It should come as no surprise to people on this list that models often outperform experts. But these are generally finely calibrated models, integrating huge amounts of data, so this seems less surprising. How can the poor experts compete against that?
But sometimes the models are much simpler than that, and still perform better. For instance, the models could be linear, rather than having higher order complexities. These models can still outperform experts, because in practice, despite their beliefs that they are doing a non-linear task, expert decisions can often best be modelled as being entirely linear.
But surely the weights of the linear models are subtle and need to be set exactly? Not really. It seems that if you take a linear model, and weigh the variables by +1 or -1 depending on whether it has a positive or negative impact on the result, then you will get a model that still often outperforms experts. These models with ±1 weights are called improper linear models.
What's going on here? Well, there's been a bit of a dodge. I've been talking about "taking" a linear model, with "variables", and weighing the factors depending on a positive or negative "impact". And to do all that, you need experts. They are the ones that know which variables are important, and know the direction (positive or negative) in which they impact the result. They don't choose these variables by just taking random possibilities and then figuring out what the direction is. Instead, they understand the situation, to some extent, and choose important variables.
So that's the real role of the expert here: knowing what should go into the model, what really makes the underlying dependent variable change. Selecting and coding the variable information, in the terms that are often used.
But, just as experts can be very good at that task, they are human, and humans are terrible at integrating lots of information together. So, having selected the variables, they get regularly outperformed by proper linear models. And when you add the fact that the experts have selected variables of comparable importance, and that these variables are often correlated with each other, it's not surprising that they get outperformed by improper linear models as well.
If I'm not mistaken, a similar principle is at work in explaining why Random Forests / Extremely Randomized Trees empirically work so well on machine learning tasks (and why they also seem to be fairly robust to numerous irrelevant variables). They aren't linear models in terms of the original variables, but if each tree is a new variable than the collection of trees is a linear model of equally weighted predictors.
Maybe. The explanation I've seen floated is that the tree methods are exploiting nearest-neighbor effects with adaptive distances; maybe that winds up being about the same thing.