Steve is very shy and withdrawn, invariably helpful but with little interest in people or in the world of reality. A meek and tidy soul, he has a need for order and structure, and a passion for detail.

Is Steve more likely to be a librarian or a farmer?

Before you proceed, pause for a moment and make your choice – A for librarian and B for farmer and also make a mental note for your reasoning. The point is not about finding the right answer but to understand your own decision making process.   

This above example is from Thinking Fast and Slow, by Daniel Kahneman. This is an interesting example of a heuristic bias. While making the decision we forget to consider the overall population of farmers and librarians. The description “a meek and tidy soul, he has a need for order and structure” flips the decision towards librarian for most people.  If you have chosen A as the answer then it is wrong but the main point is to understand why it is wrong?

When I came across this example few years back for the first time, I was blown away by its simplicity and realised  how and why we (humans) make these types of mistakes and keep repeating them. During the same time I was also revising Don Norman’s classic The Design of Everyday Things, I was working on a Process Architecture to define a lean-agile processes for my assignment with Jaguar.  I could see some common patterns around design as well as bias.

As technical people sometimes we are biased around some basic operations and we tend to forget some of the obvious things. The same things are difficult for non technical people or the end users of the system.  

Finally I came across Richard Thalers work on Nudge and choice architecture. It is a bridge between avoiding errors due to cognitive bias and human centric design approach.

Now coming back to the above example most people will vote for Steve as a librarian (option A), even a group people with good background in statistics.

Now let’s brush some basics — The classical definition of probability theory states that – probability of an event (likelihood of –  occurrence of an event) is the number of outcomes favorable to the event, divided by the total number of possible outcomes, where all outcomes are equally likely.

If you toss a coin, the probability of getting a heads is 0.50  (or 50%).

the number of outcomes favorable to the event  = 1 (getting a heads)

total number of possible outcomes  = 2 ( heads, or tails)

the probability of a getting heads = ½  (50%)

Now coming back to the above example, as per the occupational data, there are more than 20 male farmers for each male librarian in the United States. The ratio of farmers to librarian is 20:1 this translates to a huge difference of 0.95 and 0.05.  (95% and 5%). The likelihood of Steve being a farmer is much higher than he being a librarian.

Same example in visual form, if you have to pick at random in the first case both green and red have equal probability whereas in the second case the probability of picking green is higher.

This understanding is crucial in the era of Social media and mobile apps, just few incidents are enough to create a strong view against an individual and/or institution.

In the book Kahneman describes about System 1 and System 2 and explains how most human beings make decisions without being aware of the inherent bias.

System 1 operates automatically and quickly, with little or no effort and no sense of voluntary control.

System 2 allocates attention to the effortful mental activities that demand it, including complex computations.  The normal tendency is to take the path of least resistance.

The reason most people choose Steve as a farmer is it fits the mental model or stereotype of a librarian. For brain (using System 1) it is easy and faster.

In the next part the concept of Nudge and Choice Architecture will be covered.

Note:

Reference (Partial list)

Kahneman, Daniel. Thinking, Fast and Slow. Penguin Randon House, 2011.

Mithare, Raghavendra. “NUDGE – ROLE OF ECONOMICS IN ARCHITECTURE AND DESIGN.” presented at the Agile Tour London 2018 Conference, London, October 19, 2018.