For the past few years, I have been tracking patterns and ways to measure them. In some easily reproducible settings, like small-group social engagements, short-timeframe teamwork, and the like, patterns are much more useful than individual events at determining how things work out. Especially when the desired outcome is patterned, and real-life outcomes usually are (“make sure everyone leaves happy”, “come up with a solution that addresses everyone’s personal use case well enough”), focusing on natural patterns rather than linear ones* provides for better rules of thumb, and a clearer understanding of why things happen.
Indeed, most common wisdom about why things happen – how causality works, what comes first and what comes next – is simply a version of the post hoc fallacy: if two things happen near eachother, one caused the other. You can see this most eloquently in the history of many sciences. We continue to make this class of mistakes most quantitatively in abuses of statistics today. But the more prominent arena for this sort of thinking is in everyday life – the way we talk and write, the words we use to explain important events to ourselves.
If you look at almost any significant and complex world problem, you will find that both laymen and experts enjoy breaking things down into linear patterns, and choosing a small number to claim as the “key” factors in making or unmaking some change. Climate change, economic collapses, political standoffs.
In my observation, it is rare for there to be much truth in ascribing impact to any small set of such factors. Yet most people I know will, in at least some areas where we lack solid repeatable data, suggest otherwise.
After running some experiments in this area, I am keen on writing something more formal about this, including some language, metrics, and toy examples for working with patterns. I have found a close attention to patterns to be of tremendous personal use, and expect it will come to be so in larger collaborations as well. If you have run across relevant work in this area, or writings on pattern of any sort – human, biological, artistic, mathematical, or other – I should like to hear about it.
* Linear or “single factor” patterns are the simplest kind; and in many if not all cases one could describe all more complex patterns in terms of the interction of linear patterns. However we can usually evaluate a set of natural, more complex patterns with reasonably low error. Forcing a guess at their decomposition into linear ones and at what those linear factors are, and composing those guesses together, is often far more incomplete or uncertain.