recruiting, hiring and the monte carlo simulation


“… in the basement of your organization is a diesel-powered database that has a geological strata of decades old data.”

Jarred McGinnis about people in a company


I have a dubious relationship with the HR function in business. That said. This recruiting and hiring observation <rant> was inspired by two Economist articles separated by almost 3 years:

–          Big data and hiring – Robot recruiters: How software helps firms hire workers more efficiently <Apr 6th 2013>

–          In search of serendipity: Success in business increasingly depends on chance encounters <Jul 22nd 2010>

Rereading the articles ultimately drove me to remind everyone of The Monte Carlo simulation model.

First. A reminder of what the Monte Carlo simulation is:

The simulation works by running multiple trials based on random sampling to determine an outcome … using a combination of probability calculation and statistics. For example … if you roll dice you know you will roll either a 1, 2, 3, 4, 5 or 6 … but you don’t know which you will actually get on any given roll.

Aw. Geez. Forget the explanation, the simulation suggests that random is random and models can only approximate reality.

Second. The first article on models and simulation and hiring and ‘robot recruiters.’

The ‘big data and hiring’ article focuses on how software helps firms hire workers more efficiently. It begins by saying:‘

THE problem with human-resource managers is that they are human. They have biases; they make mistakes. But with better tools, they can make better hiring decisions. Software that crunches piles of information can spot things that may not be apparent to the naked eye.’

Here is an example the article highlights:

‘For instance, people who fill out online job applications using browsers that did not come with the computer (such as Microsoft’s Internet Explorer on a Windows PC) but had to be deliberately installed (like Firefox or Google’s Chrome) perform better and change jobs less often. It could just be coincidence, but some analysts think that people who bother to install a new browser may be the sort who take the drowning in informationtime to reach informed decisions. Such people should be better employees. Evolv, a company that monitors recruitment and workplace data, pored over nearly 3m data points from more than 30,000 employees to find this nugget.’


First. While I absolutely understand the intent to find ‘variables that reflect actually desired behavior’, this example is absurd and reflects the absurdity of this idea. In other words. I am not interested in hiring someone who has made a time utility decision in that their existing browser is good enough to do what is needed to be done <and does nothing with changing the browser> and instead has invested time writing a logical article disproving the theory of relativity on their blog <showing an ability to think and communicate>.

One word. Crazy. Nuts. Absurd. <choose one or all>.

Second. Your quote of the day: THE problem with human-resource managers is that they are human” <as if that is a bad thing>.

Third <lastly>. Ok. I admit I was extremely cynical with regard to what the article was suggesting <I may have even scoffed out loud … sorry … just wanted to type ‘scoff’ today>.

But. As I read the close of the article, ‘algorithms and big data are powerful tools. Wisely used, they can help match the right people with the right jobs’, I sagely nodded <to myself> and set aside my cynicism and doubts and filed this article and thought in my “I think this is bullshit and stupid and I do not believe any company could ever recruit or hire effectively this way, but I bet I end up working with some company who does passionately believe this crap” file.

Then. The third thing. The other article In search of serendipity: Success in business increasingly depends on chance encounters.’monte carlo simulation

The article succinctly states the recruiting & hiring dilemma:

‘When knowledge is dispersed, you are less likely to find what you want via a formal search. You may not even know what you are looking for. But you are more likely than you were in the past to discover something useful through a chance encounter.’

One thought when I read that … “amen brother.”

The article focuses on a book <which I have not read> by three guys <John Hagel, John Seely Brown and Lang Davison> called “The Power of Pull: How Small Moves, Smartly Made, Can Set Big Things In Motion”. Basically they suggest that business success can often be defined by excelling in “managing serendipity.”

 The Economist article highlights their thinking by saying:

By mingling with many strangers you can find that you often bump into people who can not only give you valuable information. The authors’ core argument is surely right: today’s technology, especially the internet, is undermining the old top-down approach to business, which the authors call “push”, by giving individuals more power to shape their working lives. There are huge opportunities available to people who can figure out how to use the “power of pull”, a term the authors define as “the ability to draw out people and resources as needed to address opportunities and challenges.”  They propose a three-pronged pulling strategy. First, approach the right people (they call this “access”). Second, get the right people to approach you (attraction). Finally, use these relationships to do things better and faster (achievement). The authors argue that change is both faster and less predictable than before, making traditional top-down planning trickier. And because things change so fast, knowledge is increasingly dispersed. Instead of drawing on a few, stable, reliable sources of information, managers today must tap into multiple, fast-moving, informal knowledge flows.

Therefore you need to manage the ‘serendipity funnel.’

Look. I think ‘managing the serendipity funnel’ is slightly absurd, but I get the point.

<note: you may have a serendipity funnel in your kitchen drawer>


When knowledge is dispersed, you are less likely to find what you want via a formal search. You may not even know what you are looking for. But you are more likely than you were in the past to discover something useful through a chance encounter.

I actually, firmly, believe in what they refer to as serendipity when it comes to recruiting and hiring people. And I do absolutely agree that the business world is not only moving faster, but is also less predictable – that includes change. And, because this post is about recruiting and hiring, I will absolutely acknowledge that within all the parts and cogs in an organization engine some are non-moving parts <steady nuts & bolts ‘replcation-based roles’ positions> and some are moving parts <translation: some have to be more adept at change then others> .

But permit me to note that even the non-moving parts need to adjust as the moving parts adjust. Now. I am sure that someone is going to send me some very very smart email suggesting that data can isolate some behavioral traits which can assist in recruiting and hiring. Well, sure, but no. Sorry. Not buying it.

Because in addition to the above absurd example I will share an additional thought: data cannot assess how anyone has been managed.

Garbage in and garbage out is what I say. Employees are almost as much about how they are managed as much as what they are capable of doing <and how they have behaved in the past>. A crappy manager can undermine the most capable of employees. I know of no algorithm which can isolate that.

dunce i will be goodBy the way. That is another example of serendipity – employees finding the right managers to maximize their abilities and potential. So include in that random equation the manager and the manager style.

In the end.

‘You may not even know what you are looking for.’

You may think you do, you may even say you do, and some manual or process may outline what you need to, but in the end discovery is often found in the encounter.

Look. We would all like to be able to predict aspects of randomness. Hiring and recruiting is certainly one.

I am fairly certain I wasn’t a particularly good hirer. I asked all the right questions <when I thought about it>, but I actually ended up with only one filter when hiring for my own groups <and some key senior positions when in that role>: some aspect of contrarianism, as in, did they have a contrarian muscle? Did they constantly challenge with why’s and how come’s and challenge the status quo? I liked people with an uneasiness for the status quo. It made it challenging as they challenged everything <me included>, but I found that was the character trait that fit with my managing style as well as however we went about business.

Well. How inexact is that? Sorry. Recruiting and hiring can be maddeningly inexact.

In fact, that is why I was inspired to remind everyone of the Monte Carlo simulation. Hiring and recruiting is just like Monte Carlo. I can recruit and hire people insuring they will deliver a 1,2,3,4,5 or 6 <I don’t really need big data to tell me how to do this>, but in the end I cannot be sure what I get when the dice actually get rolled.

Ultimately there is certainly a randomness in aligning right people <chemistry and capability> at the right time <opportunity actually exists> in the right place <the odds of having disparate talents in one place and actually meeting each other is fairly random>.

<note: I wrote about this in my 2010 Right People, Right Place, Right Time>

Sure. Algorithms and big data are the new powerful tools in the recruiting and hiring game. But once again I purposefully use ‘game’ because, well, Monte Carlo — you are using data to assess a gamble.

‘Wisely used, they can help match the right people with the right jobs.’

But setting up individuals in individual jobs doesn’t help me shit <any> in terms of team dynamics and efficiency.

Sure. I could pull out another excellent model to show how to help with team dynamics <it’s called the Team Model which I encourage every leader to at least think about in recruiting & hiring — it suggests that real strength lies in differences not in similarities, but that is a different post>.


Here is what I do know <part 1>: Models do not represent reality. They can only approximate reality.

The corollary. All this ‘big data’ mumbo jumbo shouldn’t absolve leaders of recruiting and hiring responsibility. Data is numbers and statistics. People are … well … people. Imperfect beautiful maddening employees.

Here is what I do know <part 2>:  Ultimately, in today’s business world to be a successful manager, you have to distribute power. And especially so with employees who are most exposed to potential change <and the need to adapt within boundaries>. Oh. That means you have to hire more people who are unpredictably predictable <and that is almost impossible to behavior model>.

Data is good, but even that doesn’t minimize risk & uncertainty in any truly meaningful amount. Serendipity is good, but counting on serendipity is like walking into the desert without a map expecting an oasis to appear just when you are thirsty. Learn to balance serendipity and data.

That said. Two words with regard to recruiting & hiring.

Monte Carlo hiring nirvana

Monte Carlo hiring nirvana

Monte Carlo.

Learn to live in Monte Carlo.

For non gamblers that makes Life extremely uncomfortable. Unfortunately recruiting and hiring is sometimes very uncomfortably random.


Enjoy your wins.

Recognize your losses are part of the game.

And understand that most of the other people playing the game are winning and losing just as often as you are.


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Written by Bruce