BayeSim
BayeSim (Bayesian x The Sims) is a speculative tech startup offering bespoke, probability-backed insights into digital social interactions using manual Bayesian analysis and machine learning. BayeSim tracks and interprets behavior using aggregated, cross-platform social media data, relieving users of what is typically a mental burden and providing behavioral predictions. BayeSim aims to streamline the mental tracking and interpretation process to help users navigate unspoken peer-peer tracking norms and digital social dynamics with confidence.
“Checking in on my Sims” has become a casual shorthand for the now-common practice of tracking friends’ locations through apps like Snapchat or Find My. Social media has given rise to subtle forms of everyday social surveillance, where we play amateur detective and assemble a picture of someone’s life from scattered digital clues. While most of us are aware we engage in these behaviors, they’re still wrapped in a quiet sense of secrecy, even shame.
Over time, unspoken rules have emerged; it’s fine to scroll through someone’s profile, but don’t accidentally like a post from 2015 that would reveal you were looking. These informal norms shape how we monitor one another: carefully and often without reflection, only selectively sharing what we do and don’t monitor.
My thesis seeks to bring those hidden behaviors to light. What if these norms weren’t subtle, but explicit? What if we took them to their logical extreme? What would it mean to offload the mental gymnastics we perform when tracking friends online? Imagine outsourcing this work to a centralized service designed to collect, interpret, and predict our friends’ digital behaviors in the name of “better relationships” and “mental wellness.”
These are the questions at the heart BayeSim, challenging us to confront the normalized, yet unsettling, ways we engage in social tracking, and to reflect on what it means to watch and be watched in the digital age.
