Some organisations use models without knowing it. Most of the companies and organisations think in models. For example, a yield curve, which compares bonds with the same risk profile but different maturity dates, can be considered a model. A hiring rubric is also a kind of model. When you write down the features that make a job candidate worth hiring, you’re creating a model that takes data about the candidate and turns it into a recommendation about whether or not to hire that person. The most sophisticated organisations, from Alphabet to Berkshire Hathaway to the CIA, all use models. In fact, they do something even better: they use many models in combination.
Data helps describe reality, albeit imperfectly. Without models, making sense of data is hard. Though single models can perform well, ensembles of models work even better. That is why the best thinkers, the most accurate predictors, and the most effective design teams use ensembles of models. They are what the author of this piece calls, many-model thinkers. With an ensemble of models, you can make up for the gaps in any one of the models. Constructing the best ensemble of models requires thought and effort. As it turns out, the most accurate ensembles of models do not consist of the highest performing individual models. You should not, therefore, run a horse race among candidate models and choose the four top finishers. Instead, you want to combine diverse models.
But, Which models does one include, and which does one leave out? How should that ensemble look like? The first guideline for building an ensemble is to look for models that focus attention on different parts of a problem or on different processes. Your second model should include different variables. The second guideline borrows the concept of boosting, a technique from machine learning. Ensemble classification algorithms, such as random forest models consist of a collection of simple decision trees. A decision tree classifying potential venture capital investments might say “if the market is large, invest.” In sum, models, like humans, make mistakes because they fail to pay attention to relevant variables or interactions. Many-model thinking overcomes the failures of attention of any one model. It will make you canny.