Knowledge Tracks is an evolving theory that combines Particle Physics concepts and techniques with Knowledge Management practices to understand and classify human interactions in pursuit of knowledge transfer.
Knowledge Tracks is the application of our understanding of particle collision signature trails that arise in particle accelerators. Particle Physicists analyze particle collision tracks to study various known and unknown particles. Each particle produced in a collision reveals specific trajectories, energy, and other properties unique to a particle. Together, these properties can help identify and predict outcomes of the particles and their interactions. In the same light, we can apply the same concepts and related techniques to help us to identify and classify human interactions that arise in the daily life of the knowledge worker. Knowledge Tracks can help us answer questions such as: What are the characteristics (or tracks) of human interactions that lead to successful knowledge transfer? Can the likelihood of knowledge transfer be predicted, if so what does that look like and can it be replicated once we understand the inputs?