Best Analysis with Ensembles

Promoting multiple (aka Ensemble) methods.  New book. Have for years used what are now called ensemble methods.  This piece gives good motivation for its use.  Passing it along.   Podcast and transcript.

Ensemble Models
How to Get the Best Results from Big Data Analysis
Author Scott E. Page, a complex systems expert, explains how applying multiple data analysis models greatly enhances decision making.

Scott E. Page, professor of complex systems, political science and economics at the University of Michigan, doesn’t want people to limit themselves to linear thinking. In his new book, The Model Thinker: What You Need to Know to Make Data Work for You, he explains how taking a multi-paradigm approach puts more power into solving problems, innovating and understanding the full range of consequences to complex actions. He believes using many models is the best way to make sense out of the reams of data available in today’s digital world. Page recently spoke on the Knowledge@Wharton radio show on Sirius XM about why it’s important to widen your data lens.  

 An edited transcript of the conversation follows.

Knowledge@Wharton: What is multi-model thinking?

Scott Page: We live in this time where there are two fundamental things going on. One is, there’s just a firehose or hairball of data, right? Tons of data out there. At the same time, we have this recognition that the problems and challenges that we confront are complex. And by that, I mean high-dimensional, lots of interdependencies, difficult to understand. So, what do we do? How do we use that data to confront the complexity?

The philosophy I’m putting forward goes as follows: You have to arrange that data on some sort of model. You want to think of a model as Charlie Munger, the famous investor, describes it — a latticework of understanding on which you can array the data.

But models by definition are simple, so there’s a disconnect. I’m trying to understand something complex with something that’s simple. What I’ve bought with that simplicity is logical coherence. But what I’ve lost in that simplicity is any notion of coverage because there’s too much stuff I’ve got to leave out.

Instead, what I propose you do is bring an ensemble of models to bear. This is a thing. People in machine learning have been doing this; all the fancy stuff’s going on in AI. If you really unpack what’s going on in those sophisticated algorithms, they really are ensembles of little algorithms and little rules. The idea is, any one model is going to be wrong, but many models are going to be not only a lot of coverage, but also a collection of coherent understandings of a complex phenomenon.

Knowledge@Wharton: Is this multi-model approach common in the business world? ... 
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