Human decision-making traps

The human brain is a poor decision-making tool. Through evolution, the human brain became very good at pattern recognition and extrapolation: from “That looks just a harmless log behind that patch of grass” to “Yum, that looks like an antelope!” to “YIKES, that’s actually a saber-toothed tiger.” Human survival depended upon our ability to recognize patterns and making quick, survival decisions based on those patterns.

Figure 5.1: Humans have become good at recognizing patterns

While great at pattern recognition, unfortunately, humans are lousy number crunchers. Because of our instinctive poor number crunching capabilities, humans depended upon heuristics, gut feel, rules of thumb, anecdotal information, and intuition as decision-making tools. But these decision models are insufficient in a real-time world where the volume, variety, and velocity of data are exploding. One only needs to travel to Las Vegas and...

The danger of making decisions based on averages

Another less explored human decision-making trap is the tendency to rely upon averages to help us make more informed decisions. If you make decisions based upon averages, at best, you’ll get average results. Beware of making decisions based on averages because one can drown in a river with an average depth of only 6 inches.The challenge with making decisions based on averages is that no one is average. For example, the United States Air Force pilots were struggling to effectively command their fighter jets in the 1950s. The problem was that the cockpit had a standard design based on the 1920s average pilot. The Air Force decided to update their measurement of the average pilot and adjust the cockpit design accordingly^[3].Air Force Lieutenant Gilbert Daniels measured more than 4,000 pilots across 10 size dimensions to create an updated standard cockpit design. The air force had assumed that most pilots would fall within average across...

Game-changing ramifications of nanoeconomics

Nanoeconomics is a term I have coined to describe the economic theory of leveraging AI to uncover individual human and device predicted behavioral and performance propensities (insights) that are buried in the organization’s customer engagement and operational management data.From these human and device predicted behavioral and performance propensities, organizations can make precision decisions to optimize the organization’s key business and operational use cases, such as predicting which customers are likely to stop using your products or services, which patients are likely to catch a staph infection, which first-year college students are likely to flunk out, which truck drivers are likely to have a car accident, or which worker is likely to retire (Figure 5.3).

Figure 5.3: Nanoeconomics: Transitioning Decision-Making from Averages to Propensities

Exploring decision-making strategies

Now that we understand the different challenges that we humans face in making rational decisions – human decision-making traps and the illusion of averages – we now need a simple, pragmatic decision-making framework that we can use to leverage data and analytics to make more informed decisions.

Informed Decision-Making Framework

Whether we know it or not, everyone creates a model to guide their decisions. Humans naturally develop models to support their decisions, whether it’s decisions about deciding what route to take home from work, what to pick up at the grocery store, or how to pitch to a power baseball hitter like Mike Trout. The comprehensive nature of one’s decision model depends upon the importance of the decision and the costs associated with making a wrong decision. For simplicity reasons, we’ll classify decisions as either low impact or high impact depending upon the costs and ramifications of making...

Critical thinking in decision-making

Critical thinking is the rational and objective analysis, exploration, and evaluation of an issue or subject to form a viable and justifiable judgment. However, to truly understand critical thinking, I believe that one must first understand objectivity.Objectivity, at its core, is the foundation for making intelligent and well-informed decisions. When engaging in the decision-making process, it is crucial to approach the decision-making process with an open mind, free from preconceived notions. If you already have a predetermined decision in mind, you run the risk of selectively seeking data that confirms your position and disregard information that contradicts your beliefs. Consequently, prioritizing objectivity and consciously avoiding personal biases are essential for mastering critical thinking and this informed decision-making.As discussed in Chapter 1, organizations with various missions gather your personal data to influence your decisions....

Summary

Everything about AI and data literacy comes to fruition in helping us make more rational and informed decisions. Decisions represent the crucial junction where theory and practical application merge, ultimately benefiting society as a whole. By harnessing the power of AI and understanding how to effectively interpret and utilize data, we empower ourselves and society to make choices that are grounded in reason and knowledge, leading to positive outcomes for all.And while not every decision will require a formal decision matrix process, you’d be surprised how quickly one can develop informed decision-making as a muscle memory. And in a world where organizations are trying to influence your behaviors, beliefs, and actions through half-truths, white lies, fake news, and alternative facts, that’s an invaluable skill.In the next chapter, we will delve into the fundamental statistical concepts that are essential for individuals and society to develop informed decision...

References

1. Sensitivity Analysis: https://www.investopedia.com/terms/s/sensitivityanalysis.asp
2. Monte Carlo Technique: https://www.investopedia.com/terms/m/montecarlosimulation.asp
3. Toronto Star, When U.S. air force discovered the flaw of averages: https://www.thestar.com/news/insight/2016/01/16/when-us-air-force-discovered-the-flaw-of-averages.html
4. Harvard Ed Magazine, Beyond Average by Todd Ross: https://www.gse.harvard.edu/news/ed/15/08/beyond-average

References

1. Scribbr. Rebecca Bevans, Understanding Confidence Intervals | Easy Examples & Formulas: https://www.scribbr.com/statistics/confidence-interval/
2. FiveThirtyEight. Nate Silver, Today’s Polls and Final Election Projection: Obama 349, McCain 189: https://fivethirtyeight.com/features/todays-polls-and-final-election/
3. SHRM. Dinah Wisenberg Brin, Employers Embrace Artificial Intelligence for HR: https://www.shrm.org/ResourcesAndTools/hr-topics/global-hr/Pages/Employers-Embrace-Artificial-Intelligence-for-HR.aspx

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