Skepsis #43: Metrics and Mating
Measuring what matters. Why dating is hard. Vaccines and misinformation.
Good metrics
Having both studied and applied many frameworks and methods for goal setting I’ve noticed that most boil down to just a few core principles. Among these is how to measure if what you're doing is working or not. That is, are you being effective?
What any management consultant worth his salt will tell you is that it's not enough just to set a measurable target for your goal, you should also define both a leading and a lagging indicator. This often goes by other names such as input and output metrics, objectives and key results, etc. You can think of it as a hierarchy with the outcome you want to achieve at the top and the behaviors that cause that at the bottom. The lagging indicator or metric measures the result or outcome you want to achieve, such as losing weight. The leading indicator is the input or the behavioral metrics that are most likely to lead to achieving the outcome, such as exercising frequently or counting calories.
The problem with most goal-setting, and arguably the reason why many people fail at achieving their goals, is that they only define an outcome or lag metric, if any. A lagging metric will not tell you if what you're doing is working because you get feedback too infrequently. If your goal is to increase your business unit's quarterly revenue and you have a sales target of $2.5M for the quarter, you won't know that you've reached the goal or made considerable progress until you've achieved it, or at the very least until you've closed a few deals. And when revenue is contingent on just a few sales deals, you may be spinning your wheels for weeks calling the wrong leads or not calling enough potential leads at all. Instead, it's often more fruitful to specify one or a few leading metrics that you can target on a daily basis, and that you can get feedback on from your manager or colleague. As a sales rep that could be the size or quality of the leads, the number of sales calls you make, and so on.
Put differently, lagging indicators describe the past. They are high-level metrics that describe results (or lack thereof). They are typically not actionable because they describe the consequences and only provide generic feedback, which means that they afford you less control.
Leading indicators, on the other hand, describe the future. They are actionable, give you more control and provide specific feedback since they describe the causes of the outcome.
The quarterly revenue target is a lagging indicator of the financial success of your business unit or company (during that quarter), once you can measure it it's already too late to do anything about it. The number of calls you make as a sales rep, however, is a leading indicator of how likely you are to reach the sales target. The latter is something you can directly control, can get actionable feedback on and it describes the future in the sense that it tells you something about how likely you are to reach your goal.
Setting rules instead of goals is another way of saying that you should specify and measure the behavior that you think will lead to your desire outcome.
This is not to say that you should do away with measurable targets or lag metrics, it's just that they are usually not enough in and of themselves. You're more likely to achieve your goals if you can figure out (through both practice and analysis) what the leading behaviors and metrics are for that goal's lagging metric.
But metrics are not a panacea. They can be gamed and lead to perverse incentives especially when people don't have skin in the game, or where rewards/punishments are tied to metrics without the proper checks and balances. What's more, quantitative metrics can lead to an impression of objectivity that they don't deserve. But the fact that metrics can be gamed or that they can be badly applied is not a reason to never use metrics. Whether or not you are successful in applying the right kind of metrics in an effective manner comes down to the culture of the organization in which they are applied, or how you apply them. As Scott Young put it in a recent blog post:
"Culture seems to lurk behind the success or failure of many management approaches in ways that can be difficult to reduce to a particular method or technique. In this sense, metrics are no different from Lean, Agile, Six-Sigma, or any of the other practices du jour. Metrics are a tool for healthy organizations, but get twisted when applied to sickly ones"
Mate Selection for Modernity
The mating strategies of men and women, as much as we like to think of ourselves as equals and as individuals with independent preferences making rational decisions, are heavily rooted in our evolutionary biology. (Yes, many people contest this.) It's not that culture doesn't matter, but that it gets shaped by the underlying stratum of our biology.
The evolutionary pressures exerted on male and female humans and their non-human ancestors over the past millions of years largely explain the modern behaviors around dating and mating preferences that we see in ourselves and in society. This is something Sam Harris and David Buss (Professor of Psychology at the University of Texas) discuss at length in a recent Making Sense podcast on the topic of human mating strategies.
For example, because males historically speaking couldn’t know for certain if they were the father of the children their female mates had given birth to, their sexual reproductive behavior has evolved to incentivize them to mate with as many females as possible. Conversely, due to the outsized physical, emotional and economic investment that females make when carrying a child and raising a newborn, they have evolved very selective preferences for picking mates that are likely to provide them with the necessary resources and safety.
To be clear, this is not to excuse cheating or other undesirable behaviors we see in society. Yet if we want to understand why those behaviors occur, we need to grapple with the stark biological asymmetry that exists between the two sexes.
Among other things, this asymmetry has resulted in something called hypergamy which is the preference for marrying or having sexual relations with a person of higher social status. This mating strategy is employed largely by women. From an evolutionary lens, high social status, education, physical strength, and money are leading indicators of being able to acquire resources and provide safety, which is what women - biologically speaking - look for in their mates. Of course, there are plenty of exceptions and deviations on the margins, but the biology of our mating preferences seems to correspond quite well to observed behaviors and stereotypes, nevertheless.
Nowhere is this as apparent as on Tinder.
In a sort of perverse twist of fate that is counter to the modern feminist ethos, hypergamy in combination with a shrinking cohort of high-status, well-educated men is leading to the mating strategies of men (hookup culture) being imposed on women:
"Premised on sex ratios, a surplus of women in education and economic groups caters to men’s desire for multiple partners. The relative rarity of men within these groups means that women, in competition with other females, are more likely to conform to the sexual strategy of males. In these environments, hookup culture is more prevalent. In contrast, environments in which men are numerous see more long-term relationships."
The reality of lower educational/societal achievements of a growing share of young men, and the progress and higher educational achievements of women is a combination of factors that could lead to a very unfavorable outcome.
“More generally, there’s a disconnect between what women want and what is actually available to them. Whereas greater male attainment increases the number of romantic options a man has, greater female attainment reduces the number of options a woman has.”
Not only can this lead to a backward slide in feminist progress, but a demographic disaster caused by an increasing group of low educated young single males with low to no prospects of finding a mate. We all know what that leads to.
How to work hard
In this essay, Paul Graham makes the case that hard work leads to great work. Hard work is not like banging your head against the wall, rather, effective hard work usually comes easy because it's driven by personal interest, your natural ability, lots of practice, and a concerted application of effort on top. Doing good work and working hard on something is done by avoiding the trivial work, the "busy work" that distracts from what really matters: real work.
"I do make some amount of effort to focus on important topics. Many problems have a hard core at the center, surrounded by easier stuff at the edges. Working hard means aiming toward the center to the extent you can. Some days you may not be able to; some days you'll only be able to work on the easier, peripheral stuff. But you should always be aiming as close to the center as you can without stalling."
Do lockdowns work?
Although we're probably going to need to wait a few years until the final judgment comes in (if ever), we can draw some conclusions already.
Various policies lumped together as “lockdowns” probably significantly decreased R. Full-blown stay-at home orders were less important than targeted policies like school closures and banning large gatherings. Talking about which ones were “good” or “bad” is an oversimplification compared to the more useful questions of when countries should have started vs. stopped each to be on some kind of Pareto frontier of lives saved vs. cost.
Sweden hasn't done that badly, but in the early phases of the pandemic clearer restrictions would have been desirable:
If Sweden had a stronger lockdown more like those of other European countries, it probably could have reduced its death rate by 50-80%, saving 2,500+ lives.
Why getting the COVID-19 vaccine should be a no-brainer
I assume most people reading this newsletter are the sort of people who have already been or will get vaccinated (voluntarily). But even among the crowd of people who are generally pro-science or pro-vaccine, there has been plenty of doubt and misinformation floating around. In this episode of the Making Sense podcast, Sam Harris talks to Eric Topol (Professor of Molecular Medicine) about vaccine hesitancy and related misinformation. Have the COVID mRNA vaccines been tested enough? Why haven't other viral treatments been used more? How effective are the vaccines against the delta variants? How worried should we be about potential side effects? As it turns out, the COVID-19 vaccines are probably some of the best and most well-studied vaccines we've ever produced.
How to make sense of wine labels
Learn how to read a wine label so you can buy the wines you like. No snobbery required.
Show me love
Show me love by Robyn is without a doubt one of my all-time favorite pop songs:
As always, stay safe out there.
/Phil