[MUSIC] The alternative to hierarchy, as a means of making decisions, is what we're going to call collective wisdom. Now there's lots of other words that we could have put into that box. Sometimes we use the term crowdsourcing, for example, or the wisdom of crowds. They all mean basically the same thing. And I'll just give you sort of a dictionary definition. Which is the collective wisdom is the aggregation of inputs from a large number of individuals. In other words, rather than one individual at the top of the organization making a decision, collective wisdom says that a large number of people either, the bottom of the organization or indeed outside the boundaries of the organization their views are taken into account, we aggregate their views. And we come to a collective view that is in many cases better than that of the individual at the top. There are two simple examples to illustrate the principle of collective wisdom. One is Wikipedia. We all know Wikipedia. If you think about it, what Wikipedia is is the aggregation of lots of people's little pearls of wisdom, nuggets of knowledge that have been collected together in a single place over, over a period of time. And of course, the Wikipedia content is vastly superior to that, that any single encyclopedia like the old encyclopedia Britannica could've put together. Another example you know, partly, partly in fun is the famous quiz show, Who Wants to be a Millionaire, which has now been syndicated to most of the countries in the world. If you ever watched that show you know that they person who is in the, in the hot seat has to choose a number of options if they don't think they know the answer to the question. One is to ask the audience. To actually ask the people who are assembled there what they think. Which is a version of collective wisdom. Another option they've got is to phone a friend. In other words, to ask one friend of theirs who's a real expert. And it turns out, when the numbers are done on this, it turns out that the collective wisdom approach, asking the audience, is right about 90% of the time. Whereas the friend, the learned expert who's been chosen specially gets it right about 65% of the time. So as the slide says, under certain conditions collective wisdom can produce more accurate forecasts and better decisions than those from a small number of experts. But it's only under certain circumstances, and it is kind of interesting just to play out. That example from the millionaire quiz show that the different questions are asked of different people at different times. So, early on in the, in that contest if it's a question about a, a soap opera, for example you would, you typically would ask the audience. If it's a difficult question about some obscure point in history. That's when you'd phone the friend. So there are certain circumstances where the learned expert gets it right. And there are certain circumstances where asking lots and lots of different people is the superior approach. Now just to play out that point in a bit more detail, because it does get quite complicated. We can actually identify three different forms of collective wisdom, and they're subtly different. So this slide shows the three forms. On the left hand side, you've got what we can call crowd or commons-based production. This is where we aggregate the input from many, many people, from a crowd, and we create some sort of viable output. Some sort of product. You know, Wikipedia is a great example of that, and there are also lots of, lots of other products which have been created through such methods. Open source software, arguably, is also crowd or commons-based production. In the middle we've got crowd-based decision making. So this is analogous to the millionaire quiz show and in this situation, you know, the person on the hot seat is still making the decision. But they are tapping into the views of the many people before making their ultimate choice. And for example, there's lots of prediction markets out there. For example, if you live in the U.S. before the general election, you can bet on which person's going to win the, win the next election. And obviously the odds there are based on what people are betting, and we actually end up with a clearer prediction of who's likely to become the next president, through those of the prediction markets than we do by actually listening to some sort of pollsters. So that's a different model, that's aggregating views to help somebody come to a decision. And then the third and final one, on the right hand side, is what we can call collective buy-in. So this isn't actually about either aggregating to create something. Neither is it about just getting views. It's actually about getting people to act differently. It's actually about getting people to agree to a path of action and actually following a point. And I'm going to give you an example shortly about a company, a software company called Red Hat who used a crowd-based process to enable not just, buy-in, but also sort of commitment and execution to a particular strategy. So, the whole point is that there are three different sort of species of collective wisdom, depending on what you're trying to achieve in an organisation. So, we're going to show you a little video in the classroom by a colleague, a friend of mine called Gary Hamel. He's a visiting professor at London Business School, and his clip is going to just give us one particular example of this notion of collected wisdom, how we tap into the views of many people. The ideas that people have across an organization in order to get new programs started. And he's not just got a point of view about how we tap into those views. He's also going to suggest a mechanism, a market-like mechanism, for how we decide which of those views to ultimately invest in and take forward.
