The Half-Life Of Truth

Facts are facts, they are records or complexes of data. I think it’s our perception of the conclusions drawn from particular facts as true that decays over time. And unlike radioactive isotopes, sometimes particular truths will return or revive as a result of new evidence or reevaluation of the old evidence and data. I hope to read the book; in the meantime I assume from the reviews that this book applies statistical methods to scientific knowledge/facts. So it is subject to one major limitation of its own method: statistics do not apply to an individual event; to use them otherwise perpetuates the fallacy or flaw sometimes called “the gambler’s paradox,”  unfortunately too often done in medicine. Even gamblers’ paradox is a misnomer since a paradox is a contradiction that cannot be explained by the accepted science or logic; it’s really the gamblers’ error. It does not matter how many consecutive times the coin came up heads, unless the game is rigged the probability of the next time being tails is still one in two. A radioactive substance may have a half-life of 3 months. An atom of that substance may not have decayed after 1,000 years. Statistics give you probabilities about future behavior of collections or aggregates of objects/events based on and derived from analysis of past outcomes of similar (but necessarily not identical) populations and events. How good is the modeling and similarity?

From The Scientist

http://www.the-scientist.com/?articles.view/articleNo/32656/title/Capsule-Reviews/

The Half-Life of Facts: Why Everything We Know Has an Expiration Date

By Samuel Arbesman
Current, October 2012

Did you think a “fact” was something proven to be true? Behold its Pluto-like demotion to merely something we think is true—for now. “Facts” live and die, writes Samuel Arbesman in this nifty introduction to “scientometrics,” the ecology of knowledge; facts migrate through social networks, interbreed, evolve, and undergo mass extinctions. Surprisingly, the growth and turnover of knowledge plot the same predictable curves as bacteria in a petri dish or a radioisotope’s decay. But predictable doesn’t mean optimal: errors are perpetuated, effort is duplicated, and pieces of many a lifesaving puzzle lie buried in widely separated studies. Data mining and crowdsourcing are beginning to leverage computers to extract more value from what we don’t know we know.

Arriving just as a seemingly solid fact—random mutation—is shaken by the epigenetics earthquake, this book is well timed. And it’s full of fascinating tidbits: DNA-copying enzymes make the same mistakes as medieval scribes! Unfortunately, the book is marred by clumsy writing and careless editing, overlooking even the odd mathematical howler: included on a chart of technological capacities that double in mere months is “DNA sequencing, dollars per base [pair].” That’s stated backwards—it’s the bang, not the buck, that doubles.

The publisher’s promotional blurb:

Facts* change all the time. Smoking has gone from doctor recommended to deadly. We used to think the Earth was the center of the universe and that Pluto was a planet. For decades, we were convinced that the brontosaurus was a real dinosaur. In short, what we know about the world is constantly changing.
But it turns out there’s an order to the state of knowledge, an explanation for how we know what we know. Samuel Arbesman is an expert in the field of scientometrics—literally the science of science. Knowl­edge in most fields evolves systematically and predict­ably, and this evolution unfolds in a fascinating way that can have a powerful impact on our lives.
Doctors with a rough idea of when their knowl­edge is likely to expire can be better equipped to keep up with the latest research. Companies and govern­ments that understand how long new discoveries take to develop can improve decisions about allocating resources. And by tracing how and when language changes, each of us can better bridge gen­erational gaps in slang and dialect.
Just as we know that a chunk of uranium can break down in a measurable amount of time—a radioactive half-life—so too any given field’s change in knowledge can be measured concretely. We can know when facts in aggregate are obsolete, the rate at which new facts are created, and even how facts spread.
Arbesman takes us through a wide variety of fields, including those that change quickly, over the course of a few years, or over the span of centuries. He shows that much of what we know consists of “mesofacts”—facts that change at a middle timescale, often over a single human lifetime. Throughout, he of­fers intriguing examples about the face of knowledge: what English majors can learn from a statistical analysis of The Canterbury Tales, why it’s so hard to measure a mountain, and why so many parents still tell kids to eat their spinach because it’s rich in iron.
*I am not at all sorry to disagree. It is an error and misunderstanding to call scientific opinions or conclusions facts.
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One thought on “The Half-Life Of Truth

  1. Pingback: Red Hair & Freckles Alert! | Everything I Learned From My Sled Dogs And More!

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