There’s a scene in Harold and Maude where Maude, having just described how every daisy is unique and different, comments that much of the world’s sorrow comes from people who are this – unique like the flower – yet allow themselves be treated as that. She gestures to the wider scene around her – a military graveyard full of perfectly identical tombstones.
I was reminded of that scene recently as I reflected on Walter Lippmann and his frustration with the assumption of a discrete public, a singular entity with a mind, a soul and a purpose.
Walter Lippmann doesn’t believe in The Public. Despite what Tyler Durden says, Lippmann thinks you are a beautiful or unique snowflake.
In many ways, this is a fundamental struggle of social science. Yes, every person is unique and we should celebrate diversity and yay, isn’t all that great, but at the end of the day, you can’t do science with 7 billion unique variables.
You have to put people in boxes to make sense of them. You need some groupings to make the process manageable.
So we break people down into general categories. Put people in boxes based on their gender, race, or sexual orientation. As if everyone in each of those boxes is the same. As if its sufficient that they are the same decaying organic matter as everyone else.
And people wonder why there is no predictive social science.
In the natural sciences, you start with a simple system and add more complexity as you learn and understand more. We’re a long way off from being able to treat each person uniquely, but I’m interested in efforts which move in this direction.
Cluster analysis helps identify naturally forming clusters – groups that share common characteristics without presupposing what types of people are similar. Network analysis shows connections – and with those connections can show clusters, flows, central nodes, and isolated nodes.
These promising approaches just begin to scratch the surface of understanding society not as a unitary blob, but as a complex array of individuals.