In his pathbreaking 2005 book On Intelligence, Jeff Hawkins proposed an alternative paradigm of how the human brain works. In his view, the brain is not a Turing machine that manipulates symbols according to a table of rules, which is the model on which computers and artificial intelligence have been based. Instead, the brain is a giant hierarchical memory that is constantly recording what it perceives and predicting what will come next.
The brain makes predictions by finding similarities between patterns in recent sensory inputs and previous experiences stored in its vast memory. It matches current fragmentary sounds in a sea of noise with a known song, or the face of a person in disguise with that of your child. The idea is similar to the auto-complete function in, say, the Google search box – constantly guessing what you will enter next based on what you have already typed in.
To see the hierarchy in this mechanism, consider that by perceiving just a few letters, you can predict the word; by looking at a few words, you can predict what the sentence means, or even the paragraph. In fact, right now you must be guessing where it is that I am going with this entire commentary. The hierarchy allows you to understand meaning, whether the input got to your brain by reading or listening. The brain is thus an inductive machine that predicts the future based on finding similarities, at many different levels, between the present and the past.
Hawkins’ alternative model of how the brain works has important implications for many fields, including the one that I spend most of my time thinking about: economic-development strategy.
By definition, development is not just more of the same, just as an adult is not just a big baby. The process involves adding and combining new and existing capabilities to support more diverse and complex activities.
But finding new things that can be done successfully is tricky, because it requires knowing what you will need and whether you will be able to procure it. This is why Jeffrey Sachs’s Millennium Villages project has faltered, as the journalist Nina Munk’s recent book shows. In trying to move farmers from subsistence to commercial agriculture, Munk argues, there are just too many missing pieces.
Traditional thinking in economic development has followed a Turing-like approach, trying to specify a general model of the world – based on first principles – and then use that model to think about a country’s predicament or a policy’s potential impact. But the world is often too complex and nuanced for such an approach.
Would it not be a great improvement if, when looking at a particular place, we could have in mind all of the world’s previous experiences and automatically identify the most relevant ones, in order to infer what to do next? Would it not be useful to see the development possibilities just as our brain, according to Hawkins, sees the world?
An alternative, Hawkins-like approach to economic development would take massive amounts of data about the world and ask what is likely to succeed next in a country or a city at a given point in time, given what is already present and in light of the experience there and everywhere else. It would be like Amazon’s recommendation system, proposing books you may like based on your and everybody else’s experience.
In a recent paper, my colleagues and I showed that such an approach to economic development actually works. In a particular city or country, you can predict, even a decade in advance, which industries will appear or disappear or grow or wane just by knowing the history of what has been there and everywhere else.
Countries tend to move into industries that are related to the ones they already have or that are present in locations that are similar to them. We have made the approach user-friendly for countries in our recent Atlas of Economic Complexity.
The idea of looking at previous experiences to inform future action is as old as civilization. Following this intuition, Justin Yifu Lin, the former chief economist of the World Bank, has suggested that when countries choose what to do next, they should look at a successful country that was similar to them two decades ago.
But we should be able to do much better than that by looking at many more experiences in much more detail, using a much bigger memory that can find many more patterns across much more of the relevant human experience. Imagine that Sachs’s Millennium Villages project had known the sequence of all previous successful moves out of subsistence agriculture, rather than relying only on guesswork or deduction. Would it not be useful to understand the paths to industrial development – and the dead ends – that are most relevant to a particular country today?
This alternative approach can empower many more people to seek successful routes to prosperity by lowering the perils and risks involved in the search – in the same way that maps empower people to get to where they want to go with much more information than they would otherwise have. Just as augmented-reality technologies make our experience of the world richer (imagine a sports match today without instant replay), putting the development experience of the world at the fingertips of those engaged in promoting development is now perfectly feasible. We should seize this opportunity.