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Tag: Algorithms

Nate Silver Is King, Long Live Nate Silver

My twitter stream is awash in math this morning, cheering Nate Silver’s exceptional forecasting (“Triumph of the Nerds: Nate Silver Wins In Fifty States”, Chris Taylor wrote), and celebrating the victory of math and big data over pompous punditry.  Jeff Greenfield tweeted, “I, for one, welcome our new Algorithmic Overlord.”

At some level, I thrill to the ascendancy of math, and of math nerds – and I write this as a proud former math team captain (and math team T-shirt designer), and as someone whose very best summers as a teenager were spent in math (and writing) camp at Duke University.  It’s also one of the reasons I love Silicon Valley so much – it’s where nerds rule, and where even emerging VCs promote themselves as “Geeks.”

However, before we turn all of life over to algorithms, as some are suggesting, it’s important to place the election prediction in context.

The accomplishment of Silver’s splendid forecasting was to intelligently aggregate existing data, to accurately summarize the current, expressed intentions of the national electorate.  And we’ve learned that careful analysis is far more useful than blustery experts – something Philip Tetlock has been trying to tell us for years.

At the same time, all forecasting challenges are not created equal, and summarizing current public opinion is a much lower bar than predicting events far into the future – and Silver has been clear about this; it’s others who seem to be leaping ahead.

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Do You Believe Doctors Are Systems, My Friends?

In the current issue of The New Yorker, surgeon Atul Gawande provocatively suggests that medicine needs to become more like The Cheesecake Factory – more standardized, better quality control, with a touch of room for slight customization and innovation.

The basic premise, of course, isn’t new, and seems closely aligned with what I’ve heard articulated from a range of policy experts (such as Arnold Milstein) and management experts (such as Clayton Christensen, specifically in his book The Innovator’s Prescription).

The core of the argument is this: the traditional idea that your doctor is an expert who knows what’s best for you is likely wrong, and is both dangerous and costly.  Instead, for most conditions, there are a clear set of guidelines, perhaps even algorithms, that should guide care, and by not following these pathways, patients are subjected to what amounts to arbitrary, whimsical care that in many cases is unnecessary and sometimes even harmful – and often with the best of intentions.

According to this view, the goal of medicine should be to standardize where possible, to the point where something like 90% of all care can be managed by algorithms – ideally, according to many, not requiring a physician’s involvement at all (most care would be administered by lower-cost providers).  A small number of physicians still would be required for the difficult cases – and to develop new algorithms.

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