Google.org

Interesting piece here from The Economist about Dr Larry Brilliant, head of Google.org. Google.org is the philanthropic division of Google. According to the article, when it was conceived in 2004 Google hoped that this division would one day “eclipse Google itself in overall world impact by ambitiously applying innovation and significant resources to the largest of the world's problems”.

What's interesting about this article is its description of how Google.org has developed its strategy. It's taken Larry Brilliant two years to work out what Google.org is going to do. They started with 1,000 ideas, narrowed them down to 11 (dealing with the "biggest, most imminent, least well resourced problems”). Each of the 11 got one member of the team to act as its advocate. These have now been reduced to the 5 final initiatives. These 5 were chosen in part by thinking about how much impact Google itself could have on them.

Interestingly, the final 5 initiatives span 3 areas: fighting global climate change; economic development; and early warning for global pandemics.

I'd love to know more about the process they used to cull their ideas from 1,000 to 5.

Roger Martin and Strategic Choice Structuring

I've just re-read a great 1997 paper by Roger Martin, "Strategic Choice Structuring". There's a lot in this, but it's worth reading just to be reminded that strategy is all about choice. And good strategy is all about good choices.

And, as Martin says in this paper, you need to make "genuine" choices for your strategy to be sound. A genuine choice entails not just what you will be doing, but also what you won't be doing. For example, a strategy that will "focus on the customer" is no strategy at all - because it is no choice at all. As Martin says "could the company really have decided otherwise? Could it ever truly choose to ignore the customer?"

"Expectancy bias" in the USS Vincennes shootdown

I've just started dipping into what looks like a great book: "Sources of Power - How people make decisions" (Gary Klein, MIT Press, 1999). It has a fascinating chapter on the shooting down of an Iranian commercial jetliner in 1988 by the USS Vincennes.

Klein says that some decision making experts see this terrible incident as an example of "expectancy bias" (pp 84ff). Specifically, the crew in the Vincennes' Combat Information Centre reported seeing the unidentified aircraft descending towards the ship - in a pattern they saw as being aggressive. In fact - the data shows that the aircraft was always ascending through all stages of its short flight (it was shot down only 7 minutes after take-off).

What explains this mis-reading of the flight of the airliner? The decision making experts mentioned by Klein think the Vincennes crew were already convinced that this was an enemy jet. Why? Just after take-off there had been a (later shown to be false) IFF reading that the airliner was an enemy jet - and not a civilian plane (the full account of the whole incident in this chapter is worth reading). Because of this belief, the experts claim, the crew then started to see the data they expected to see: the data that supported the scenario they had already decided was occurring. A classic case of "expectancy bias".

Klein is not so sure. He asks the question - what would have happened if the Vincennes had not shot down the unidentified aircraft and it turned out to be an enemy jet that then successfully attacked them? Well - over the preceding month there had been 150 challenges issued by US ships in the Gulf to presumed enemy aircraft. In over 80% of those cases the aircraft involved turned out to be Iranian military (and only 1.3% turned out to be commercial airliners). In other words, if the Vincennes had not acted, and it did turn out to be the wrong call (that is - the aircraft attacked them) - then this decision could also be explained by "expectancy bias". It's just that in this case the bias would have been different - a bias against the available data that these unidentified jets turn out to be Iranian 80% of the time.

This is a great insight from Klein. "Expectancy bias" explains why the Vincennes shot down the airliner. But "expectancy bias" would also have explained the opposite scenario - the failure to shoot down an enemy jet that then successfully attacked. In Klein's words expectancy bias "explains too much" (p85). A theory that explains too much is no theory at all.

So why did the Vincennes make such a tragically wrong decision? Well, it seems that after a terrible chain of events, it was at the end a simple failure of information design. On the Vincennes' computers, the altitude data for nearby aircraft was contained in a small display off to the side. And critically, it did not display a trend: crew-members had to remember the previous altitudes of the jet so as to see if it was going up or coming down. In a pressured situation - over a short period of time - this turned out to be too hard.