Speaker 1:	My problem in introducing Barry is that there's so many choices of things to talk about in his background, that no matter what I say, I'm going to be disappointed later, you know? 
Speaker 2:	[inaudible 00:00:15]
Speaker 1:	It's also a little daunting to introduce someone who has his own Wikipedia page, has been on the Colbert Report and has three Ted Talks posted online. Barry's been a professor of psychology at Swarthmore since 1971. I understand that he's working hard on being the longest tenured faculty member at the college so luckily he won't be leaving any time soon. He received his BA from NYU, his PhD from the University of Pennsylvania. He has chaired the psychology department, served as associate provost and attributed his insights to multiple committees and working groups over the years. As the Dorwin Cartwright professor of social theory and social action, Barry teaches courses ranging from introduction to psychology to thinking, judgment and decision making to the course he and Ken Sharp have co-designed and are co-teaching again this semester entitled "practical wisdom." 
	When I first started teaching at Swarthmore, I had students read chapters from a book that was co-authored by Barry and Hugh Lacey entitled Behaviorism, Science and Human Nature, a text which examined the limits of behaviorism through lenses of both psychology and philosophy. In later work, Barry has explored issues of morality, decision making and the intersections between science and society. His books include The Battle for Human Nature: Science, Morality and Modern Life; The Cost of Living: How Market Freedom Erodes the Best Things in Life and The Paradox of Choice. His latest book was released this past December, co-authored with Ken Sharp and it is entitled Practical Wisdom. You may have caught Barry and Ken on NPR in various different programs over the last couple of months as they make the rounds explaining their work. 
	Today Barry will, again, help us explore the connections between our own lives and the world in which we live in a talk entitled, "Living in a radically uncertain world: How to think about it and what to do about it." 
Barry:	Thank you very much. I'm going to talk to you today about something that has sort of been a peripheral interest of mine partly because doing it right would require that you really know something about mathematics, which I don't, so I've kind of done it wrong, although I had a collaborator who I think did it sort of right, so I think, however, that the idea that I'm going to try to communicate to you is actually kind of an important one, and it's an idea that people have known about for a very long time and not acted on, and my hope is that actually people will start acting on it. It does bear a relation to an idea from the book The Paradox of Choice, and some of you who are familiar with that book may see the connection eventually. 
	There's a paper on this talk that will be out in the next issue of a journal called The Journal For the Theory of Social Behavior. My collaborators, [Yakov 00:03:23] [Benheim 00:03:24] is a structural engineer and mathematician and Cliff [Daxo 00:03:27] is a professor of internal medicine. Strange collaboration but all of the math is Benheim's and so he is to be blamed for all of it, and it's published in this journal because it's a journal that nobody reads, so we don't have to worry about being taken to task. 
	Okay, so, in order to explain, to make my point to you, I need first to make a very quick tour through a kind of cartoon version of the theory of rational choice. This is the sort of normative view about how people are supposed to make decisions in their lives. If you have a decision to make, in a second I'll give you an example of choosing a college but you can imagine choosing a place to go on vacation, choosing a restaurant, choosing pretty much anything. What you're supposed to do is identify relevant features of each alternative, assign each of those features an importance weight. Not everything is equally important. The color of the napkins in the restaurant is probably less important than the items on the menu. Assign a value to each alternative. How good is the color of the napkins or the roast chicken? 
	Then the utility of any option is simply the weighted sum of its values, and so you create a great, big Excel spreadsheet in your mind, do these calculations, and out pops the answer to, "What school should I go to? What restaurant should I eat in? What dish should I order from the menu? Who should I marry?" It's a very multi-purpose framework for making decisions, but it's a little bit more complicated than that, although that may already strike you as rather complicated, because most of the time, most of the dimension of the decisions we face will produce only probabilistic, not certain results. 
	If you order the chicken, you can't be certain that it's going to taste as good as you expect it to. It will taste that good with some probability, and so, for each cell in that huge spreadsheet, you need to multiply the value that you assign by the probability that that will actually happen. Expected utility is value times probability. Actually that's not right. Expected value is value times probably because there's still another complication. This is how you compute expected value for those of you who are really mathematically challenged. If you have a choice between 10 dollars worth of probability of .75 and 20 dollars worth of probability of .4, which option should you choose? The way you are supposed to figure that out is by imagining that you are going to face this gamble again and again and again and figure out how much on average each of the options would get you and you choose the one that has the higher expected value, which is the 20 dollars worth of probability of .4. 
	There's still another complication because expected value is not the same as expected utility since, as the economists have been teaching us since the beginning of time, we seem to obey what you might call the principle of diminishing marginal utility. Not every dollar is as important as every other dollar, although by looking at investment banker behavior, you might wonder about whether that's true, and so there is the objective, expected value and the subjective, expected utility and the curve relating utility to value is a curve. It's not linear. 
	So, this is how you're supposed to make decisions. If you are choosing a college, and I put this up because this is the example I use with my students, you might do something like this. You list all of the colleges you're considering down the side. You list all of the factors that you think are relevant across the top. It gives you lots and lots of cells, and each cell needs to have a weight. How important is size, how important is reputation and so on. A value, how good is Harvard? How good is Swarthmore and a probability. How sure am I that Harvard is as good as it might be? That Swarthmore's as good as it might be, and so on. 
	Once you have done all of this hard work filling out the table, the rest is trivial. You just do the math. A little bit of multiplying, a lot of adding, and you go to whichever place actually will take you. Okay, so this is the framework of rational choice and when I put this up in my decision making class, I asked the students in the class how many of them actually chose their college by going through a procedure like this. The mean number of people who say that they do it this way is zero, which is right away a sign that maybe there is something rotten in the state of rational choice theory, but I want to make things a little bit more complicated by asking us to think carefully about how we go about assigning probabilities to outcomes. That's really what the talk is about. What does it mean to distinguish probabilistic uncertainty from what I have called radical uncertainty? 
	I'll give you a sense of what this distinction is by describing a famous experiment that was done by Daniel Ellsberg. Yes, the same Daniel Ellsberg who having done this experiment decided he has made his mark in the world of decision theory and went on to publish the Pentagon papers and changed his life. Here's the experiment. There are two urns. The urn on the left has 50 red balls and 50 blue balls. You're supposed to close your eyes, reach in and pick a ball. If you pick a red ball, you get a hundred bucks. If you pick a blue ball, you get nothing. The urn on the right has a hundred balls. Red and blue. You close your eyes, you reach in, you pick out a ball. If you pick a red ball, you get a hundred bucks, and if you pick a blue ball, you get nothing. 
	Ellsberg presents people with these two kinds of urns and asks which urn would they prefer to pick a ball from, so which urn would you prefer to pick a ball from? A. Why? What's the probability that you will win if you pick a ball from A? 50 percent, okay. We are mathematically literate. What's the probability that you'll win if you pick a ball from B? Right. Urn B is a kind of schematic of what it means to say that some outcome is radically uncertain. The probability of winning if you pick from urn B could be anything from zero to a hundred percent. You don't know, nor is there any way you can know, and people vastly prefer choosing in situations that are probabilistically uncertain, that is urn A, instead of situations that are radically uncertain, urn B. 
	Now, Ellsberg did this 50 years ago and it actually is [inaudible 00:10:48] him, Frank Knight made this very point in 1921. Very distinguished economist, but it has not, as near as I can tell, had much of an impact on the way in which people actually go about making decisions or even formulate guidelines about how people should go about making decisions. The urn B is an urn where I call the outcome radically uncertain, so let's contrast probabilistic uncertainty with radical uncertainty by unpacking what probability means. There are three different kinds of meaning that probability can have, depending on the space that you're using probability to describe. 
	The first is sometimes called probability understood logically. If you are flipping coins, you can specify quite precisely what the probability is of every possible sequence of heads and tails. If you are throwing dice, you can specify quite precisely what the probability is of every sum on the two dice. If you're picking cards, you can specify quite precisely the probability of picking the five of spades, the probability of picking a spade, the probability of picking a black card, so probability is sort of formulated to capture spaces in which we can specify fully all of the possible results that we're trying to attach probabilities to. 
	Then there is probability as frequency, and here, and this captures ... There are not a whole lot of coin flips, dice throws or card pulls that characterize the decisions we make in our lives but the second notion, probability as frequency, is more common. The more often something has happened in the past, the more likely it is to happen in the future. This seems perfectly plausible. I'd say there's a 50 percent chance I got an A, says the senior statistics student after his twelfth quiz. What might he mean by that when he says there's a 50 percent chance? What he means is that, when he says there's a 50 percent chance? What he means is that I've had experience taking quizzes in the past. When I have felt like this having come out of quizzes in the past, I have gotten an A most of the time, and so I am making a prediction about the future based upon my experience in the past. 
	This works okay as long as there is some reason to have confidence that the future is actually going to be relevantly like the past, and this is the sense of probability that we mostly use, so in the case of meteorologists who are telling you that there's a 30 percent chance of snow, almost certainly what they're doing is using past results when the various factors that influence precipitation in the atmosphere have aligned in the way they have now. Using past results to predict future behavior, and because until global warming, it was reasonable to think that the future was like the past. It's perfectly reasonable to use frequencies in the past as estimates of probability in the future, okay? 
	The third sense of probability is probability as strength of belief, and so when that freshman says there's a 50 percent chance I got an A and it's a Freshman statistics student after the very first quiz, what the hell does that mean? What does it mean to say, "There's a 50 percent chance I got an A?" It is certainly not like throwing dice. It is certainly not using the past to predict the future in any straightforward way. What is it? It's a sign of the strength of your conviction about what's going to happen. 
	Now, it's okay for you to have strong convictions or weak ones about what's going to happen, but when you attach a probability to these convictions, you are assigning a kind of precision that the basis of the prediction doesn't warrant. I really believe that this is going to happen. Nine times out of 10, this is going to happen. The Yankees are really going to win the World Series this year. You know, if you're one of these people who does sophisticated statistical analysis of baseball then you're kind of like a meteorologist of baseball and I take you seriously. If you're a six year old living in the shadow of Yankee Stadium, it means nothing. 
	The reason I'm emphasizing this is that when people assign probabilities to outcomes, it's important to know on what basis those probabilities are being assigned, and when we assign probabilities based on the strength of belief, we may often be taking what is in fact a radically uncertain situation and translating it into a probabilistically uncertain situation. How else could you attach a probability to it? And in that way, misleading ourselves about how certain we are about what the future's going to look like and acting on the basis of really false precision, so there are people who acknowledge all of this, and think the problem is that we just don't have enough time to gather all the information we need to turn strength of belief into probability as frequency or probability as logical. 
	Given enough time and enough data, this last category would go away and be absorbed by one of the other two. I think that's not right. I think that really often, radical uncertainty is an epistemological condition, not a psychological one. It's not that we haven't had the time to gather the information. It's that the information isn't there to be gathered. If you are operating in a world in which that's true, then how can you possibly adopt the standards of rational choice theory, which tells you to multiply for every option the value of that option times its probability of occurrence. That is simply not specifiable because you can't really attach a probability to each occurrence. If you can't attach a probability to each occurrence, how the hell are you supposed to make decisions? Do you just ignore uncertainty altogether and assume that whatever it is is definitely gonna happen? That  doesn't seem all that smart. 
	Well, if you can't do it probabilistically and it would be foolish to do it certainly, what is the alternative? Here's the alternative. Let's go back to choosing a college. Suppose that you have completely ... You'll change your mind, of course, once you start studying, but you've completely fallen in love with microbiology. You visit Swarthmore and you know, the person who does microbiology at Swarthmore, you just think is the best. Exciting, smart, welcoming. If I could spend my life studying with this person, I would be a happy person indeed, and then you go and visit stayed, boring Princeton, and there, there's nobody who really turns you on the way the microbiologist at Swarthmore does, but Princeton, it turns out, has more than one of them, so your question is, where do you go to college? 
	If you are out to maximize utility, then you go ... The highest value is attached to the Swarthmore microbiologist, and then you say, "What are the chances that she'll still be here? What are the chances that she'll still be interested in microbiology?" You know? What happens if I put all of my hopes in this one person and she disappoints me by leaving? That's a lot of hopes to put on one person. How likely is it that she's going to leave? Is it like throwing dice? I don't think so. Is it like the weather? I don't think so, so what the hell is it like? It's like I don't know how likely it is that she's going to leave. 
	Social life. What's the likelihood I'm going to like my roommate? Is that like throwing dice? Is that like the weather? No. These are both radically uncertain and chances are pretty good that who's teaching you biology and who your roommate is are going to have a huge impact on the satisfaction you get out of whatever college you've gone to, so what do you do, faced with that? The answer is you do what Yakov Benheim calls "robust [satisficing 00:19:44]," and at last we get to the connection with my previous work. 
	What does it mean to robust satisfice? What it means is that instead of asking the question, "Which option will maximize my utility?" You ask a very different question. You ask, "Which option will give me a good enough result under the widest set of possible future states of the world?" You are not multiplying a value by a probability. You are establishing a good enough value and then looking at the various alternatives that are on offer to you to figure out under which of these alternatives that value is most robust to uncertainty, so if you are trying to decide what to do with your 401K money, to choose an example that may be more relevant to most of you than deciding where to go to college, you could think that the task is to maximize return on investment. You could think that the task is to come up, do some fancy calculation of risk reward, and maximize the reward to risk ratio, or you could say, "I want a retirement portfolio that will earn me inflation plus one percent. That's good enough for me." 
	Now the question is "Which distribution of investments will get me that return under the widest set of future states of the financial world?" It's a completely different approach to asking questions about your own future financial decisions. If you run a company about how to invest your company's resources in the coming year, you're not out to maximize anything. You're out to get a good enough result again under the widest set of possible circumstances, so this is what Benheim calls robust satisficing, and in the book on choice, the argument that I made about the virtues about satisficing, that is looking for good enough, was mostly a psychological argument. You'll do better if you try to maximize than you will if you try to satisfice but you'll feel worse, so why not do something that will make you feel better? 
	This is not a psychological argument, this is a normative argument. This is an argument that ... Not that this is the way to decide that will make you feel better but that this is actually the better way to decide if you are facing decisions that involve radical uncertainty. Now, there's one distinction I want to make for some of you that actually pay attention to any of this. There's a guy named [Taleb 00:22:27] who wrote a book about black swans and his analysis or at least a piece of his analysis of the financial collapse and previous collapses is that people whose job is to assess risk underestimate how likely it is that events in the tails of the distribution will happen. What does that mean? 
	Here's a normal curve and as you look at the normal curve, you can see that at the extremes, on either side, the area under the curve is really, very, very, tiny, so in effect, you can treat extreme results as having probability zero, and Taleb says, "That's wrong, they don't have probability zero. In fact, the tails of these curves are fatter than people think," so the red curve is a normalish distribution with a fat tail and the blue curve is a normal distribution with a normal tail, and as you can see, there is a fair amount of stuff that goes on in the tails of the red distribution. 
	This, according to Taleb, is the way the financial world actually operates, and to show you that this is true, this is the Russell 3000 index over a period of almost 25 years, and you can see that extreme low returns and extreme high returns are not nearly as unlikely as a normal distribution would lead you to think. The tails are fat. Similarly, this is the S & P 500 and the tails are fat. Much fatter than you would think if it were just a normal curve, and so, Taleb's point is that risk assessors don't get this, so they underestimate the likelihood of extreme events. They develop investment strategies that underestimate the likelihood of extreme events, and the extreme events will eventually happen, and when they do, all hell breaks loose. He also has an auxiliary view that extreme events have much more substantial consequences than non-extreme events, so that only makes the problem worse. 
	This is his analysis and I want to make it clear to you that what I'm saying is quite different from this, because to be able to come up with a distribution like this means that you can specify probabilities. The mistake, if he's right, is that people just specified the wrong probabilities, but this curve is a curve from which you can extract probabilities of every conceivable rate of return. Radical uncertainty is a condition where you don't know what the distribution even looks like, and if you don't know what the distribution looks like, you can't estimate probabilities wrong. You just can't estimate probabilities at all. 
	The question is, "On which of the decisions you face in your life are like the radically uncertain decision of a potential college Freshman trying to decide where to go to college?" My view is that this is the norm in the decisions we face in life. It is the unusual case where we can actually specify probabilities with any real justification. Most of the time when we specify probabilities, all we're doing is making ourselves feel better that we're behaving rationally and we're deceiving and deluding ourselves and the reason why this is consequential is if you recognize that you are facing a decision where uncertainties cannot be specified, your strategy for choosing ought to be to ask yourself what's good enough, question one, and then question two, which of the good enoughs will be most resistant to future states of the world that I can't even imagine let alone attach probabilities to, so it's a radically different notion about how decisions should get made under the, and here we can have a disagreement, the rare, not so rare, common or pervasive occasions in the natural environment where attaching probabilities to outcomes is just an exercise in science fiction. Thanks.