Rethinking fast and slow

Everyone except homo economicus knows that our brains have multiple processes to make decisions. Are you going to make the same decision when you are angry as when you sit down and meditate on a question? Of course not. Kahneman and Tversky have famously reduced this to ‘thinking fast’ (intuitive decisions) and ‘thinking slow’ (logical inference) (1).

Breaking these decisions up into ‘fast’ and ‘slow’ makes it easy to design experiments that can disentangle whether people use their lizard brains or their shiny silicon engines when making any given decision. Here’s how: give someone two options, let’s say a ‘greedy’ option or an ‘altruistic’ option. Now simply look at how long it takes them to to choose each option. Is it fast or slow? Congratulations, you have successfully found that greed is intuitive while altruism requires a person to sigh, restrain themselves, think things over, clip some coupons, and decide on the better path.

This method actually is a useful way of investigating how the brain makes decisions; harder decisions really do take longer to be processed by the brain and we have the neural data to prove it. But there’s the rub. When you make a decision, it is not simply a matter of intuitive versus deliberative. It is also how hard the question is. And this really depends on the person. Not everyone values money in the same way! Or even in the same way at different times! I really want to have a dollar bill on me when it is hot, humid, and I am front of a soda machine. I care about a dollar bill a lot less when I am at home in front of my fridge.

So let’s go back to classical economics; let’s pretend like we can measure how much someone values money with a utility curve. Measure everyones utility curve and find their indifference – the point at which they don’t care about making one choice over the other. Now you can ask about the relative speed. If you make each decision 50% of the time, but one decision is still faster then you can say something about the relative reaction times and ways of processing.

dictator game fast and slow

And what do you find? In some of the classic experiments – nothing! People make each decision equally as often and equally quickly! Harder decisions require more time, and that is what is being measured here. People have heterogeneous preferences, and you cannot accurately measure decisions without taking this into account subject by subject. No one cares about the population average: we only care what an individual will do.

temporal discounting fast and slow

But this is a fairly subtle point. This simple one-dimensional metric – how fast you respond to something – may not be able to disentangle the possibility that those who use their ‘lizard brain’ may simply have a greater utility for money (this is where brain imaging would come in to save the day).

No one is arguing that there are not multiple systems of decision-making in the brain – some faster and some slower, some that will come up with one answer and one that will come up with another. But we must be very very careful when attempting to measure which is fast and which is slow.

(1) this is still ridiculously reductive but still miles better than the ‘we compute utility this one way’ style of thinking


Krajbich, I., Bartling, B., Hare, T., & Fehr, E. (2015). Rethinking fast and slow based on a critique of reaction-time reverse inference Nature Communications, 6 DOI: 10.1038/ncomms8455


How little we know

In a recent issue of Nature there is a discussion of the history of utility theory:

Three centuries ago, in September 1713, the Swiss mathematician Nikolaus Bernoulli wrote a letter to a fellow mathematician in France, the nobleman Pierre Rémond de Montmort. In it, Bernoulli described an innocent-sounding puzzle about a lottery…The result is surprising. Each product — 1 × ½, 2 × ¼, 4 × ⅛, and so on — is a half. Because the series never ends, given that there is a real, if minute, chance of a very long run of tails before the first head is thrown, infinitely many halves must be summed. Shockingly, the expected win amounts to infinity…

In May 1728, writing from London, the 23-year-old mathematician Gabriel Cramer from Geneva weighed in. “Mathematicians value money in proportion to its quantity, and men of good sense in proportion to the usage that they may make of it.” This was a far-ranging insight. Adding a ducat to a millionaire’s account will not make him happier, Cramer reasoned. The usefulness of an extra coin is never zero, but simply less than that of the previous one — as wealth increases, so does utility, but at a decreasing rate. Assuming that utility increases with the square root of wealth, Cramer recalculated the expected win to be a little over 2.9 ducats.

Daniel encapsulated the probability scenario in a plot of utility versus monetary value, now known as a ‘utility function’ (see ‘Risky business’)… The curve’s diminishing gradient implies that it is always worth paying a premium to avoid a risk. The consequences of this simple graph are enormous. Risk aversion, as expressed in the concave shape of the utility function, tells us that people prefer to receive a smaller but certain amount of money, rather than facing a risky prospect.

It was a bit shocking to me how advanced these concepts were for the year 1700 – and how we haven’t come very far from those insights from the mathematicians of the 18th century. It’s a testament to the lack of experiment in economics that it took until the 1900s for Allais (and his “paradox”) and Kahneman and Tversky‘s theories to come about.