From the very first days of the world wide web, engineers used webcrawlers (automated bots that move randomly between websites) to collect information about the newly created sites.
But the question was how to put all the information they collected together to find the most interesting content?
To answer this question, the founders of Google, Larry Page and Sergey Brin, had an idea. They realised that the real information was not to be found in the words and pictures on the internet, but hidden in the process of hopping itself: because the webcrawlers moved around at random, and there were more links to more popular pages, the most popular pages would be visited more often.
To turn this insight into an algorithm that became Google Search and made them both billionaires, Page and Brin made use of a 90-year-old equation, known as the stationary distribution of a Markov chain. This equation allowed them to automatically generate the search results we use many times per day.
I use the name The Influencer Equation when I refer to the Markov chain equation; both because it is easier to remember and, mostly, because it captures the way it has been used, not only by Google, but by other social media giants, like Amazon, Netflix and Instagram.
It has made searching more convenient – it is easier to find the most popular books, films and Instagram selfies – but it has also had a side effect. Since the equation doesn’t look at the content of web pages, it means that the only factor it accounts for when recommending things to us is popularity. In this way, The Influencer Equation warps our view of the world.