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| Reason and feeling in business |

This article appeared in June 2017, in Romanian, in BIZ Magazine

It’s a universally accepted truth that a CEO must make decisions using his rationality. But in reality, most leaders base their decisions on instinct or mental shortcuts. Is it good? Is it bad?

One study[i] (2016) found that, in solving puzzles and riddles, those who responded instinctively performed better than those who used analytical thinking.

Harry Markowitz received the 1990 Nobel Prize in Economics for laying the foundations of what is called the MPT (Modern Portfolio Theory). Markowitz’s theory evaluates investment funds based on a mathematical technique he invented, called mean-variance analysis, a model that calculates the risk and profit of an asset.

When he retired, Harry Markowitz decided to put his savings in a few funds. However, he didn’t use his own model to calculate how much to allocate to each fund. No! He allocated exactly the same amount to each fund, arguing that sometimes complex calculations have a fairly large margin of error, so it’s best to keep it simple.

Is instinct more effective than analytical thinking? Has a Nobel laureate ignored his own complex algorithm in favor of a simplistic, childish method? Well, then it’s clear that we have to throw away all this obsession with decisions based on reason, numbers and calculations and put instinct and feelings in the forefront, right?

In fact, it’s not really like that!

First things first. The classical economic theory is based on the model of the perfectly rational agent. In a market which has a constant offer, an increase in demand will automatically lead to an increase in price following a yield curve. Yes, but only if it’s assumed that all market players are perfectly rational agents.

These agents, whether we are talking about consumers, producers or distributors, are supposed to have immediate access to all the information on the market (they know immediately all the prices), to have plenty of time to do all their calculations and aim to maximize their profits. But reality shows us that this is not the case at all.

People prefer to do business with partners they know, not with anyone who comes with a better price. People get stuck when they have too many options, this is called The Paradox of Choice. People don’t necessarily choose the best product, but a product that they’ve bought before or a product they saw at the neighbors. No one has the time or patience to compare all the prices and read all the labels.

In short, reality looks different from the classical economic theory. So, a new theory began to emerge in the late 1970s, a theory that says we often behave irrationally (and therefore wrong) systematically. Daniel Kahneman (another Nobel laureate) and Amos Tversky defined these thinking traps with the term “cognitive biases”, and subsequent research gave rise to a new science, the Behavioral Economics.

This explains and predicts economic reality not as it would rationally happen, but as it really does. This new theory is very popular, with nice evangelists like Dan Ariely and very funny empirical examples, perfect to tell while having a beer (that’s why MBA students are socially successful, because they can tell their friends in the evening some kind of real anecdotes they learn in the morning at the Critical Thinking class).

Let’s recap: classical theory assumes that we are all rational. Behavioral Economics comes and says: “No! We are all irrational and we make all kinds of mistakes based on mental shortcuts”. What’s next? You guessed it! According to the third principle of mechanics (action and reaction), the research continues and shows that, sometimes, mental shortcuts are not only faster and more comfortable, but give more accurate results.

These shortcuts are called “heuristics” and are defined as simple decision algorithms that deliberately ignore certain information in favor of speed or convenience. Heuristics have existed since the beginning of time (I will give a few examples below), but the novelty of this third wave of theory is that, sometimes, heuristics are more accurate than complex models which consider all available information. Here are the examples I was talking about:

Hiatus Heuristic

It’s important for retailers to know when to consider a customer as active (will buy in the near future) or inactive. There are complex regression models and other complicated calculation methods that determine the active or inactive status of a customer. However, in the market, retailers use what is called “Hiatus Heuristic”: if a customer has not bought something for 9 months, we can consider him inactive.

Other retailers use a 6-month period as reference. This model ignores all information about a customer (demographics, how much he bought before, how often, etc.) except for the time since the last purchase. How accurate do you think this method is? According to Gigerenzer and Gaissmaier [ii], more accurate than complex models that include all parameters (in different studies 83% versus 75% or 77% versus 74%).

Sometimes “better” is the enemy of the “good”. And don’t get me started on the perfectionism discussion. Sometimes, a good solution now is better than a better one tomorrow. This led to the “satisficing” model (combination of “satisfying” and “sufficing”) in which you define a minimum criterion that a solution should have, you search until you find a first option that meets the criterion and then stop searching and implement the first found.

Recognition Heuristic

Here the only criterion that guides you when you have to choose between several options is the one that seems more familiar to you. And if all the options seem familiar to you, then you choose the one you’ve heard about most often. Really? And does it work? It works!

An experiment done in Germany went like this: during the Wimbledon tennis tournament, a number of Germans were asked to predict the results of the matches based on how familiar they are with the names of the tennis players. This method gave better results (72% accuracy) than ATP Entry Ranking (66%), ATP Champions Race (68%) and Wimbledon experts (69%). Ok, maybe it works in tennis, but that’s a business magazine. Well, it works in business too! Some portfolios were created based on this recognition model and performed better than famous funds, market indices (Dow) or even experts[iii].


This technique is the one described at the beginning of the article, the one used by Markowitz for his own portfolio. When allocating resources (time, money) for a number of projects, it’s more efficient to divide the resources equally than to calculate according complex models that involve many assumptions and anticipations.

That’s why it’s called 1/N. If you have two projects, divide the resources into ½ and ½. If you have three projects, divide resources into 1/3, 1/3 and 1/3. And so on. 1/N was the most effective method out of 15 models studied for predicting investment performance. That’s why Markowitz preferred it to his own method.

Liquidity ratios

The economy books describe various indicators of the activity or health of a business. Perhaps the most important are the two liquidity ratios, which show how far a firm is from insolvency. The first ratio is the current asset ratio and is calculated as the ratio between current assets and current liabilities and must be at least 1:1 (some books say 2:1 at least).

The second is called the acid test ratio and is calculated as the ratio between current assets minus stock and current liabilities. The minimum viable here is 1:1. However, none of these values ​​have any mathematical proof or scientific proof. The economics books highlight that these indicators should only be used as a guide. These are values ​​deduced from practice, but they seem to work very well.

Prior to Gigerenzer’s research, it was considered that if you use of these shortcuts, you sacrifice accuracy in favor of speed or convenience. However, at least in the cases described above, the accuracy of heuristics proves to be better.

And then the question arises: why not throw away complex models? Well, apart from the fact that, obviously, the above examples only describe the cases when heuristics work (there are tons of examples when they fail), there is something else: heuristics are far away from instinct. Heuristics are methods that use reason in very small quantities.

And one more thing: a leader needs rational analysis to know when to use them and when not. When we have a limited number of alternatives and the success of each can be predicted within a reasonable range, complex models can be used to successfully determine the optimal solution. When certain information is missing, when future evolution is uncertain and the estimation of probabilities is too risky, it’s more efficient (faster, more convenient and more precise) to use simplified (heuristic) models that deliberately ignore large amounts of information.

Instinct-based solutions

Okay, now we know what’s the thing with shortcuts and mental simplifications. But what about instinct? Well, let’s return to the study of puzzles and riddles (remember: instinct-based solutions were better than those from analytical thinking). In reality, it’s not that simple.

First, the study was done with a time limit (each problem had to be solved in 15 seconds), and a wrong answer (sometimes unavoidable in rational solutions, but absent when the solution occurs to you, suddenly comes into your mind) was marked lower than a missing answer. Secondly, if we want to extrapolate the use of these “aha moments” in running a business, we must not ignore the past experience of the person looking at a situation.

What I mean is that, indeed, sometimes the solutions suddenly come into someone’s mind, but this happens especially to experts in the field. You must have solved hundreds of similar situations to develop a healthy instinct. In his article, Intuition versus Reason [iv], philosopher Berit Brogaard describes how chess champions store in the long-term memory over 300,000 positions on the board.

Unlike a beginner, who analyzes piece by piece, they recognize these patterns and apply solutions they played before, which come from instinct. But it’s an instinct based on experience. To return to business area, I would always trust the instinct of an experienced CEO, but not when I participate in the BIZ CEO Exchange (an annual exercise in which some companies change their bosses for a day).

In conclusion, mental shortcuts work, but you have to decide rationally when to apply them and which ones. Instinct works, but based on a lot of experience. So, despite this new theoretical current, we must not neglect analytical thinking at all. Look, I can still teach Critical Thinking without being worried (hiuh!).

I hope you recognized the name of a Jane Austen novel in the title. Perhaps the fans also recognized the paraphrase in the first sentence. This paraphrase is intended as a prologue to a future article also inspired by Jane Austen, “Pride and prejudice in business”.

[i]  “Trust Your Aha! Moments, Experiments Show They’re Probably Right” de Frank Otto, in Drexel Now, 2016.

[ii] “Heuristic Decision Making” by Gerd Gigerenzer and Wolfgang Gaissmaier, in Annual Reviews of Psychology, 2011

[iii] “Heuristic Decision Making” by Gerd Gigerenzer and Wolfgang Gaissmaier, in Annual Reviews of Psychology, 2011

[iv] Intuition versus Reason, by Berit Brogaard, Institute of Art and Ideas 2017,

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