Crossing the epistemic divide

Bridging the gap

As the global knowledge economy continues to grow in speed and complexity, you need to think seriously about how to bridge the world of the algorithm and the world of intuition. One way to start is by laying out the key event chains leading up to a problem or challenge you or your organization is facing. Identify the root conditions underlying those events, along with the key decision points along the way. Then “flag” those points as being primarily algorithmic, intuitive, or a combination of both.

Have your team openly and honestly answer the following question: “Are you operating from within a tightly bounded epistemology caused by over-reducing a complex ecosystem down to a few select variables and control levers?” If so, you need to engage your brain trust’s innate human capacity for suspending belief, questioning assumptions, and looking for latent framing effects. Then start aggressively reframing not only the problem but how you think about the entire ecosystem in which you operate.

Thoroughly work through the reframed problem space conceptually, no matter how frightening the new representation might be. Then apply analytics to verify, and, if necessary, revise or refine.

In the 1990s, Russian Academy of Sciences mathematics professor Alexander A. Zenkin became one of the first researchers to systematize the process of crossing the epistemic gap (he called it “super-induction”). He started by graphically displaying large datasets in different colors, textures, and spatial orientations. Then he would let artists, musicians, poets, and other creative individuals look for interesting patterns and anomalies.

On a few occasions, they discovered connections and other insights that likely would not have been detected by mathematicians and statisticians running computer algorithms.

Yet, he intuitively sensed that his artistically minded subjects weren’t making anything close to the number of discoveries that were possible. So he did something that was both simple and dramatic. He started changing the number base of the dataset. Base 8, base 9, base 17, base 42, it didn’t matter. As he kept changing the base, new patterns and anomalies would be uncovered by the artists.

Fast-forward to today, and many of the Gulf States in the Middle East are using a combination of human creativity and data analytics to aid in making the transition from fossil fuel-based to knowledge-based economies. The same goes for innovations that are being applied in many of the smart cities that are popping up all across the globe.

If you want to perform basic planning tasks such as determining how to price your products based on hourly, daily, or seasonal changes in consumer behavior, then the purely analytical approach will work. But if you want to formulate broad-based, strategic alternatives to compete in a complex, fast-changing world, then you need to be looking out for bounded epistemology traps, and boldly redefine your market’s epistemology before someone else does.

And don’t think that artificial intelligence will solve the problem. Most, if not all, of today’s artificial systems are built upon tightly bounded epistemologies, which only serve to widen the gap. But knowing that such a gap exists is in itself a major step in the right direction.

You’ll likely encounter fierce resistance to the notion of reframing, especially if you’re working in an organization with firmly entrenched processes and protocols. If this happens, consider looking for past examples of when your organization or similar ones were blindsided by a sudden shift in the operating environment that wasn’t predicted by the data.

The good news is that humans have the unique capacity to bridge epistemic gaps. Unfortunately, it’s rarely used. As a KM leader, you have the opportunity to change all that.