Hi, in this set of lectures we are going to talk about problem solving. We’re going to talk about individuals and teams go about solving problems. and we’re going to focus on a couple of things — We're going to talk about the role that diversity plays in problem solving, and we’re also going to talk about how ideas can get recombined, and how a lot of innovation actually comes from somebody having an idea in one place and it being applied someplace else. So, those are going to be the two main themes: The role of diversity, and the power of recombination. So to get there, think about how we model problem solving: We got to start off by making it more formal, constructing a somewhat formal model. So here is how we are going to do it: We are going to assume that you take some sort of action (a), where you have some sort of solution we’ll represent by (a), and there's a payout function (F), that gives you the value of that particular action. So that action could be a particular string of code if you are writing computer code, and (F) might be how fast that code runs. Alternatively, (a) could be a health care policy and (F) would be how efficient that health care policy is. So, (a) is the solution that you propose and F(a) (F of a) is how good the solution is. What we want to do is to have some kind of an understanding [of] how people come up with better solutions — where innovation comes from. So to do that we are going to invoke a metaphor. And we are going to use this 'metaphor of a landscape' as a lens through which to interpret our models. Okay, so, think about it in the following way: You are trying to come up with some solution to a problem, and each solution has a value. So the altitude here is the value of it. So, B is the best possible solution. Now, along here on the X-axis, these are all the different solutions. So I might start out by having some Idea (I) (Let’s just put it right here, and here’s my idea.) And it's an okay idea; but we’d like to think about “How do we find better ideas?” So one think we might do is [we] might “try things to the left and the right,” and realize that “climb uphill” here and we get to some point (C); and (C) might be where I get stuck, because if I go to the left I’m lower, and if I go to the right I’m lower, so I could say “Wait, (C) is the best thing I can come up with.” What we want to see is how people come up with these ideas, how teams of people come up with better ideas, and how we can avoid getting stuck on (C), and possibly getting ourselve[s] up to (B). How are we going to do it? Well, here’s what the model is going to look like: We are going to start out by talking about something I am going to call ‘perspectives’. What is a perspective? Perspective is how you represent a problem. So if someone poses some problem to you— again, whether it is code, health care policy, designing a bycicle, or designing an addition to you house— you have some way of representing that problem in your head. That's going to be a perspective — it’s literally how you encode the problem. Once you have encoded the problem, what you do is you create—again, this is metaphorically—a ‘landscape’. as if you can think of your encoding is like that horizontal axis, and that there is a value for each possible solution, and that creates a landscape. So we are going to talk about how different perspectives give different landscapes. That is the first part. [The] second part is something I’m going to call ‘heuristics’. Heuristics are how you move on the landscape. So, remember when I drew that landscape, I talked about climbing up the 'hill'. Well, 'Hill Climbing' is one heuristic. 'Random Search' would be another heuristic — if you just randomly pick some points and then find which one is where the highest value is, that is another heuristic. So we will talk about how different perspectives and different heruistics allow people to find a better or improving solutions to problems. So that is going to be the focus of our model of problem solving: People have perspectives, and people have heuristics. Once we finish talking about individuals, then we will talk about teams. One of the interesting things here is if you have groups of people or [a] team of people solving a problem. You actually can show that they will be better than the individuals in it. And the reason why is because they have more tools, and those tools tend to be diverse. So they have different perspectives and different heuristics, and all that diversity makes them better coming up with new solutions and better solutions to problems. So, teams are going to be important. After we have talked about teams, and after we have talked about the role of how one person can improve upon the solution of another, we are going to extend our model a little bit and talk about recombination. So here is sort of the big idea. The big idea is this: I have some solution from one problem, you have a solution from a different problem, and sometimes I can take your solution and combine it with my solution, and come up with something even better. So, the thing about sophisticated products— like a house, an automobile, or even a computer— that consists of all sorts of solutions to sub-problems. And we are going to see how by recombining solutions to sub-problems we get ever better solutions, and that is really a big driver of innovation. So let us think back for a second — Remember in our previous lecture, we talked about how without sustained innovation we no longer get growth, that growth depends on sustained innovation. What we’re going to talk about here is how diversity leads to innovations, and how recombinations of innovations can lead to even more Innovations. So that is the big theme — So that is where we are headed: We are going to start by talking about perspectives. Then we will talk about heuristics. Then we will talk about how teams of people can leverage their diverse perspectives in heuristics. And then we’ll talk about recombining ideas can really drive a lot of growth. All right, let’s get started. Thank you!