Leveraging Agent-based Models and Digital Twins to Prevent Injuries

On the surface, preventing injuries to professional-caliber athletes would seem to have little in common with preventing operational failures for a machine (i.e., autonomous vehicle, locomotive, airplane, CT Scan). However, both athletes and machines deal with inter-twined complex systems (where the interactions of one complex system can have a ripple effect on others) that can have significant impact on their operational effectiveness. My son Max, the Director of Sport Science at Resilient Code and Chief Science Officer at Exsurgo Technologies, has turned into quite an analytics nerd (check him out on twitter at @strong_science, but he saves his good stuff for Instagram where you can follow him at "strong_by_science"). Max and Resilient Code co-founder Dr. Dustin Nabhan have been educating me on the use of Agent-based Models (ABM) as a technique to predict and prevent injuries in athletes, especially high-caliber professional athletes. Unlike the weekend warrior like myself, preventing career-ending injuries can translate into tens of millions of dollars of additional income for professional athletes[1]. Here is the conversation with Max that got me thinking more about the similarities between ABM and Digital Twins (Max's input is in drab, boring grey and mine is in cool, hip blue): Okay, probably a boring conversation for most families, but now you know what we talk about when we go out to eat! Yea, you don't wanna sit next to us… During these conversations with Max and Dustin, I was struck by the similarities in using ABM to prevent injuries in the same way that we use Digital Twins (or what we call Asset Avatars) to prevent machine breakdowns, failures, under-performance and unplanned downtime.