The hidden danger of big data

The hidden danger of big data

The hidden danger of big data
Dirk Helbing is professor of computational social science at the Swiss Federal Institute of Technology.

In game theory, the “price of anarchy” describes how individuals acting in their own self-interest within a larger system tend to reduce that larger system’s efficiency. It is a ubiquitous phenomenon, one that almost all of us confront, in some form, on a regular basis.

For example, if you are a city planner in charge of traffic management, there are two ways you can address traffic flows in your city. Generally, a centralised, top-down approach – one that comprehends the entire system, identifies choke points, and makes changes to eliminate them – will be more efficient than simply letting individual drivers make their own choices on the road, with the assumption that these choices, on aggregate, will lead to an acceptable outcome.

The first approach reduces the cost of anarchy and makes better use of all available information.

The world today is awash in data. In 2015, humankind produced as much information as was created in all previous years of human civilisation.

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Every time we send a message, make a call or complete a transaction, we leave digital traces. We are quickly approaching what Italian writer Italo Calvino presciently called the “memory of the world”: a full digital copy of our physical universe.

As the internet expands into new realms of physical space through the internet of things, the price of anarchy will become a crucial metric in our society, and the temptation to eliminate it with the power of big data analytics will grow stronger.

Examples of this abound. Consider the familiar act of buying a book online through Amazon. Amazon has a mountain of information about all of its users – from their profiles to their search histories to the sentences they highlight in e-books – which it uses to predict what they might want to buy next.

As in all forms of centralised artificial intelligence, past patterns are used to forecast future ones. Amazon can look at the last 10 books you purchased and, with increasing accuracy, suggest what you might want to read next.

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But here we should consider what is lost when we reduce the level of anarchy. The most meaningful book you should read after those previous 10 is not one that fits neatly into an established pattern, but rather one that surprises or challenges you to look at the world in a different way.

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