I used to assume that math professors were at their blackboards engaged in esoteric work with no relevance to HR. Much to my surprise, this is far from the truth. There are at least a couple of fundamental advances in mathematical theory that are likely to make their way from the blackboard to your office in the next five to ten years.
Advance One: Causal Modeling
Machine learning is often seen as a form of statistical modeling to predict outcomes. In HR, we use it to predict who might quit (known as “flight risk”). The trouble with traditional statistics is that it’s primarily focused on correlation, and, as we all know, correlation does not imply causation. What I wasn’t aware of was how traditional statistics struggled with applying the concept of causation. They could warn us about not over-interpreting correlation, but they lacked a robust approach to causation.
Thanks to the work of Dr. Judea Pearl and others, statisticians now have the tools needed to handle causal models and integrate them with machine learning. It’s essentially a new branch of mathematics that can help us better understand the complex issues we face in HR.
For example, if we are interested in the factors leading to better innovation, it’s clear that a complex mix of causal factors is at play. Machine learning combined with causal modeling may offer better insight than any of the tools we’ve had in the past. We can potentially build better models than ever before to see how factors such as team size, incentives, and leadership style interact to get the outcomes we want.
Advance Two: Mathematical Modeling of Management Concepts
In HR, we have an intuitive understanding of concepts like “engagement.” To the extent that there is a mathematical model of engagement, it is just that engagement is the average of the answers to a dozen or so questions. While putting a number on engagement is helpful, that number only captures a fraction of the nuanced concept of engagement.
Can we approach the subtle concepts we use in HR in a more mathematically rigorous way? The answer is yes, and, like causal modeling, it has a close link to AI. Machine-learning models can take vast amounts of data related to a concept like engagement and create a mathematical representation of it in what’s called “latent space.” The representation will have a lot of detail about the different aspects of engagement. It will be a big step beyond saying, “Engagement is 6.2.”
You will start hearing terms like “graph databases,” “hypergraphs,” or “topological data modeling” that help deal with these more complex mathematical representations. As an HR professional, you may never need to understand these topics in detail, but, one day, they will underpin tools that can provide a better understanding of what is going on in your organization’s culture.
Mathematical Models and HR: The Implications
Causal modeling will enable more effective organizational interventions, as we will be able to better predict which ones are likely to work. Perhaps even more importantly, we can track whether the intervention is proceeding as the model suggests and adjust accordingly. If the model says that intervening on A will lead to increases in B, which will ultimately improve C, but measures of B aren’t rising, then we don’t have to wait until the project is over to discover it’s not working.
The implications of mathematical modeling of management concepts are a bit more challenging to foresee. A simplistic view is that we’ll have a far more rigorous way of defining a concept like engagement, and, thanks to that comprehension, we’ll be able to manage it more effectively. The challenge will be dealing with mathematical definitions of concepts that are so complicated that they are hard to grasp, and we’ll have to learn to trust the model based on experience using it.
The Bottom Line
Tucked away in universities, mathematicians are conducting research that may affect HR in ways we never expected. Frankly, HR hasn’t changed much in 40 years, but that stability may be challenged in the years ahead. The new advances in mathematics just might revolutionize the way we approach HR, making this an exciting time for professionals in the field.
Will New Mathematics Revolutionize HR?
