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[SQUEAKING] [RUSTLING] [CLICKING] LILIANE UMUBYEYI: Hi, good morning, everyone. I'm happy and honored to give this introductory session on reparations. But before I start, I would like to give you my disciplinary perspective. The concept of reparations has been studied from different angles and different disciplines, whether it's economy, sociology, law, philosophy. And I would like to give you more details from where I speak and I stand when I talk about this concept of reparation. So during my PhD dissertation, I studied mobilization of apartheid victims in South Africa before American and in South African courts. And within this dissertation, I treated the question of reparations, how this concept was appropriated and used by victims movements, NGOs, lawyers, and different groups of actors. So I'm not-- and after that, I work for different NGOs and human rights organizations mainly in sub-Saharan Africa, an organization, which were which were implementing p...

[SQUEAKING] [RUSTLING] [CLICKING] RICHARD DE NEUFVILLE: So what do we do in practice? The simple approach is to calculate the upper bound. This is known as the expected value of perfect information. So this is where I'm going to be leading you in this exercise. So the expected value of perfect information is totally hypothetical. It does not exist. But it simplifies the analysis and gives you an upper bound, which is useful because we can definitely exclude some tests if they don't meet-- if they exceed the upper bound of the possible value. So the concept is that there is a perfect test, which exactly which event is going to occur. I will call this-- I'm calling this a Cassandra machine. The Greek mythology, not that anybody should necessarily know it, is that this lady was Cassandra who had the gift, so to speak, of always seeing what the future would be and with the curse that nobody would ever believe her. It was all about the battle for Troy, and she coul...

[SQUEAKING] [RUSTLING] [CLICKING] RICHARD DE NEUFVILLE: So now, what if there are many stages? So you just had a 2-stage problem. And the general idea here is, all right-- I mean, a 2-step and one choice that is outcomes. If I want to do more, so for example, in the same question, suppose that you are in-- as a student, you were wondering whether to take the raincoat. Suppose you decide to consult The Weather Channel. So again, for simplicity, let's suppose it has a yes/no answer, it's going to rain or not. In practice, of course, they give you a percentage of, it will have a 40% chance of rain or something, which means you can never tell if they're right or wrong unless you study their results over a long, long period. So it's a yes/no answer. And of course, now, these forecasts say it rains are wrong. That is, they can be wrong. But if they do say rain, you're saying, hmm, I thought it was a 30% chance of rain, but now, hmm, they say rain, so I'm...

[SQUEAKING] [RUSTLING] [CLICKING] RICHARD DE NEUFVILLE: Decision analysis provide a structured, efficient way to help people recognize this complexity, to recognize the probabilities, and to deal with it. So what are the general features of this method? First of all, it offers a simple way of defining the choices. Indeed, it assumes that everything is a discrete choice that is either yes, no, or yes, somewhat, a lot, maybe, not at all. But it has discrete-- it forces you to think in terms of discrete choices, not a continuum. It also enables you, but generally compels you, to look over several periods. That is, it's not just what you do in the first period, but then what you do. Just like going to a course at MIT, you have your choice of first semester courses. And the choices you make then enable you to do certain things or prevent you from doing other things because you didn't take the prerequisite course and so forth so that you want to think about it over seve...

[SQUEAKING] [RUSTLING] [CLICKING] RICHARD DE NEUFVILLE: This is the first of a couple of sessions revolving around decision analysis. And what I want to do here is to introduce a useful technique in this way of thinking about analyzing how we respond to uncertainties. And it's appropriate for simple uncertain situations. I emphasize that. So the other aspect I want to emphasize is that this is a method that was largely popular starting around 40, 50 years ago. It was developed by a person called Howard Raiffa, who was a great exponent of it, and Ralph Keeney, who I taught with. At around that period in the '70s, we did some work together. And it was very popular and was a staple of most analytic business school courses, which it is now not. That-- it took a while, but what has happened is that a number of companies didn't address it. And it is certainly an advantage over not addressing uncertainty. And for example, I know, since I've been working with Chev...

[SQUEAKING] [RUSTLING] [CLICKING] RICHARD DE NEUFVILLE: I want to tie all this in with the discount rate here. Now, when the demand for capacity is growing, say, for electricity, you have two choices. You can build a big plant now for many years ahead. But I'm going to, I'm going to build a plant for the next 20 years. My father had this experience working, actually, in India when they were building the original steel mills about 30 or 40 years ago. The whole pressure was build big plants. Get them really efficient. That's one approach. And the other approach is to build small now and some more smaller units later on, as you phase the capacity according to the growth of the market. So the choice A is based on economies of scale. And the choice B has this particular feature, which I want to emphasize here, is that if you decide to build the capacity when you need it, that is, not all now-- because if you're building for the next 10 years or 15 years, but ma...

[SQUEAKING] [RUSTLING] [CLICKING] RICHARD DE NEUFVILLE: There are two main reasons why we might not want to delay decisions. The main one is, perhaps, economies of scale. And the second one is competitive gaming. So let me address the first one next. But the answer is there are reasons why you don't want flexibility. And here is an economic one. So what are economies of scale? The basic idea is that for many systems, larger units of capacity-- that's what's meant by greater scale-- can deliver lower cost economies per unit of production than smaller units. One big electric plant is cheaper than two smaller plants with a same total capacity. So first of all, let's think about what's the intuition? The common driver for the existence of economies of scale is that the cost of unit capacity is a function of its area. And the production is a function of this volume. Now there are many more subtle aspects of it. But think about it in terms of, say, thermal p...

[SQUEAKING] [RUSTLING] [CLICKING] RICHARD DE NEUFVILLE: So the first portions of it, the title is the "Drivers of Flexibility." That is, through our exploration of the fact that the forecast is always wrong, that there's uncertainty, and the observation that we need to explore what might happen, there are certain factors that make it much more likely that we should have flexibility to not. And on the other hand, there are things that act against it. There are reasons why flexibility is either not desirable or, the most important thing I want to emphasize, why there are certain factors in the practice of engineering, the paradigm of engineering as we have it that act against the idea that we should be flexible. And this is what this topic is about. That is, what are the issues that are for and against flexibility and how we might deal with them? We can come back to some of the implications later on. But for the moment, I want to address these five issues. So ...

[SQUEAKING] [RUSTLING] [CLICKING] RICHARD DE NEUFVILLE: Now, the bidding process was two stages. There was a big penalty if you kept it but didn't develop it. Why would that be? Well, because what would happen is, a company might want to say, well, I will acquire the site, but then I'm a big copper company and I have assets elsewhere, I won't use it right away. I'll use my other assets and only use this one and spend the money to develop this particular project when it's worthwhile for me. Now, the country that owned it, Peru, was not interested in that because they were interested in having the jobs. They were interested in having the exports. They were interested in having investment. So they wanted to be sure that the company that bid for it would, in fact, either develop it seriously or walk away from it if it turns out not to be geologically or, in terms of timing, to be interesting. So the timeline was, you could explore the geology and the topog...

[SQUEAKING] [RUSTLING] [CLICKING] RICHARD DE NEUFVILLE: We don't have a way of calculating when's the optimal time. There are some methods for dealing with that, such as dynamic programming. But dynamic programming implies there's nothing you can do about it to affect future aspects. This is the so-called path independence function. And the whole point of having flexibility is precisely that you can do things so that that powerful method does not work for us. And there are so many paths. If we think about 2,000 samples of what may happen, that's only 2,000 samples of what may be a million possibility of paths as a combination of the different probabilities at different times of prices and quantities and so forth. It's much too large to be searched. So the procedure is to define what we know as decision rules, the a priori condition for when to exercise flexibility. These a priori conditions may be set by higher-level mandate. This is standard operating...

[SQUEAKING] [RUSTLING] [CLICKING] RICHARD DE NEUFVILLE: Now, given that you have a valuation model, given that you have uncertainties, you can see what the outcome, the performance of your system is when there's no flexibility-- simply saying, OK, if we build this plant and then see what the revenues are, we can think about, well, our production may go up and down because of mechanical problems or whatever. The demand may go up and down, depending upon competition or whatever. And all together, we may have the following results in terms of benefits and costs. To do this, you generate a dynamic scenario and apply it to the model period by period. That is, the demand starts at one level, goes up, goes up, goes down, up, down, whatever. And you do that for all the different demand possibilities that you simulate, and then you can think about-- that would also happen in terms of production possibilities. And for each of those scenarios, combination nation of the variables...

[SQUEAKING] [RUSTLING] [CLICKING] RICHARD DE NEUFVILLE: So I recommended a process of how to approach. The five steps, they're one way to think about it. You can have six or seven. In the text we wrote some time ago, we had seven put out because we went to more complexity. But I'm now going to talk about these central five but it's mostly to cover the basics. Five is not the magic number. So the first one we need a valuation model. And for those of you who are filling out your initial project form, a number of you weren't quite understood that what was involved when I asked about having a model of your system. But you need to have some way of saying, OK, if I design the model or I set up my model in my system in a particular place, which is not only the machinery and the production of a process but also my employees, my marketing, my sales, my productivity, and so forth, you need to have a way of transforming those assets and [? ERUs ?] into some kind of v...

[SQUEAKING] [RUSTLING] [CLICKING] RICHARD DE NEUFVILLE: What is simulation? Simulation is a way of replicating the outcomes of an uncertain process. That is, we know the forecast may be wrong, we're looking at different possibilities, and if this were to happen in the terms of the forecast in, say, the demand for cars or the amount of liquid fuels consumed, here's the way we would deal-- here's the way the system would deal with it, here's the value of the system or the sales or the losses or whatever. It's often called Monte Carlo simulation. Why is this? Because Monte Carlo is a European gambling hub. It's the Macau or the Las Vegas of the-- actually of the 1900s and 1920s. It was the place at that time, and therefore, it is designed to show about-- it's associated with luck and chance and uncertainty. That's why it's called Monte Carlo simulation as opposed to a deterministic simulation. So we don't use the Monte Carlo very often...

[SQUEAKING] [RUSTLING] [CLICKING] RICHARD DE NEUFVILLE: At this point, I'm going to talk about the third element of the garage case. We already started with the idea of what the deterministic engineering design was, then we saw how that analysis led to a wrong estimate of the value and, in fact, the wrong design once we accounted for the reality of uncertainty. Now let's think how we can be proactive about this design, and we're going to do that by introducing flexibility. What does that mean? The flexibility is the ability to adjust the project to the actual needs, according to how the future develops. In particular, it means that if the future is very positive in terms of demand and you'd like to have greater capacity, that you have the capability of adding that if needed. It also presumes that we are managing this project. We just don't create it and say, there. It's off on its own. It's on autopilot. No. We have a system where we manage it ...