[SOUND] [SOUND] In our first class session, we talked about the fact that the two big levers that a leader has are the management of information and the management of motivation. And that these two levers are really critical to being able to make and implement decisions. Today we're going to talk specifically about making decisions, and we're going to talk about how to use information to make decisions more effectively. Making a decision is really about selecting the best available option for getting you to your goal. So we can think about decision making as involving goals, that is the outcomes you want to achieve, and the options or actions for getting you there. We're specifically going to talk about decision analysis today, because decision analysis provides a framework for thinking about making decisions. Decision analysis is a systematic framework for selecting options to get you to your goal. What makes decision analysis difficult, what makes any decision-making difficult, is uncertainty. And uncertainty is captured in the idea of a decision dilemma. A decision dilemma occurs when you make decisions under uncertainty. That is, there are probabilities attached that you have to manage. For example, when you are uncertain what will happen if a particular action is taken. The classic decision dilemma is a choice between an action that leads to a certain outcome and an action that leads to an uncertain outcome. So, we can think about three elements that we have to manage here. The outcomes that we want to achieve, the actions we're going to take to get there, and the probabilities that might be attached to some of these actions or outcomes. Let's take an example to illustrate this. Bob owns a t-shirt store. Bob's hometown team is playing for the championship this weekend. Bob needs to decide whether to print some championship t-shirts to sell to the fans of his hometown, if the team wins the championship. Now, Bob knows that his best chance to sell these shirts is going to be right when the game ends. And so he needs to make a decision about whether or not he prints these shirts and has them ready. The problem for Bob, of course, has to do with uncertainty. What happens if the hometown team loses the championship game, what will happen to the shirts then? Bob has estimated that it will cost about $200 to make the shirts, but he thinks he can sell them for about $600. The local newspaper puts the probability of the hometown team winning at about 30%. What do you think Bob should do? Well, let's look at this, Bob's decision, in terms of decision analysis. First, let's look at Bob's action outcomes. Bob really has two action outcomes to choose from. He can either print the shirts or he can not print the shirts. That's the central choice in Bob's decision. Now, in addition to that, there is some uncertainty here. If Bob prints the shirts, maybe the hometown team wins, maybe the hometown team loses. That's the uncertainty that Bob has to manage in this decision. And of course, there are some outcomes attached. If the hometown team wins and Bob sells the shirts, he's going to make a profit of about $400. If the hometown team loses, and Bob can't sell the shirts, he's going to lose his investment of $200. And then finally, of course, we're can tab some probabilities here. The newspaper said the probability of the hometown team winning was about 30%, which puts the probability of the hometown team losing at 70%. This is what decision analysis looks like. It is a framework for capturing action outcomes, uncertainties, and outcomes to the actions, so that we can try to decide which of the actions we should choose. How does Bob decide? Well, he can calculate the expected outcome for each action option. For "Don't print the shirts," the expected outcome would be $0. Nothing ventured, nothing gained, nothing lost. That's the certain outcome for Bob. He knows if he doesn't print the shirts, he knows exactly what he's going to get. What happens if Bob prints the shirts? The expected outcome, we know, has a 30% chance of making Bob a profit of $400. So 30% chance of $400 is $120. We know there is a 70% chance of Bob losing $200 to print the shirts if the team loses. So that's 70% of minus $200, or minus $140. The sum of all outcomes for print the shirts action option would be the $120 minus $140 or, in other words, $-20. We can look at this in terms of Bob's decision analysis or what we call Bob's decision tree, and this means we can assign an expected outcome to each of the action outcomes. The assigned value for the action outcome of printing the shirts is -$20. The expected value of the action option of not printing the shirts is zero. It looks like it would be a better bet for Bob to not print the shirts. So Bob's decision is don't print the shirts is zero dollars, print the shirts is minus -$20. Zero sounds a lot better than -$20, so Bob decides not to print the shirts. Of course, what we've talked about is the simplest version of decision analysis, where there is a simple choice between one certain outcome and an uncertain outcome. We could expand this to two available action options, some certain, some uncertain. And there could be more than two possible outcomes to consider for each uncertain action alternative. So, decision analysis provides us a systematic way to integrate action options, uncertainty and the probabilities, and the outcomes that we might achieve. This allows us to calculate the expected outcome for each action option and to select the action option with the best expected outcome. Decision analysis is about using this integration of actions and uncertainties and outcomes to be able to arrive at expected values that allow us to select the action options with the best expected value. The problem with decision analysis, of course, is that it assumes a lot. It assumes that we have a lot of information that you may not actually have when you really make decisions. A lot of times, we are not given all the information we need to do a proper decision analysis. So, for example, we aren't given the action options, we invent them. We decide what the choices are we're going to make about what we might be able to do. You might realize in the example that I gave you, Bob actually has more than two options. He could print some shirts, he could do something else with the money. There are lots of other action outcomes. We've simply simplified this by only having him consider a couple. We aren't given action outcomes either. We invent those as well. We might ask, okay, so what's going to happen if we take this particular action? And we don't necessarily contemplate all the possibilities that might occur because that would be pretty difficult. And we aren't typically given probabilities. Maybe, in this case, you get a probability from the newspaper, but the fact of the matter is often we have to estimate those probabilities. So often, it's up to us even to decide even when there is a decision to be made. Think about right now. You could be making a decision right now, but instead you're watching this video. So, the fact is, you have, in some ways, constrained the amount of choice you have to make by deciding, at least for now, you're not going to decide. But in real life, every second provides another opportunity to make a decision, if you decide that's what you want to do. So the problem with decision analysis is that we often have to fill in the blanks. We have to decide what information we have. And we often have to limit the amount of information we use to make decisions.