Learn a new word: Nomophobia

My son hassles me from time to time because I have, (again, from time to time), became irrationally worried when we are out and about and I notice the battery life on my mobile phone has dipped below, say 80% or so.

It could be because I travel a fair bit and rely on my mobile more than most people for essential functions, or that for that last 7 or 8 years I no longer have a working 'home phone' so my mobile is the only way to contact me. Or it could be that I need UP TO DATE scores on Knicks, Liverpool, and South Carolina Gamecock games.

Actually, that is probably it.

But all of us probably at one time or another felt the creeping anxiety or frustration or maybe ever fear that comes from being without our mobile phones - either from a dead battery, being in a place with no mobile/data service, or even one of the rare (and disappearing fast) places where mobile phone use is not permitted.

How long could you go, in a non-emergency, 'normal' life situation, without having access to a working, functional mobile phone? An hour? Maybe two? Maybe much less than that, if you are the kind of person who more or less runs your life and business and family stuff from your mobile phone.

Turns out this anxiety/fear of being out of mobile contact has a name, or at least a proposed name - Nomophobia.

From our pals at Wikipedia:

Nomophobia is a proposed name for the phobia of being out of cellular phone contact. The term, an abbreviation for "no-mobile-phone phobia", was coined during a 2008 study by the UK Post Office who commissioned YouGov, a UK-based research organization evaluating anxieties suffered by mobile phone users. The study found that nearly 53% of mobile phone users in Britain tend to be anxious when they "lose their mobile phone, run out of battery or credit, or have no network coverage".

I'd say since 2008, the year of the referenced study above, that the percentage of folks who would admit to being 'anxious' if they were without their working mobile phone would be much, much higher. Like everyone, I am thinking.

Why bring this up, this pretty obvious, 'We all are reliant on our mobile phones and we get really squirrely when we don't have them or they don't work' take?

Because there is at least some responsibility from workplace rules and norms, and associated workplace technologies that are contributing to this phenomenon.

The original research into the causes of nomophobia most often cited respondent's need to keep in touch wth and be available to friends and family as the prime driver of their anxiety during times when they had no mobile access. Today, for many if not most employees and even contingent workers, I would probably add "the need to be able to see, respond, and otherwise be accessible to 'work'" as another significant driver of nomophobia-type anxiety.

Sure, we need to be able to text our kids to find out where they are, when they need to be picked up, or when they are coming home. Not being able to perform that function is a real hassle, and can be anxiety-filled.

But I bet if you were honest with yourself, you would rank 'Missing an important email from the CEO/Boss/Client' almost as high on your list of nomophobia triggers.

Once any tool becomes a workplace tool, the folks who architect and design work and our relationships to the tools we use for work have at least some responsibility to ensure that these tools are used, well, responsibly.

It is probably worth a minute or two, before 2018 really gets going and you won't have time for this nonsense, to think a little bit more about what we expect, demand, and require from our teams and ourselves, when it comes to being 'always' accessible.

We have a lot to get nervous and anxious about without worrying about missing an email at 11PM on a Saturday.

Postscript- The Wikipedia piece on Nomophobia links to a 2012 research paper titled 'Mobile phone addiction in adolescence: The Test of Mobile Phone Dependence (TMD)', that includes a 12-question survey (way at the end of the paper), to test your own addiction to mobile technology. Worth a look if you suspect you might have a nomophobia problem. 

Learn a new word: Goodhart's Law

Happy 'First-day-of-the-rest-of-the-year'. I suppose every day is the first day of the rest of the year, but for some reason on the Tuesday following the long Labor Day weekend that feeling is much more acute.

Quick shot for your cram five days of work into four week. Another installment of your favorite series here on the blog - Learn a new word, where I share a word, term, phrase, or concept that I had not been familiar with previously, and for some reason seemed interesting/important/cool enough to share.

So here goes - and this one is especially for the 'You can't manage what you can't measure' types out there.

Our submission - Goodhart's Law

(from our pals at Wikipedia)

Goodhart's law is an adage named after economist Charles Goodhart, which has been phrased by Mary Strathern as: "When a measure becomes a target, it ceases to be a good measure." This follows from individuals trying to anticipate the effect of a policy and then taking actions which alter its outcome.

Actually Goodhart himself stated the 'law' just a little bit differently, theorizing that "When a measure becomes a metric, it ceases to be a good measure.”

Either way, the key point by Goodhart is still sound (and pretty obvious, even if you have never heard of our friend Goodhart).

Make a measurement - say time to fill open jobs, percent of new hires who stay longer than 6 months, or even number of new patents filed by the R&D department - doesn't matter, the sole or even a primary metric of success and evaluation and things tend to get a little strange.

The effected people pretty quickly learn how to manage/game the measurement, they start thinking, maybe too much, about how to drive that specific measurement in a manner that is positive for them, and they stop thinking so much about other, perhaps more cross-functional or strategic measurements.

And even worse, too many managers or leaders focusing too much on measurements can sometimes be an excuse for not exercising good judgement, a method that corporate bureaucrats use for CYA and holding on to territory, and an imperfect way to try and describe people and relationships in a numerical manner.

'You can't manage what you can't measure' is a fun thing to say. And sort of easy to agree with. But like Drucker's other widely quoted maxim, 'Culture eats strategy for breakfast' it definitely deserves more and closer scrutiny than it is typically given.

We manage the unmeasurable all the time. And reducing everything to something we can measure, to a number, is probably the fast path to inflexibility, failure to adapt, and a workforce conditioned to respond and behave to the movements of numbers on a spreadsheet.

Have a great week!

Learn a new word: The General Theory of Second Best

There's nothing I care more about that NBA basketball, (I promise this isn't another basketball post, but I may have to dig out a basketball analogy to make the point), with the possible exception of learning new things.

Which is why, I think, I run the 'Learn a new word' series on the blog. I am also falling into the trap of thinking 'if this is interesting to me, then it should be interesting to people who read this blog'. After 10 years of this, I am not really sure if that is even true. But I persist.

So here's today's 'Learn a new word' entry - The General Theory of Second Best.

What in the heck is that?

A decent description can be found in the Economist: (emphasis mine)

The theory of the second-best was first laid out in a 1956 paper titled, sensibly enough, "The General Theory of the Second Best", [paid access] by Richard Lipsey and Kelvin Lancaster. Roughly put, Lipsey and Lancaster pointed out that when it comes to the theoretical conditions for an optimal allocation of resources, the absence of any of the jointly necessary conditions does not imply that the next-best allocation is secured by the presence of all the other conditions. Rather, the second-best scenario may require that other of the necessary conditions for optimality also be absent—maybe even all of them. The second-best may look starkly different than the first best.

Let's think on that for a moment and take it back (sorry) to the basketball analogy I hinted at in the open.

The optimal allocation of resources for say a basketball team has traditionally consisted of five different kinds of players, with different body types, playing styles, and characteristics that when assembled, would provide the team with the right balance of scoring, passing, rebounding, and defensive play that would result in winning.

But let's say that the team can't acquire or develop one of the positions, let's say the point guard - the player who usually is charged with handling the ball, setting up his/her teammates for easy scores, and functioning as the on-court leader of the team. If this example team can't find a good enough point guard, the Theory of Second Best suggests that 'answer' to the problem isn't making sure the other four positions/roles are filled as designed and slotting in any old player as the point guard.

The theory suggests that the 'optimal' solution, when one resource (the point guard), is missing, may be to take a completely different approach to building the team. Maybe the team looks for more 'point guard' like skills in the other positions, or maybe the team implements a different style of offense entirely to mitigate the problem.

The real point is that once conditions appear that make the 'first best' strategy impossible to execute, that you may need to think really, really differently about what will constitute the 'second best' strategy. 

The second best may look starkly different than the first best.

I really dig that and hope you think about it too, once your plans in business or in life run into some challenges.

Learn a new word: Positive Predictive Value

Predictive analytics and the application of algorithms to help make 'people' decisions in organizations has been a subject of development and discussion for several years now. The most common applications of predictive tech in HR have been to assess and rank candidates for a given job opening, to estimate an individual employee's flight risk, and to attempt to identify those employees with high potential or are likely to become high performers.

These kinds of use cases and others and the technologies that enable them present HR and business leaders with new and really powerful tools and capabilities that can, if applied intelligently, provide a competitive edge realized from the better matching, hiring, and deploying of talent.

But you probably know all this, if you are reading an HR Tech blog anyway, and perhaps you are already applying predictive HR tech in your organization today. But there is another side or aspect of prediction algorithms that perhaps you have not considered, and I admit I have not really either - namely how often these predictive tools are wrong, and somewhat related, how we want to guide these tools to better understand how they can be wrong.

All that takes us to today's Learn a new word - 'Positive Predictive Value (PPV)'

From our pals at Wikipedie:

The positive and negative predictive values (PPV and NPV respectively) are the proportions of positive and negative results in statistics and diagnostic tests that are true positive and true negative results, respectively. The PPV and NPV describe the performance of a diagnostic test or other statistical measure. A high result can be interpreted as indicating the accuracy of such a statistic.

A good way to think about PPV and NPV is using the example of an algorithm called COMPAS which attempts to predict the likelihood that a convicted criminal is likely to become a repeat offender, and has been used in some instances by sentencing judges when considering how harshly or leniently to sentence a given criminal.

The strength of a tool like COMPAS is that when accurate, it can indicate to the judge to give a longer sentence to a convict that is highly likely to be a repeat offender, and perhaps be more lenient on an offender that the algorithm assesses to be less likely to repeat their crimes once released.

But the opposite, of course is also true. If COMPAS 'misses', and it sometimes does, then it can lead judges to give longer sentences to the wrong offenders, and shorter sentences to offenders who end up repeating their bad behaviors. And here is where PPV really comes into play.

Because algorithms like the ones used to create COMPAS, and perhaps the ones that your new HR technology uses to 'predict' the best candidates for a job opening, tend to be more or less wrong, (when they are wrong), in one direction. Either they generate too many 'matches', i.e., recommend too many candidates as likely 'good hires' for a role, including some who really are not good matches at all. Or they produce too many false negatives, i.e. they screen out too many candidates, including some that would indeed be good fits for the role and good hires.

Back to our Learn a new word - Positive Predictive Value. A high PPV result for the candidate matching algorithm indicates that a high number of the positives, or matches, are indeed matches. In other words, there are not many 'bad matches' and you can in theory trust the algorithm to help guide your hiring decisions. But, and this can be a big but, a high PPV can often produce a high negative predictive value, or NPV.

The logic is fairly straightforward. If the algorithm is tuned to ensure that any positives are highly likely to truly be positives, then fewer positives will be generated, and more of the negatives, (the candidates you never call or interview), may have indeed been actual positives, or good candidates after all.

Whether it is a predictive tool that the judge may use when sentencing, or one your hiring managers may use when deciding who to interview, it is important to keep this balance between false positives and incorrect negatives in mind.

Think of it this way - would you rather have a few more candidates than you may need get screened 'in' to the process, or have a few that should be 'in' get screened 'out', because you want the PPV to be as high as possible?

There are good arguments for both sides I think, but the more important point is that we think about the problem in the first place. And that we push back on any provider of predictive HR technology to talk about their approach to PPV and NPV when they design their systems.

Learn a new word: The Optimal Stopping Problem

I caught an interview over the weekend with one of the authors of Algorithms to Live By (can't recall which of the two co-authors I heard, but it doesn't matter. Kind of like it doesn't matter which of the two guys in Daft Punk plays a particular instrument on any given track. But that is another story.), and wanted to share a new word I learned from the interview that has some relevance to HR/Recruiting.

For this installment of Learn a new word I submit The Optimal Stopping Problem.

From our pals at Wikipedia:

In mathematics, the theory of optimal stopping or early stopping is concerned with the problem of choosing a time to take a particular action, in order to maximise an expected reward or minimise an expected cost. Optimal stopping problems can be found in areas of statistics, economics, and mathematical finance (related to the pricing of American options). A key example of an optimal stopping problem is the secretary problem. Optimal stopping problems can often be written in the form of a Bellman equation, and are therefore often solved using dynamic programming.

I bolded the 'secretary problem' which, despite its dated-sounding kind of name, is the example most commonly cited when discussing optimal stopping, and as luck would have it, is directly tied to HR/Recruiting.

The secretary problem is essentially, the question of 'Given X number of job candidates for a given position, and also given you have to make a 'hire/decline' decision on each candidate before moving to the next one, how many candidates do you need to interview in order to maximize your probability of identifying the best candidate, while minimizing the risk of making a 'bad' hire, (say by waiting too long, rejecting too many candidates, and having to settle for a candidate that is left).

Let's say you have 10 candidates for a position. You probably wouldn’t offer the job to the first candidate you interview, because you have no idea how that candidate compares to anyone else, or the general caliber of the candidates overall . But you probably don't want to wait until the 10th candidate, because if they’re the only one left you’re going to be forced to offer them the job (or keep it unfilled), regardless of how strong a candidate they are. Somewhere in the middle of the process there must be an ideal place to stop interviewing more candidates just to see what they’re like, and make a selection. But where to stop?

Enter the Optimal Stopping Problem. You can dig into the math here, but it turns out there is an ideal place to stop interviewing candidates, (or dating different people in order to try and choose who to marry), and it's after you have interviewed (or dated), 37% of the contenders. After you get to 37%, make a note of the 'best' candidate you have seen so far, (let's call her Mary Jane). Then, continue interviewing candidates and when you find the first one that is 'better" than Mary Jane, stop all further interviews and immediately offer that person the job.

How it works is related to the math behind estimating where the best candidate could be in the lineup. This number, expressed as 1/e, where 1/e eventually approaches 0.368, or about 37%. By analyzing the possible distribution of talent, it also turns out that if you interview the first 37 percent of candidates then pick the next one who is better than all the people you’ve interviewed so far, you have a 37 percent chance of getting the best candidate. 

It's a really interesting way of looking at the hiring decision making process, (as well as other processes that involve trying to make the 'best' choice amongst a number of alternative). But it makes sense somehow, even if only on an anecdotal level.

How many times have you slogged endlessly through an interview process where after some point candidate after candidate seem the same, and certainly no better than one you saw two weeks ago?

Or how many of us have, (maybe even privately), thought about a past boyfriend or girlfriend that 'got away' and for some reason has never been eclipsed by the series of people that you have subsequently dated?

Knowing when to stop, and understanding the probability that you have seen the best, or close enough to it, in any decision process is an enormously valuable thing.

In the secretary problem, and in probably a bunch of other problems too, the answer seems pretty clear - once you hit 37% you have seen enough, you won't learn much if anything else useful, and you know how to make your decision.

It is easy to apply in a job vacancy with 10 candidates. 

It is a little tougher to estimate just how many people you are willing/able to date in order to know when to apply the 37% cutoff.

Have a great week!