Introduction

The rules that determine what gets attention on social platforms are often called “algorithms,” but algorithms are only part of the story. Another way to understand social platforms is as social protocols, or sets of formal and informal rules that groups use to allocate their collective attention.

Like parliamentary procedure or courtroom rules, social protocols shape conversations: they determine what topics are discussed and which voices are heard. This can help explain some of the phenomena we see in social networks—like filter bubbles, polarization, and misinformation—and suggest ways social protocols could be engineered to promote healthier outcomes.

Measuring Uncertainty

How do you measure “uncertainty”?

That may seem like an odd question. But let’s just dive right into it, because starting down this path of inquiry will lead us step by step to the definition of the fascinating concept of Shannon entropy.

“I’m 99% Certain”

We can start with one common way people express certainty. You might say “I’m 99% certain it will rain today”. This, of course, implies that you’re 1% uncertain. So one obvious definition of uncertainty is the inverse of certainty, or $1 - p$, where $p$ is certainty expressed as a percentage.

Introduction

In most programming languages, any function can reach out and touch the world: read the clock, write a file, open a socket. But ambient access to state—through singletons, globals, built-in functions—makes testing brittle and reasoning murky. You run a test once and it passes; run it again and it fails because the clock advanced or a temporary file wasn’t deleted. You call out to a small math library and it exfiltrates your SSH keys because it had access to your home directory.