David provides a deep overview of ten equations: Betting, Judgement, Confidence, Skill , Influencer, Market, Advertising, Reward, Learning, and The Universal Equation. Each equation (algorithm) includes stories that certainly provide deeper understanding.
On the other hand, the opening chapter did not really connect with me. The Betting Equation focuses on UK soccer and for reveals how betting odds can be adjusted against reality.
Nevertheless, I am not sure why this did not resonate as the rest of the book is just delightful to read.
In addition, the second chapter, Judgement Equation is the basis of Bayesian statistics and this storyline really stuck with me.
Furthermore, it is great to see David reference The Signal and the Noise by Nate Silver within the third chapter, The Confidence Equation.
The Skill Equation:
So buckle up, The Skill Equation focuses on Markov Chains. This is a fantastic chapter about the Markov assumption applied to sports. This is perhaps the most fascinating chapter of the book.
Lessons draw upon Markov Chains use the poster child Moneyball by Michael Lewis. Nothing new here, However, to my surprise, the amount of algorithms applied to NBA players by the San Antonio Spurs basketball club is very surprising.
The Influencer Equation:
The Influencer Equation shows how a network settles against certain weights with the weight of each being the influence of each node. This is certainly at the heart of Google’s search algorithm:
This is why equation 5, the influencer equation, has been so valuable to internet giants. It tells them who are the most important people on their network, without the company knowing anything about who these people actually are or what they do.
pg. 180
Accordingly, what is missing are recent developments in which social media companies are pimping influencers for additional revenue.
Facebook/Instagram:
In addition, knowledge that anyone can actually purchase Facebook likes to falsely drive up their influence is much well understood. However, this is outside of David’s goals of simply providing the algorithm and related insights.
A former Instagram employee told me that the company’s founders “saw Instagram as very niche, very artistic, and they viewed algorithms as inauthentic.” The platform was for sharing of photographs between close friends. So, initially, they resisted using an algorithm to decide which friends’ images should be shown highest up in a user’s feed, and simply showed the most recent pictures. That all changed when Facebook took over. “Over the last couple of years, the platform has become drastically different. One percent of its users now have over 90 percent of its followers,” my contact told me.
Instead of encouraging users to follow only their friends, the company solved the influencer equation for its social network. It promoted the most popular accounts. The feedback began, and the celebrity accounts grew and grew. So, too, did the company, to over 1 billion users. Just like every other social media platform before it, once Instagram used the influencer equation, its popularity exploded
pg. 182
In conclusion, David’s writing is certainly providing insights to a foundation of equations carrying deep insights to everyday human behavior. Actually after finishing this book, I decided to re-read chapter one. Still a miss, perhaps due to the deep focus on gambling on UK soccer teams and their long history continues to miss the mark with me.
London School of Economics | The Ten Equations That Rule The World
Alain Guillot | David Sumpter: The Ten Equations That Rule the World
Oxford Mathematics | How Learning Ten Equations Can Improve Your Life
The Royal Institution | The 10 Equations that Rule the World