways. In his capable hands, we learn about everything from how medieval manuscripts resemble genetic code to what bacteria and computer chips have in common. This book unravels the mystery of how we come to know the truth—and how long we can be certain about it.”
—Nicholas A. Christakis, MD, PhD, coauthor of Connected
“Facts fall apart, some famously so. Brontosaurus is not a real dinosaur species; Pluto is not a planet. When you look at them en masse, patterns emerge: Facts die, and are born, at specific, predictable rates. These rates are the subject of applied mathematician Samuel Arbesman’s engaging, insightful jaunt across the backstage of scientific knowledge. Packed with interesting tidbits—for instance, more than a third of mammals thought to have gone extinct in the last 500 years have since reappeared—the book explains how facts spread and change over time. It also explores how today’s data-soaked reality has yielded high-throughput, automated ways to produce new truths, like algorithms that discover connections between genes and disease.”
—Veronique Greenwood, Discover magazine
“Knowledge shifts over time, explains Sam Arbesman in The Half-Life of Facts, and it does so in predictable ways. The book takes us on a whirlwind tour of emerging fields of scientometrics, and undertakes a broader exploration of metaknowledge. Arbesman details how researchers beginning to focus the big-data lens back on science itself are uncovering quantitative laws and regularities in the way that scientific knowledge is constructed and modified over time….Arbesman is a delightful guide to the territory, patently in love with this emerging field. He is also a skilled storyteller, and his wide-eyed reporting invigorates material that could have been dry and academic.”
—Carl Bergstrom, Nature magazine
About the Author
Samuel Arbesman is an applied mathematician and network scientist. He is a senior scholar at the Kauffman Foundation and a fellow at the Institute for Quantitative Social Science at Harvard University. His writing has appeared in The New York Times, The Atlantic, Wired, New Scientist, and The Boston Globe. He lives in Kansas City with his wife.
Top Customer Reviews
Unfortunately, I did find a couple of instances where the information presented was rather misleading at best. In particular, on page 104 (near the middle), it is stated that "Richard Feynman ... shared the Nobel Prize with another physicist, Sin-Itiro Tomonaga". In fact, Feynman shared the 1965 Nobel Prize with two other physicists, not just one: Tomonaga and Julian Schwinger. Also, near the middle of page 123, it is stated "... when dry ice becomes carbon dioxide...". In fact, dry ice is carbon dioxide in the solid state.
Notwithstanding these minor shortcomings, I found this book to be quite absorbing and I learned quite a bit from it. Although it should appeal to a fairly wide readership, I believe that scientists and science enthusiasts would likely enjoy it the most.
The half-life of facts per se is really only chapter one of the book, but throughout, Arbesman details the ways in which knowledge is gradually replaced. Rather than rely on the old Newton/Einstein paradigm shift so heavily used by philosopher and historian of science Thomas Kuhn, Arbesman gives hard stats on how different fields tend to discard half of their knowledge at different rates. Softer sciences like psychology tend to have shorter half-lives (~5 years) than harder sciences like physics (~50 years). Medicine and economics, if I recall correctly, tended to fall somewhat closer to the shorter end of the spectrum. Arbesman also talks a lot about trends in citations—for example, something like a third of all papers published are never cited—and how citation counts are used, for better or for worse, as a proxy for the confidence we have that a bit of knowledge is the Truth.
In short, Arbesman's The Half Life of Facts is a pretty solid knowledge return on time investment, covering a somewhat neglected subject in a very accessible manner. Four stars overall.
Most Helpful Customer Reviews on Amazon.com (beta)
And so, I was delighted to find Arbesman's book genuinely refreshing. He omits any discussion of Königsberg and the birthday paradox, which would have been off topic, and instead contributes a genuine thesis about the 'science of science' that is delightfully fresh. Many of his vignettes were entirely new to me: the coPub approach to discovering links between disparate domains of science, his review of Galton's more esoteric studies (apparently Galton was an early Scientometrician, the book discusses several great studies I'd never heard of), and the 'Bone Wars' that have shaped the public knowledge of dinosaurs.
So, I guess what I'm trying to say is that I'm a sucker for vignettes, and where many books fail to deliver me fresh ones, Arbesman's tour of scientometrics offered wonderful portions of fresh meat. Yes, there was Pluto, and a somewhat slow discussion of exponential growth, but I'll forgive that. This was a worthy read.
Note: I "read" this book as an audiobook while on a long drive. When I've recommended a book that I've audiobook'ed in the past, it has on occasion happened that people have found it slow in book form when I had no such opinion of the audiobook. This seems to be because it's easier to space out during a chapter while driving then to wade through a slow chapter of reading. I don't think that's the case here, but just a brief warning.
I think the biggest issue I had with it was the very myopic view applied to the topics. And the fact that I think that Mr Arbesman really makes too much of the methods he relies on to tell a story. Basically the book relies on the idea that you can graph anything that you can put a number on, and then using math that is complicated compared to, say what you learn in high school, you can fit a line to any graph and a lot of times that line is a particular family of curves. He makes it sound very magical but its not really - sometimes the fit is great and you can learn a lot from it but you can do this, like I said for anything. It doesn't per se, mean anything major. It isn't really even uncovering any secrets of how things are organized in nature or the world - we're fitting the lines after all.
Plus, when he talks about science he seems to ignore lots of factors that would make his "story" messier or just different. He talks a lot about citations of research papers but without seemingly understanding how people actually function in science. Finally, at the end, he has a chapter that promises to discuss the "human" aspects of knowledge generation but he doesn't really do that there either. What I mean is, he attributes the fact that few references in papers appear to have been actually read by the authors to laziness and doesn't talk at all about how social networks among scientists influence choice of citations (i.e. I cite what my boss cites, or even better, what he wrote) despite have a whole chapter on the social movement of information just earlier in the book! Lame, I say!
The Half-Life of Facts is easily understood by a lay person. I found it very readable and I don't have a head for science at all. Each chapter outlines a different reason why facts may either change or be found to be untrue. Arbesman uses examples throughout, all of which I found fascinating. I would love to read even more stories about which facts have changed over time and why.
I was surprised by some of the facts that are no longer true. For instance, did you know that there really isn't a dinosaur called a Brontosaurus? I had no idea and both of my boys have been through dinosaur obsessions within the past few years. The Brontosaurus was found to be a type of Apatosaurus over a hundred years ago. However, once something is out in the ether, it's really hard to circulate information modifying or correcting the original assertion.
I appreciated that not only does Arbesman discuss the various ways in which untruths persist and facts change over time, he also offers suggestions of how to keep current without getting information overload.
I love that in keeping with the spirit of The Half-Life of Facts, Arbesman's website has a Errata and Updates section for the book. There is already one case listed in which Arbesman unknowingly perpetuated a myth about how spinach became known to have a high iron content.
It's very rare that I read a non-fiction book that I have a hard time putting down. The Half-Life of Facts is one of those rare riveting works of non-fiction. I highly recommend it to all.
Arbesman cites a study where nearly 500 medical articles from the past 50 years were vetted by current experts, and a graph is shown which displays the time since publication on the x-axis and the percentage that stand up to scrutiny on the y-axis. The graph is ambiguous, showing a stair-step curve so that you can't tell where the actual data points are, but this is not the main problem. The graph clearly shows a curve which is accelerating downward; it is concave down. Arbesman infers from this that "they got a clear measurement for the half-life of facts in these fields by looking at where the curve crosses 50 percent on this chart: forty-five years." He goes on to say that this graph displays exponential decay. This is blatantly and hilariously wrong. An exponential decay curve has exactly the opposite shape, its rate of decay slows down, it is concave up. If the half-life were 45 years, then it would cross the 25 percent mark at 90 years. However, the graph shows it dropping to 25 percent at about 50 years. Even worse, this graph does not track a group of papers published at about the same time, displaying their time to be disproven or rendered obsolete. It takes papers published at different times and tested at one time, so there is no real valid comparison between them. An exponential decay curve will drop to half of its value in a certain amount of time, FOR EACH POINT ON THE CURVE, meaning, you need to measure this 45 years at multiple times and make sure it is consistent. Arbesman tries to infer such a curve from only one data point, and because of the problem with how the papers were selected, it's not even a true data point.
This tendency to draw grand conclusions from insufficient data pervades the text. On page 18 he states that Nobel laureates tend to give first authorship of their papers to their younger colleagues more frequently that other scientists. His immediate conclusion is that nicer people are more likely to win Nobel prizes. This is a fundamental error, similar to how false positives can be relatively frequent in drug tests compared to true positives, despite a high testing accuracy. The population of Nobel laureates is much smaller than the population of nice people who also happen to be scientists. The conclusion does not follow.
Another example: On page 53, he states that the average American lifespan is increasing, and that this rate of increase is itself increasing. Without blinking, he says: "If this acceleration continues, something curious will happen at a certain point. When we begin adding more than one year to the expected life span ... per year, we can effectively live forever." This of course does not follow. It is entirely conceivable that lifespan increases will tend to be heavily weighted towards younger people, i.e. medical technology may help a baby live to be 150, but not an 80-year-old man who didn't have that technology 80 years ago. Whether or not this is the case is irrelevant; Arbesman does not provide enough information to show it.
There are much simpler examples as well. On page 46, he says that the movement abilities of robots "have gone through about thirteen doublings in twenty-six years. That means that we have had a doubling about every two years: right on schedule and similar to Moore's Law." No, it doesn't. He has not given any indication that these doublings have been equally spaced.
His apparent lack of understanding of math, and inability to explain it clearly, also shows up throughout the book. On page 42, when describing Moore's Law of exponential increase, he says, "Processing power grows every year at a constant *rate* rather than by a constant amount." These are the same thing. A constant rate means growing by a constant amount. Of course he means that the rate itself is growing, proportional to the processing power (not at a constant rate, that would be quadratic growth instead of exponential), but his explanation is obfuscated.
A few pages later, he is describing how multiple logistic curves (curves that start out looking exponential but eventually approach a maximum, as in population growth) can be overlayed to produce an exponential looking curve. The point of this is to illustrate how successive technologies can contribute to a continued exponential growth in processing power or whatever. But the graph he includes shows three of these "S-curves" combining to form a linear trend, a straight line. He certainly does not state that the scale is logarithmic, in fact the axes are not even labelled. I really doubt that he understands the difference between constant, polynomial, and exponential growth.
On page 59, we have this gem: "A population's growth rate will increase in size proportionally to the current number of people. To be clear: This is much faster than exponential growth, the fastest growth rate we've considered so far. Exponential growth is a constant rate, and here the rate is growing, and growing along the speed at which the population increases." Now, this completely confused me until I figured out that he was actually talking about technological growth, not the actual growth of the population. But even so, the statement is absurd. Exponential growth is NOT a constant rate. (It's a constant proportion, making the rate grow, well, exponentially.) At this point I'm pretty sure that he's never taken an intro level calculus class.
I'm not just cherry-picking the worst examples. This stuff is on virtually every page. I started skimming heavily at around page 40, and still managed to find all these examples. The funniest one is on page 149: "By fitting the curve of Pluto's diminishing size to a bizarre mathematical function using the irrational number pi, they argued that Pluto would vanish in 1984." Bahaha, that must be a bizarre mathematical function indeed, to be using irrational numbers like pi. That kind of thing NEVER happens in math.
The book is also sloppy in many non-technical ways. Arbesman constantly touches on topics without ever finishing his thoughts, or refers back to something that he didn't actually talk about before. Around page 80, he is telling a story about a nasty rivalry between paleontologists Edward Cope and Othniel Marsh. He talks about Marsh discovering the brontosaurus and the apatosaurus, and then a bit later says, "Despite their vitriol and animosity, they actually didn't fight any more about the brontosaurus." But I don't know what he's talking about here, because he never mentioned them fighting about the brontosaurus to begin with.
On page 161: "Regarding a kerfuffle about the possibility of bacteria that can incorporate arsenic into their DNA backbone ... Carl Zimmer explains: 'But none of those critics had actually tried to replicate the initial results.'" He goes on to make points about replication and publishing negative results, etc. But what about the damn arsenic bacteria? What ever happened with them? I remember reading an article when that was discovered, and now I'm curious.
The most maddening case of this is on page 134: "Some famous problems go decades before being solved, and some, those that exist far out in the tail of the distribution, remain outstanding for hundreds of years. There was even a famous conjecture in the data set that took more than fifteen hundred years before it was eventually proven." WHAT WAS IT?!! The longest ones I can find, proven or not, are Fermat's Last Theorem, the Goldbach Conjecture, and Kepler's Conjecture. Seriously, if anybody knows the answer to this, comment with it please.
Probably the best parts of the book are when Arbesman is quoting or summarizing somebody else. There is actually quite a lot of this, and it's why it just barely gets 2 stars. But it's a shame that it's covered in crap, and I'll never see most of it. This book was a birthday gift, along with Nate Silver's The Signal and the Noise: Why So Many Predictions Fail -- but Some Don't. I let a friend borrow that while I was reading this one, and he said it was pretty good, so maybe go read that instead.
Almost nothing in Science is "Fact"! 99.99999999% of Scientific data supports a theory or theories through the support of evidence produced in various studies that may or may not have been designed properly to support the theory being tested. For example lack of precision in the use of the term "Fact" has led to the statement paraphrased as 'Global Warming and Climate Change Due to Rising CO2 Levels is Settled Science'(blatantly false as to the "settled science" conclusion). The whole thing is a theory that has been modeled and tested to some degree but certainly not to the degree of predictive certainty.
The problem in that situation is that the ability to precisely measure temperature and the necessary complete record keeping from the past allowing one to compare current temperatures to those 100-200 years ago or longer is just not available. Therefore those who study this area use proxies such as tree rings, etc. There are many variables affecting what produces our weather and climate that we know about, and likely thousands if not millions of ones we know nothing about. That makes any realistic projections based on atmospheric CO2 content simple extrapolations and calling it settled science is ludicrous.
That area of study is not the primary subject of this book, but the author uses the word "fact" when the more appropriate word/words would be "consensus belief", "knowledge" or "current theories" in the title and discussion would be more appropriate. The problem with using the word "Fact" is that the general public and some scientists associate "Fact" with "Truth" which is a major mistake. There are only a few absolute truths such as the value of Pi and a few other constants and very basic theorems.
The political class and politically motivated scientists use the word "fact" in such a sloppy way that it's meaning has been perverted. More alarmingly they are using such statements to force major laws and policies on the entire human population worldwide using "theories" stated as "facts".
The late Stephen J. Gould, PhD wrote many books and essays dealing with this subject as it applies to evolution and natural science. His writings document vividly how the "Facts" of the day were used for political purposes to the detriment of whole races of people, and in fact, set the stage for racial problems present in our country and worldwide among many other examples. The author may cite some of Dr. Gould's writings but in reading half the book I have yet to see any mention of his work.
Using the word "fact" with such a loose definition is very dangerous and increasingly so because of the internet and how easy it is for people to read selectively to support beliefs and not consider the many aspects of an area of study leading to an absolute belief when it is clearly not justified.
While I think this book is a good discussion of the evolution of knowledge and how "current theories" or "current consensus belief" evolves in a predictable pattern, I wish the word "Fact" was not used. The very subject of the evolution of "current consensus belief" to an entirely different place over time and the definition of a "half-life" for such things is proof that what is being discussed is not "Fact" but current theory at best.
I would recommend reading this book for the theory and discussion but would always stay conscious of the false use of the word "Fact" and the need to translate it's meaning to another less definitive word or phrase.
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