Make better decisions by understanding The Neuroscience Behind Bad Decisions
How can we make better decisions
For many of us, the main concern over decision-making is practical — how can we make better decisions?
When we are overwhelmed by choice, how can we make better decisions?
Rather than pick what we hope is the best, instead, always start by eliminating the worst element from a choice set, reducing the number of options to something manageable, like three.
It derives from our study of the math. Sometimes you learn something simple from the most complex stuff, and it really can improve your decision-making.
The Neuroscience Behind Bad Decisions
Irrationality may be a consequence of the brain’s ravenous energy needs.
Our decision-making system is subject to glitches.
Knowing something about how information is represented in the brain and the computational principles of the brain helps you understand why people make decisions how they do. This is called neuro-economics.
The neural model, described in biology and tested in neurons, works well to describe something economists couldn’t explain.
At the core of the model lies the brain’s insatiable appetite. The brain is the most metabolically expensive tissue in the body. It consumes 20 percent of our energy despite taking up only 2 to 3 percent of our mass. Because neurons are so energy-hungry, the brain is a battleground where precision and efficiency are opponents. The costs of boosting our decision-making precision outweigh the benefits. Thus we’re left to be confounded by the choices of the modern American cereal aisle.
Neuroeconomics is still a young field; scientists don’t even agree on what part of the brain makes decisions, let alone how.
The brain is a power-hungry organ; neurons are constantly sending each other information in the form of electrical pulses, known as spikes or action potentials. Just as with an electrical burst, prepping and firing these signals take a lot of energy.
In the 1960s, scientists proposed that the brain dealt with this challenge by encoding information as efficiently as possible, a model called the efficient coding hypothesis. It predicts that neurons will encode data using the fewest possible spikes, just as communication networks strive to transmit information in the fewest bits.
In the late 1990s and early 2000s, scientists showed that this principle is indeed at work in the visual system.
The brain efficiently encodes the visual world by ignoring predictable information and focusing on the surprising stuff. If one part of a wall is yellow, chances are the rest is also yellow, and neurons can gloss over the details of that section. But a giant red splotch on the wall is unexpected, and neurons will pay special attention to it.
The scientists behind neuro-economics propose that
the brain’s decision-making machinery works the same way.
Imagine a simple decision-making scenario: a monkey choosing between two cups of juice. For simplicity’s sake, assume the monkey’s brain represents each choice with a single neuron. The more attractive the choice is, the faster the neuron fires. The monkey then compares neuron-firing rates to make his selection.
Divisive Normalization
The first thing the experimenter does is present the monkey with an easy choice: a teaspoon of yummy juice versus an entire jug. The teaspoon neuron might fire one spike per second while the jug neuron fires 100 spikes per second. In that case, it’s easy to tell the difference between the two options.
When the choice is simple, it is easy for the brain to pick an option.
The situation gets muddled when the monkey is then offered the choice between a full jug of juice and one that’s nearly full. A neuron might represent that newest offer with 80 spikes per second.
It’s much more challenging for the monkey to distinguish between a neuron firing 80 spikes per second and 100 spikes per second.
How can we avoid this problem? Glimcher proposes that the brain avoids this problem by
recalibrating the scale to best represent the new choice.
The neuron representing the almost-full jug — now the worst of the two choices — scales down to a much lower firing rate. Once again it’s easy for the monkey to differentiate between the two choices.
Glimcher’s model, based on an earlier model known as divisive normalization, spells out the math behind this recalibration process. It proposes that
neurons can send more efficient messages if they encode in their sequence of spikes only the relative differences among the choices.
“Choice sets have a lot of shared information; they are not random and independent,”
Glimcher said.
“Normalization is sucking out redundant information so that the information coming out is as relevant as possible, wasting as little energy as possible.”
He notes that
engineers, who are used to working with adaptive systems, aren’t surprised by this idea. But people who study choice often are.
What’s great about divisive normalization is that it takes these principles we know from vision and applies them to value in ways that make sense but are out of the box.
The divisive normalization framework emerged from work in the visual system. Yu suggests that applying it to decision-making is more complex. Scientists know a lot about the information that the visual system is trying to encode: a two-dimensional scene painted in color, light and shadow. Natural scenes conform to a set of general, easy-to-calculate properties that the brain can use to filter out redundant information. In simple terms, if one pixel is green, its neighboring pixels are more likely to be green than red.
But
the decision-making system operates under more complex constraints and has to consider many different types of information.
For example, a person might choose which house to buy depending on its location, size or style. But the relative importance of each of these factors, as well as their optimal value — city or suburbs, Victorian or modern — is fundamentally subjective. It varies from person to person and may even change for an individual depending on their stage of life. There is not one simple, easy-to-measure mathematical quantity like redundancy that decision scientists universally agree on as being a key factor in the comparison of competing alternatives.
Uncertainty in how we value different options is behind some of our poor decisions.
If you’ve bought a lot of houses, you’ll evaluate houses differently than if you were a first-time homebuyer. Or if your parents bought a house during the housing crisis, it may later affect how you buy a house.
Moreover, the visual and decision-making systems have different end-goals. Vision is a sensory system whose job is to recover as much information as possible from the world. Decision-making is about trying to make a decision you’ll enjoy. I think the computational goal is not just information, it’s something more behaviorally relevant like total enjoyment.
For many of us, the main concern over decision-making is practical — how can we make better decisions? The research for neuro-economics can help develop specific strategies.
Rather than pick what we hope would be best, instead, always start by eliminating the worst element from a choice set - reducing the number of options to something manageable, like three.
It derives from our study of the math. Sometimes you learn something simple from the most complex stuff, and it really can improve your decision-making.