The asymptotic half-life of facts

Do you remember calculating half-lives in high school chemistry? A single atom of a radioactive substance decays at an unpredictable rate. You might get lucky and witness the decay immediately or  It might take as many as 10000 lifetimes--pick a comfortable chair. 

​But in aggregate we can make estimates based on the law of large numbers. Similar to scientific facts--we can calculate the point where half of the atoms (or knowledge around a specific topic or discipline) has decayed or according to complexity scientist Samuel Arbesman, "...measure the amount of time for half of a subject's knowledge to be overturned." 

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To understand the decay in the "truth" of a paper we can measure how long it takes for the citation of an average paper in a field to end. Whether it is no longer interesting no longer relevant or has been contradicted by new research this paper is no longer a part of the living scientific literature. It is out-of-date. The amount of time it takes for others to stop citing half of the literature in a field is also a half-life of sorts. 

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Sam brilliantly distinguishes facts based on their rate of change. There are fast-changing facts like weather, slow-changing facts like the number of continents (think Pangea) and somewhere in the middle--mesofacts. Mesofacts change as well but typically along the scale somewhere within the human lifetime.

Read his book, The Half-Life of Facts: Why Everything We Know Has an Expiration Date
for more depth, it is truly fascinating. For example the half-life of physics is ~ 13 years while math is churning away around 9 years.

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Why does this matter? Think of how accurate your calculation would be about the decay of a single atom of uranium. It is only when a large collection of atoms have accumulated that we can understand the rate of decay. The law of large numbers is quite simply a law of probabilities--or as the number of events or sample size grows--the difference between theoretical and actual approaches "0" or becomes closer to the average of a population.

In the field of Oncology--and beyond--these are critical points to remember. The problem with modern R&D and discovery is that we are heralding each unique uranium atom and betting on the outcome of the rate of decay. Early research is exploding in the media and investors are frenetic. What we know is incremental. Unless you work in science or clinical research you tend to hear the proclamations and the hype. The problem is each new hypothesis begins with an observation.

​It may be a fact today but what happens when replicated or expanded outside of clinically controlled environments? We once waited for data--for certainty or at least the probability of certainty. Now we want to rush discovery into "fact" status at the cost of accuracy or actual scientific methods. If you attended ASCO this year--you know what I mean. Investors want something to believe in and we are only too eager to provide fodder--even if it is quick to decay.

Here is a link to tweets on the problem with precision oncology. Vinay Prasad describes the cost of rushing to approve drugs where we lack sufficient facts to be certain that benefits outweigh risks.

If the topic fascinates you, here are two podcasts that fueled my curiosity. The Half Life of Facts and The Person You Become.