What We’re Reading
Here are a few good articles and books the Collaborative Fund team came across this week.
Loyalty
Josh Brown writes about the only metric that matters for investment managers:
I’ve come to a realization about financial advisory firms that I think is an important one. You can boil down whether or not a financial advisor is adding value into a single metric, you might even say it’s the only metric that matters: Retention. Do clients stay?
Prosperity
Median full-time wages adjusted for inflation are at an all-time high after more or less stagnating for 15 years:
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Succession
Steve Blank writes about what happens when a crazy, visionary founder passes the torch to a groomed successor:
Once in charge, one of the first things these operations/execution CEOs do is to get rid of the chaos and turbulence in the organization. Execution CEOs value stability, process, and repeatable execution. That’s great for predictability, but it often starts a creative death spiral. Creative people start to leave, and other executors are put into more senior roles, hiring more process people, which in turn forces out the remaining creative talent. This culture shift ripples down from the top and what often felt like a company on a mission to change the world now feels like another job.
Sample size
In the era of big data, it turns out that taking a political poll is actually becoming considerably more difficult:
In 1997, 36% of households sampled agreed to participate in a poll, according to the Pew Research Center. Now it is 9%. This means thousands more calls must be made for a telephone survey to reach a sufficient sample.
Compounding the problem is that roughly half of households now have only cellphones, and a 1991 federal law prohibits calling mobile phones with an auto-dialer. To call these people, pollsters must dial all 10 digits by hand.
Assessment
This is a fascinating article that details, among other things, how harmful algorithmic employee evaluations can be:
In her illuminating new book, Weapons of Math Destruction, the data scientist Cathy O’Neil describes how companies, schools, and governments evaluate consumers, workers, and students based on ever more abundant data about their lives. She makes a convincing case that this reliance on algorithms has gone too far: Algorithms often fail to capture unquantifiable concepts such as workers’ motivation and care, and discriminate against the poor and others who can’t so easily game the metrics.
Basing decisions on impartial algorithms rather than subjective human appraisals would appear to prevent the incursion of favoritism, nepotism, and other biases. But as O’Neil thoughtfully observes, statistical models that measure performance have biases that arise from those of their creators. As a result, algorithms are often unfair and sometimes harmful. “Models are opinions embedded in mathematics,” she writes.
Book: The Gene by Siddhartha Mukherjee
The Gene is one of the most fascinating books I’ve read in years. In a way that any non-scientist can easily grasp, it details the history of our understanding of genetics in the past, what we know about genetics today, and what we’re likely to do with genetic manipulation in the future. The part on how we used to think genetics worked was, to me, the best part of the book, since it shows how massively wrong the smartest people in the world were 150 years ago. It makes me wonder what we’re currently wrong about but don’t yet realize. Mukherjee writes:
Three profoundly destabilizing scientific ideas ricochet through the twentieth century, trisecting it into three unequal parts: the atom, the byte, the gene. Each is foreshadowed by an earlier century, but dazzles into full prominence in the twentieth. Each begins its life as a rather abstract scientific concept, but grows to invade multiple human discourses— thereby transforming culture, society, politics, and language. But the most crucial parallel between the three ideas, by far, is conceptual: each represents the irreducible unit— the building block, the basic organizational unit— of a larger whole: the atom, of matter; the byte (or “bit”), of digitized information; the gene, of heredity and biological information.
Have a good weekend.