A well known mathematical problem begins with a group of millionaires meeting over lunch (clearly a pre-COVID scenario). The group has a wonderful time, enjoying their food and drinks wholeheartedly. As they ask for the bill, one particularly cheeky member of the group proposes that the richest among them pay the bill. The group agrees, but immediately finds itself at an impasse over the bill. No one wants to share their exact wealth.
This scenario, called the Millionaires’ problem, was first proposed in 1982 by computer scientist and computational theorist Andrew Yao. It is a secure multi-party computation problem, a subfield of cryptography with applications in e-commerce and data mining. It is commercially applicable to any situation where there is a need to securely compare numbers that are confidential.
Many solutions to this mathematical problem have been put forth, the first presented by Yao himself. You can read more about those here. As the Glowlit team set out to reimagine market intelligence in the Animal Feed industry, we looked to the Millionaires’ problem for inspiration. What we saw was really an issue of trust. Each of the millionaires needed to know where they stood relative to the others, without giving away their own position.
Glowlit facilitates trust across the industry by allowing users to anonymously benchmark their position relative to the market. Like the Millionaires’ problem, our solution is mathematical. The Glowlit algorithm ensures that only verified price entries make it into our reports, and the accuracy of our reports encourages more users to enter better data. We see this time and again. As products grow in numbers of user entries, we see a proportional increase in the number of verified entries. Slowly but surely, we’re restoring trust across the supply chain.
Founder and CEO of Glowlit.com