1) Main insight: It’s easy to overpay for something.

This is true for stocks, baseball players, and movie stars. And ebay auction items.

Knowing this, is a good thing.

But it helps less that you might think – there’s a lot of social factors that push people to overpay.

It’s not a bad insight – but using it is harder than it looks. Like “buy low, sell high”.

2) Stories. Beware the data-miner with a story. Too many of these stories turn out to be excuses for bad policy.

3) Sample limitations. With data-mining, often you get a lot of me-too’s since the model replicates the successes of the sample set. But it doesn’t tell you what other models might work. It turns into “formula”.

And regarding: “do we really want a supercrunching program to promote such music and excuse its supporters from giving reasons for it”

You don’t need any sort of complicated mathematics to knowthat sex and violence sell.

Great post. My only comments is that your critique is primarily normative — that reliance an data will erode our humanity. In some cases that this surely right.

But in a world that rewards results, isn’t the genie out of the proverbial bottle? The tools themselves are here to stay. So let’s use them appropriately.

A fascinating case study was provided by Thomas Schelling (in Micromotives and Macrobehavior), describing the city of Oak Park’s efforts to maintain a diverse neighborhood by prohibiting ‘for sale’ signs, and insuring home owners from any decline in their home value. Free marketers would’ve hated the supposed ‘government intervention,’ but Oak Park’s actions actually helped increase its economic competitiveness, while serving laudible moral goals.