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Artificial Intuition

Artificial Intuition: Can we improve the quality of gut decisions by leveraging Machine Intelligence?


"All of my best decisions in business and in life have been made with heart, intuition, guts...not analysis.” -Jeff Bezos, 2018

As a company with analytics in its name, this is a bit concerning.

It’s sometime in the mid-2000s in Baghdad, Iraq. A U.S. Army unit awards a lucrative contract to a local Iraqi company that assists with sanitation and life support for a Forward Operating Base. Immediately, attacks on U.S. patrols in the local area spike.

During one of the unit leaders’ periodic meetings with local tribal leaders, the sheik mentions the increased violence and says:

“It’s business, not personal.”


A light bulb goes off...this group had connections to one of the losing bidders and the firm that won the contract was connected to a rival group. Instead of studying tactics, the organization should have been watching The Godfather.

Experienced Military intelligence officers would have intuitively (there’s that word) predicted the consequences of that decision. However, at the time, they lacked that experience; units spent as little as 12 months deployed, and a portion of that period was the handover between rotating units at the beginning and end of each tour.

This isn’t a dig against Military Intelligence but in order to successfully operate in today’s Volatile, Uncertain, Complex, and Ambiguous (VUCA) environments, groups need to fully grasp historical and cultural context. And in order to grasp this context, it takes experience. The longer you live in an unfamiliar ecosystem, the more you start to pick up these subtle clues and understand context.


This is not only true for the military but for any organization that wants to succeed in a complex world, like investors and businesses interested in entering Frontier and Emerging Markets. 

You need experience in order to succeed quicker and more effectively.

So back to decision making…

The debate about how to make better decisions goes back a long way. One of the earliest thought-leaders was Frank Knight, a professor at the University of Chicago’s School of Economics in the 1920s. He asserted that all important decisions need to follow a heuristic process because there are “too many unknowns and complexities.”

More recently in his best selling book, Thinking, Fast and Slow, Daniel Kahneman talks about the two types of decision making:


Kahneman’s research shows that effective decision makers need to rely on both System 1 and System 2 type thinking- and to know the difference between each. So how do decision makers find the balance between quick and effective in a VUCA environment? And if Kahneman is correct in that System 1 (think “gut”) thinking is error-prone, how does this explain Jeff Bezos’ belief that his best decisions have not been based on analysis?

Does Jeff Bezos always make the best decision based on his gut?

We submit they are both right. What Jeff Bezos is referring to as intuitive decisions are actually based on data...in a way...

Jeff Bezos has made so many decisions over the past 20+ years at Amazon (and in life) that this experience (and the consequences of previous decisions) is stored deep in his brain, improving the quality of his gut decisions. This would also apply to a military or a business leader who knows and understands a local ecosystem by being immersed in it for an extended period of time.

This 20+ years of decision making is something we’re calling the Experience Cycle. It’s vital for quality decision making, but it’s a slow process.


Can we speed up this Experience Cycle for leaders operating in complex environments?

We think we can by developing an approach that fuses machine intelligence with trusted human networks. This is our concept of Artificial Intuition.

In recent work, our team focused on the Ebola Crisis of 2014 in Liberia. The U.S. military deployed an Infantry Division headquarters a few months after the NGO community, the U.S. Government, and the Liberia Government had initiated steps to combat the epidemic. As might be imagined, there was now a new “big” player in the ecosystem and they stepped on a few toes and had a few missteps as they struggled to better understand the local ecosystem and more importantly, be effective.

They had to work their way through the Experience Cycle.

Imagine if those decision makers had access to an analytical platform that illustrated the local influence network, identified the most influential people and groups, and generated recommended courses of action? They could be effective from Day One.

This is what we set out to do.

Our team collected and processed open source data about the groups and key leaders on the ground operating prior to the deployment of the U.S. military. We then visited Liberia and spoke with decision-makers from both the Liberian Government and NGOs who were on the ground during that period.

Based upon this fusion of science and art, we discovered the granular linkages that caused certain groups and people to be connected and more importantly to be influential. 


Our team mapped and identified the linkages between the organizations responding to the crisis developed a multi-layer network model identifying the connections between the U.S. Government organizations, Liberian Government organizations, and NGOs responding to the Ebola Crisis.


What have we been doing since that time?

Based on the lessons learned from this initial effort, our team has incorporated additional layers into this concept. In a recent research project, we modeled a VUCA environment utilizing four layers: 1) The Influence Layer, 2)  The Political Goals Layer, 3) The Resource Network Layer, and the 4) The Geographic Layer.


The integration of the layers in this type of network model is the foundation of quicker more effective decision making in VUCA environments and shortening the Experience Cycle.

In summary, imagine any leader, not just a military leader, operating in an unfamiliar, complex environment. It could be investing in a new sector in a new region or it could be entering a new market in a new region. How do they quickly come up to speed and succeed? Since they don't have the “wisdom and experience” of multiple years in this new ecosystem, they can successfully leverage Artifical Intuition to speed up the Experience Cycle. 

Interested in learning more about this fusion of machine intelligence and trusted human networks can help you succeed in complex environments? Please drop us a line at info@stormkinganalytics.com


Intuition will tell the thinking mind where to look next. ~ Jonas Salk


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info@stormkinganalytics.com

508.687.0326

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