Thinking, Fast and Slow: A Brief Overview and Analysis

The best-selling behavioural economics and psychology book written by Nobel laureate Daniel Kahneman

CRAVE Analysis

CRAVE stands for Credibility, Reliability, Accuracy, Vested interest and Expertise, and is a system of analysing sources like books and articles to ensure that they are of high quality, and so I thought it would be a good idea to do this for any books that I review in my articles.

Daniel Kahneman won the Nobel Prize in Economics for his contribution to the field of behavioural economics despite being a psychologist, with his book ‘Thinking, Fast and Slow’ revolutionising the field. His Nobel Prize clearly evidences his expertise and credibility in the subject, and the theories and ideas presented can be considered accurate and reliable as despite having been released in 2011, they have very little criticism and have been heavily peer-reviewed, receiving praise across the world and selling over one million copies. As an academic, Kahneman has no vested interest in promoting these theories, nor does he show any bias – he instead investigates biases within humans, such as loss aversion or bounded rationality.


The primary message of the book discusses the two primary systems that are put into action when a human makes a decision – System 1 and System 2. System 1, also known as the Automatic System, takes care of most decisions and does not require any conscious effort – it ranges from choosing how to get to work in the morning to picking up your phone when you’re bored. However, this automatic decision-making process can lead to irrational choices, such as being scared of a shadow behind the door, and the use of heuristics when making the wrong decision can lead to cognitive biases. System 2 is also known as the Reflective System, which takes time and effort but usually makes the rational decision more often. A decision made by System 2 could be where to go on holiday or what car to get, as these are important decisions that would be very costly if made incorrectly.

Kahneman also explores a variety of cognitive biases and heuristics, such as the heuristics of availability and anchoring, and biases like computational weakness or loss aversion. One thing that I found especially enjoyable in this book, and which made it a lot easier to understand, was the use of multiple well-explained and very relevant examples, ranging from analogies to his own primary research. It gave substance and backing to what he explained and also contextualised his work, allowing the reader to see how the concepts he discussed could be applied in real life.


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