Can we trust our intuition?

As a Venture Capital investor, you need to select a company in which to invest out of 10. Your time is extremely limited. There are many criteria. You have just read the long summaries of interviews, business plans and due diligence processes for each of it.

How do you proceed to take your final decision? Can you trust your intuition?

Intuition and heuristics

When we use our intuition (see system 1) to make decisions, we usually use heuristics.

It refers to any approach which helps to make decisions quickly and effectively, even if there are not always optimal or fully rational. There are mental shortcuts, rule of thumb or educated guess which help us to make decisions quickly and often effectively with a limited cognitive load.

Heuristics are an effective way to solve rapidly complex problems. Nevertheless, the provided answer is often an approximation or even false. It is thus important to learn the ”bad habits” of heuristics to use them adequately.

In the Kahneman system, this corresponds to our system 1, while our system 2 will correspond to a thoughtful approach of the problem.

To come back to our problem, the intuitive approach will mean that, after reading all the documents, we choose the company that we “feel” the most relevant. IN the thoughtful approach, a new analytical approach will be necessary, like creating an excel file with a specific weight for each criteria, for instance.

So what is the most effective approach? Research in cognitive science has shown that this highly depends on the context.

Criteria 1: Familiarity

The first aspect to consider is if we are making our decision in a familiar or in a new context. Is it the first time we to select this kind of companies or is it a situation we face every month? Do we already have information about the performance of the companies we selected previously?

Heuristics are indeed mostly based on our past experience. More precisely, research in behavioral economics have shown they are based on a Bayesian learning model.

We simply have an a priori belief about, for instance, the link between being a female CEO and being a successful startup that we update each time we interact with a female CEO.

These prior beliefs help us cope with uncertainty. When we face a new situation, we try first to identify similar situations we had in the past to decide how to react. The more similar situations we faced in the past, the most powerful how intuitive thinking should be. An art expert will immediately recognize that a painting is from Picasso, even if he has never seen this painting. For that, he will not use his analytical thinking. His intuitive thinking, based on his numerous past experience, will be more effective.

He will have difficulties to explain why he is convinced that it is a Picasso and justify it by tangible elements.

On the contrary, if you do not know a lot about art and want to identify the author of the painting. We will need time and attention to analyse thoughtfully the painting in order to find the solution based on rationalized elements (date, color, subject, style…).

TO come back to our example, if you are new to VC investing, it may be dangerous to trust your intuition to find the right investment.

To further understand this powerful learning system of the brain, you can look at this more theoretical talk from the University of Edinburgh.

Criteria 2: Number of criteria

Many cognitive experiments have shown that our conscious brain (system 2) cannot deal with a choice where there are more than 5/7 criteria to take into consideration.

This is the case when we want buy a car, for instance. If we focus on a limited number of features (ex: price, color, size, hybrid, brand), we can find the right car using our analytical brain (system 2). If we focus on more criteria (ex: price, color, size, hybrid, brand, look, number of seats, engine power,…), our conscious brain will not be able to manage so much data. Our decision will be biased and suboptimal. We will either focus unconsciously on less criteria, or choose the last car we have seen, or the one our neighbor has just chosen… The experiments show that we will be less satisfied by our choice in this case than if we had chosen the right car intuitively.

If we come back to our example, our analytical brain can be effective if the selection criteria are limited and quite clear. For instance, the company should have a minimum turnover of 1M euros, should have a developed technology and have a positive environmental impact.

In this case, the choice can be quite straightforward. If there are more criteria, the analytical decision will be complicated and you may certainly need to if

If the list of criteria is much longer or very subjective, our analytical brain may not be able to treat properly so much data. There will be the option to use tools to help us (excel, paper, decision-making software…), or to trust our intuition if time is limited and do not allow this time-consuming decision-making processes.

Criteria 3: Time and effort

Using our analytical brain is always more time and effort-consuming that using our intuition. If your time is very limited or if you are very tired, you may not be able to use your system 2 properly. You will, for instance, analyze the first 3 companies in details, and then give up. In this case, it should be much more effective to use our intuitive brain than using our broken analytical decision-making process.

Criteria 4: Situation prone to personal biases

A last aspect to take into consideration is your knowledge of potential biases which could influence your decisions. You love biscuits. One of the companies offer a new concept of biscuits. You are biased in favor of this company. You know the father of one of the founders ? Even worth…

In these situations, trusting your intuition may be suboptimal. Coming back to a more analytical approach may help you to limit these biases.


Thinking fast and slow, D.Kahneman, 2001

Antifrigale, Nassim Taleb, 2012

Knill, D. C., & Pouget, A. (2004). The Bayesian brain: the role of uncertainty in neural coding and computation. TRENDS in Neurosciences, 27(12)