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