Reducing complexity to nothing

In machine learning, we reduce multiple dimensions to something we can manageably visualise to interpret relationships. More than three are a challenge for us humans to visualise, as our visual experience of the world is in three physical dimensions, and it's tough to keep track of our experience as we move through the fourth. Yet, we have political ideological systems that, in the most advanced of countries, operates on a single axis with opposites (“left” and “right”; “republican” and “democrat”). All this multidimensional complexity is projected onto this single axis. In machine learning, we call this dimensionality reduction. Our ability to project all the complexity that we face in society onto a single dimension is a farce: We have to fix our political systems.

We cannot distil out the complexity of the modern world onto a single line. We lose too much information. We're running this experiment over and over again, and polarising the a complex world into something we can instantly consume, and it does us a disservice. So much is lost in the nuance.

We need to build resilience for ourselves to this changing world, so we can understand and grapple with complexity. And, we need leadership that can keep up. We need political systems that keep up with a multidimensional world.