>> From the Library of Congress in Washington, D.C. - ^E00:00:04 ^B00:00:10 >> His name is Charles Wheelan. He's the author; he's also an academic. His topics are economics and public policy. And he's an author of a handful of books and some of them with pretty provocative titles: we've got Naked Economics and Naked Statistics. And Naked Statistics is this year, and also the... Centrist Manifesto -- excuse me. I've just finished reading it, and I've been thinking it about it a lot. In my hometown newspaper, the Des Moines Register, I just read an article about it. So, being from a state with one Republican senator and one Democratic senator, I'm particularly interested to hear what he has to say. So I welcome you to listen to Charles Wheelan. ^M00:00:57 [ Applause ] ^M00:01:03 >> Charles Wheelan: Well, thank you very much. It's a huge honor to be here on the Mall, to be recognized by the Library of Congress, and to be among people who appreciate books. And for me just to look up and down the author signings, there's a certain excitement. And it reminds me of other points in my life when I've been really, really excited about books. And one is a time that I think is now lost. So after college I traveled around the world with my now-wife, and one of the great excitements when we would get to destinations in mostly south Asia, India, Nepal... is the used bookstore. And you find those relics in the United States, but the idea that you could walk into this store and just be surrounded by the great books that people have brought with them; I still remember the excitement of, I can go in and for $10 I can by what's going to last me through this country. So it reminds me here, again, to be around -- not used books, but so many authors and people who appreciate literature and so on. I'm going to talk about two books that came out in 2013. One is Naked Statistics; the second is the Centrist Manifesto, as you heard in the introduction. The first thing I have to explain is, how do you get two books in one year, one about statistics, and one about creating a new third political party in the middle, that come out within two months of one another? And there is actually a story behind that. So, the first piece of the story is that, after I wrote Naked Economics, which is my first book, it did well, and I went back to W. W. Norton, and we agreed that I would write some more books in the same vein. So we agreed that I'd write two more. One would be a book about statistics that would do the same thing: to make it accessible for a lay audience. And the second would be about monetary policy, so to demystify currencies and central banks, and what the Fed is doing and so on. That seemed great; we signed a deal. So I went back, did nothing for about nine months, and then I started working. I started writing the book on monetary policy, and I thought I was actually making some decent progress -- enough that I actually proactively called my publisher. I said, "I've got good news: I'm actually making progress on the monetary policy book." And there was this long pause, and they said, "Well, you know you're supposed to write the statistics book first." And I said, "Oh. No." And then I told the story to my wife, and she said, "You didn't read the contract?" And I'm like, "I don't think I did, actually. No." So... the monetary policy book is what I'm writing now, so I switched gears. But then meanwhile, going back at least six years, I had this interest in politics and this growing belief that the two political parties were drifting in a way that was not particularly good for the country, and that we had to do something about it. So I would write a little bit about it, and I would take notes, and keep clippings, and so on. So, while I was writing the Naked Statistics book, I was also kind of working on this political book, and then things kept getting worse, and worse and worse. And I was persuaded that the time was right to deliver this political book. So, I had finished Naked Statistics, and of course, the publisher was looking for the book on monetary policy, and by then, I had finished the political book, which I hadn't told them about. So I did the brave thing, which is to call my agent and tell her to call the publisher and tell them I wrote a different book. "Well, tell them I haven't finished the one that they paid for, but I got another book they might want." And that's what happened, which is, I kind of on the sly was writing this other political book, which has turned out well, as well; they embraced that. But that's how two such dissimilar books ended up coming out in the same year. So let me talk a little bit about Statistics first. To be honest, this is a book that was conceived before we fully appreciated what was going to happen with big data. It really grew out of the fact that Naked Economics had done quite well, and the reason Naked Economics had done well is there were so many people writing about economics in a very technical, very inaccessible way, that it left a vacuum for people to write about the big ideas in a non-technical way, but to still express why this was such powerful stuff. And we thought, "Well, why not do the same thing for statistics?" And I had taken a lot of statistics. I had taken a lot of statistics from some very famous people. I had taken statistics from at least one person who won the Nobel Prize, and I didn't understand at least half of it. Alright? The more famous they were, the less I understood the course. So the idea was, "Okay, let's write about the big ideas in statistics in a way that you appreciate the intuition, and not necessarily the math." The thing that turns out to be interesting about both economics and statistics is the ideas are more important than the math. And certainly with statistics, if you understand the ideas, the math becomes relatively straightforward. Unfortunately with both of those topics, the way they tend to be taught is I'm going to throw a lot of math at you, and you could easily walk out of this course without appreciating what actually is going on. Now, it just so happens that over the course of time when I was working on the Naked Statistics, there was a lot more attention around big data. Now, I'm going to tell you a story, but the reason the story resonates is because I can look out and see most of you are certainly in my age bracket, plus or minus 10 years, and you are old enough to remember when you went into a retail store, and they just pulled the price tag off whatever you were buying and they put it in a box. And if you paid with a credit card, they kind of ran the imprint. Remember that sound? "Chhhk! Chhhk!" Right? And they put that in another box, and that was information more or less lost. If your car had been stolen, you'd go to the police department and someone would type something up in triplicate and pull off the different pieces; they would go in boxes. And the key here is that information really was difficult to search, it was difficult to aggregate, it was difficult to see the patterns that we can now see. Well, you don't have to fast forward very long to know that that stuff is now being scanned and there's an affinity card so somebody can see, not only where you live, but what you bought, how you paid for it, what else you bought, and so on. And we can aggregate that in meaningful ways. That's really what big data is about. And statistics is nothing more than the tools that we use, to make sense of the data. Now here's the story -- this is not my story. This is something that the New York Times original wrote about, but it's in the book. And it's a story about Target, the retailer. And Target, like every other retailer and law enforcement agencies and sports teams and other entities are looking for meaningful patterns that are going to allow them to do what they do better. One of the things that Target realized is that pregnant women develop very strong retail relationships. Now, I can't explain exactly why, but what they had observed was that if you're a pregnant woman and you started shopping at Target, you were likely to be a shopper at Target for a decade or more. This is a time, for whatever reason, your tastes were being firmed up. So what's the challenge? The challenge is to find pregnant women and get them in your store. And if they're already in your store, get them to come back more often. So Target had what they called something like a statistical analysis unit; I'm going to say it was in the basement of some windowless room. I'm not sure if that's true, but that's usually where you put the statisticians. And they had a lot of data. And they said, "How do we find pregnant women and get them into the store?" Now, it turns out that pregnant women were giving them a head start, which is Target, like many other retailers, had a birth registry. So if you were pregnant, and you were going to have a baby shower, one thing you could do is you could register with the store and your friends could come in and buy gifts for you. But of course, what you had just told Target is not only that you're pregnant, but when you're due. And then, they can track what you're buying. So they can say, "Look, here are people who told us they're pregnant, and huh! They're buying a lot of cotton balls. And they're switching to unscented lotions," both of which are true. "And they're buying prenatal vitamins, not surprising." Well, they came up with about 25 indicators that suggest somebody is pregnant. ^M00:09:58 And then they said, "Alright, let's look at the universe of Target shoppers, and see who else is changing their shopping behavior the way these people are, because they're probably pregnant, too. In fact, it was called the Pregnancy Prediction Analysis." Well, there's a punch line here. Let's fast-forward to Minneapolis. There's a father of a high school girl, and his daughter starts getting a lot of coupons from Target for baby wipes and cribs. And here's a father saying, "You know what... she's got to go to college; she's got to finish to high school." So he's really angry. He calls up the manager at Target and he says, "This is totally unacceptable. You should not be sending my daughter coupons for baby wipes. Don't be putting these ideas in her head. She's not ready for this!" And the Target -- being Minneapolis, everybody's very nice. And the manager says, "I'm really sorry; this is horrible. It'll never happen again." And he sleeps on it, and it's bothering him and bothering him. So a couple days later he has to call back again and says, "It's been bothering me. I need just to apologize again." At which point there's a long pause, and the father says -- and this is the quote from the original argument. "It turns out there have been some things going on here I wasn't aware of." [Laughter] And yes, Target knew that his daughter was pregnant before he did. And to my mind, that is a story that encapsulates big data, because first of all, it tells us -- is it good? Is it bad? Well, it's kind of a little bit of everything. Right? It tells us there are retailers out there who are going to know what we want. And I don't think that's inherently bad. If you live in a certain place, and you're likely to want jasmine rice, well, it's probably going to be on the shelves. And they might know that from your zip code; they might know it from other people with your income who buy the same things. And that's pretty good. If you look at what Netflix does, or iTunes, and you say, "How is it that Netflix knows what kind of movies I like?" it turns out that's just a variation on what Target figured out. In fact, it's a variation on what you probably learned if you took a couple math classes, which is just an application of correlation, which is all they do, is every time you rate a movie, they file that away in their database, and they know there are other people out there who's preferences for movies are highly correlated with yours. So if there's somebody in their database, or a group of people in their database, who are likely to rate the same movies that you give four stars as also four stars, and then they know that you have similar tastes. And if there's a movie that they've seen that you haven't, and they gave it five stars, then it's a pretty good chance that you're going to give it five stars, too. It's just a variation on the pattern that they see in the data. And to my mind, it's a really good thing, because when they suggest that I'm going to like a movie, I usually like the movie. Now, Netflix went a step further, and I think this is for people who are in these industries. This is the way you can go in a very clever direction. Netflix said they created a contest, and they offered actually a million-dollar prize, and they said, "Can you improve on the algorithm that we're currently using? So we know it's going to be based around correlation, but can you use even better predictive tools? Can you weight, for example, movies that people have seen more recently so we can count those ratings more than the ones they've seen ten years ago? And if so, can that give us an even more -- better indication of what people are likely to enjoy watching?" So they had this million-dollar contest. And the way they judged the contest is clever, as well, which is for everybody who entered, and it could be teams or individuals, and some of the world's most powerful and smart mathematicians got engaged, they said, "We'll give you a whole bunch of data from Netflix customers; we'll sever all the stuff that allows you to identify people, but you can see what people have rated. You play around with it, find the patterns that you want to find, and bring us your algorithm." And the clever part is, they reserved another large pool of data, also real ratings, and they said, "Okay, we're going to take the model you've built based on the real data we gave you, and we're going to test it on the real data we didn't give you. So you say that somebody who liked these movies are going to rate Adam Sandler movies a five, well, let's test that when they actually watch the Adam Sandler movie." And it turns out that somebody was able to improve on their existing algorithm by more than 10 percent, which was the criteria for winning the prize. And in the end -- you know I've seen the mathematical formula; it's way beyond what I can possibly comprehend, but it did in fact improve Netflix's ability to predict how you will feel about a certain film. So to my mind, that's all good. But let's go to one more example. In the Target vein, which is predictive policing, right? So one place where we like patterns is if we can see crime hotspots. And indeed, if you follow the news, you know places like New York City, probably Washington, D.C. are increasingly using statistics to figure out where crimes are likely to happen. And, again, it's not a huge undertaking to understand why this is the case. You're basically looking at a pattern where, if there are likely to be shootings at a certain place at a certain time of day, then you don't have to be a Nobel Prize-winner to know that that's where you should put your manpower. Now, that's just fine with me. We all have finite resources, and we would prefer to prevent violent crimes, but again, with all of these, it's that extra half tick that makes it kind of interesting. So San Jose, California claims that they actually prevented a crime before it happened. Now, if you've seen the film Minority Report with Tom Cruise, you know that's exactly what he does. How do you prevent a crime before it actually happens? Well, they did everything I've just discussed, which is they had an algorithm, and they had a series of car break-ins at a particular place at a particular time, and they wisely went there during that particular time. And they said, "Well, what's happening?" It turns out there were several ladies loitering around a car. And they said, alright these are the people that are going to break into that car, but, of course, you can't arrest somebody for thinking about breaking into a car. But it turns out when they question the two women, they both had prior arrest records. One of them had jumped bail; one of them was wanted for something else. So they were able to arrest them for other crimes, but reallybased on the idea that they assumed they were about to break into these cars. So now we're right up on the frontier of where a perfectly legitimate use of statistics starts to feel a little bit uncomfortable. Right? What if we were to build a model to predict who's likely to go back to prison as to who, relative to those not likely to go back to prison? We know we have a huge recidivism problem in this country, and we know there probably are things to predict, again, just with a pattern, who's most likely to recidivate, relative to who's not. Would you be comfortable using that in public policy? Would you like -- you know, would you, probably you'd be most comfortable saying, "Alright, well, you fit a pattern of somebody who's likely to reenter quite successfully. You've dealt with your substance abuse problem, you're highly educated, we've given you some job training, right, so it doesn't make me terribly uncomfortable to say that I'm willing to entertain a reduction in your sentence based on what the data are telling us." But the flipside of that is somebody who just fits a bad pattern, somebody that says, "You know what? I don't think you're going to make it on the outside. This is based on nothing that has to do with you other than the pattern that happens to match other people that come back to jail," prison, right? So, again, I think what's interesting is not that statistics are mathematically wonderful -- I have very little interest in the math -- it's that they're a very powerful tool. And that now that we have so much access to data, the question is, what can and should we do with those tools? I'll finish with one more before I somehow manage to transition to starting a third party, and that is the use of data as a performance tool. So the other thing that's happening, probably in every workplace represented here, is now that we have more data, we have a greater capacity to figure out who is doing better, however we choose to define "better," and that, of course, turns out to be very important. And presumably, if you go by the old management aphorism, "You can't manage what you can't measure," so now that we can measure more, we're all good. Right? Well, how many of you, I wish I had, I don't like PowerPoint, but there is one, two slides that I particularly like. The one is a picture of a woman, African-American woman, short hair, she is wearing a medal around her neck, a big gold medal, and she looks really excited, happy, proud. And I'll ask the crowd, "Anybody know who this is?" Uh, no. Then I advance to the next photo. It's the same woman. She's looking a little more beleaguered, no medal. Instead it's a mug shot, and it says "Source: Fulton County Jail." Anybody know who that is? Alright, so, and the two photos are related. Oh, I can't hear. You probably, it's the superintendent of the Atlanta schools, who was arrested, and some of these trials have already begun. The first woman, who's a small player, was just found not guilty though. What happened was, Atlanta, like many other places, started imposing high-stakes testing, ^M00:19:53 which is not inherently a bad thing, but as the story will unfold will make clear, it can be a bad thing. And Atlanta put a lot of pressure on doing well on the tests. One of the strategies you can use when you have high-stakes testing is you can try and figure out what's on the tests, and you can focus on that, and you can spend more hours teaching kids, and that's kind of what we hope will happen. The other thing you can do is cheat. And so, it's interesting here because statistics motivated the problem that arose here, right? So the use of data, changing behavior, and it prompts the cheating scandal, but it's also true that statistics was used to catch the cheaters. The way Atlanta was caught was that there are now private firms that analyze answer sheets, and they look for patterns that suggest irregularities. So, for example, if you got a bunch of students and they're getting more easy questions wrong and hard questions right, that shouldn't make much sense. You can't send anyone to jail based on that, but you can ask more questions. They look at erasures. So how often, when somebody erases an answer, does it go from wrong to right vs. right to wrong? And you remember there are more wrong answers for every question than right answers. So at a minimum you should have about even, and more often than not you should have right to wrong. Well, Atlanta had a lot of wrong to right, which means, either the student suddenly woke up at the end of the test and said, "Wait a minute," you know, and it got better, or teachers collected all the answer sheets and did a lot of erasing. So it turns out the pattern that was illustrated in Atlanta had so many wrong to right erasures that the probability of that happening just by chance was described by the investigators as roughly equal to the Georgia Dome being filled by roughly, I don't know, 40,000 or 50,000 fans all of whom by coincidence on game day were over seven feet tall. Right? That could happen. It's not likely. Right? And again, you can't send anyone to jail, but they said, "Maybe we should ask some questions." And when they asked questions, what they found out was that the teachers were having pizza parties on the weekend, where everybody would get together and have pizza and beer and erase answers together. So this, I mean, again, there's a lot in here - some of it good, some of it bad - but I return to this idea that it's just a tool, and it's really all about how you use the tool. People will often ask me, "Well, can you really lie with statistics?" Everyone is obsessed with this phrase about "lying with statistics." And I have two answers. One is well, you can lie or cheat with or without statistics. It doesn't matter. If you're a liar and a cheater, the statistics may help, but really, you're just going to be dishonest. But probably the more subtle answer is that statistics have a point of view. What I like to, the metaphor I like to use is statistical tools are like an American courtroom where the prosecution and the defense by law have access to all the same information, all the same interviews, all the same exhibits, all the same evidence and so on, but when you go to trial, both the defense and the prosecution are going to present that evidence in radically different ways. They are going to leave things out, they are going to emphasize other things more, they are going to put things in different contexts and so on. Well, statistics are the same. Statistics are just tools for simplifying data, and the word "simplification" implies that you're going to leave something out. And it's a question of how you simplify. So, some people are going to choose to simplify in a way that amplifies their own point of view. So one, if you want one takeaway, it's that a single statistic usually has very little value. If you're really looking to get at some underlying truth, or something that's broadly representative of what's going on, then really you want to see many different representations of the data. You're going to get in trouble if you look -- even one single study might point you in a direction that may not be consistent with other things that the same data might tell you. So the last piece I'll talk about in the same vein, particularly using data and statistics as a tool for rewarding performance, is a study that came out of the Air Force Academy, and this is in the back, you know, one of the concluding chapters of my book. And, because there is so much in the news about using data and performance and value added as a tool for figuring out what are the good schools? Who are the good teachers? And so on. The beauty of the Air Force Academy and the other academies is that all of the introductory classes have students that are randomly assigned. So unlike -- it's very hard to tell who the good professors are at other schools because you get to select the professors. Different professors are going to have different students. If you have a reputation for really hard, you're only going to get really -- for being really hard, you're only going to get really smart students. So it's hard to get much traction figuring out who the good teachers are if there's not random assignment. But at the Air Force Academy, there is. And it gets better because they have a common curriculum -- same books, same readings, same tests. So over the course of time you can start to figure out, "Well, who are the good professors? And more important, what do they have in common so we can figure out when we're hiring and promoting, who we should emphasize, who we should promote, who we should hire?" Well, it turns out that they did a study and quite statistically significant they find that the best, the most effective professors as measured by scores on the final exam in the introductory calculus class, and I think they also did some introductory science classes, mandatory, randomly assigned, and also as measured by student evaluations, both of those things point to the fact that the best teachers were the newest faculty members with the fewest degrees from fancy places, right? So it's the old codgers from, like, Harvard and Yale who are getting bad evaluations and low scores on the introductory exams. And you can certainly tell a story around that, right? They're getting out their notes and they're giving a compelling lecture about the Eisenhower administration, and you know, they think PowerPoint is an energy drink, except they don't really know what an energy drink is, right? So, and the cadets are drifting off to sleep. So, but that's not the punch line. The punch line is that conclusion turned out to be wrong. Because what they did, wisely, is they said, "Well, let's follow these students a little longer," and when they looked at performance and follow-on classes, so higher level math classes, advanced sign courses -- science courses -- the results flipped. And it turns out that the more experienced professors with the fancy degrees actually had students who did better overall and longer term. And they had to go back and say, "Alright, well, what explains that? And what they figured out was the new professors were much better at teaching to the test, right? Remember, everybody gets to the see the final exam before it's given, so if you're a new professor and you want your students to like you, you just start giving them a lot of examples that look exactly like the final exam. That will get you good reviews, and that will get you high scores on the final exam. The old codgers are saying, "Whatever, I don't care about the final exam. This is what you need to know if you're going to be an electrical engineer." So they focused on the big concepts and it turned off, turned out to pay off in the long run. Now, you're probably not teaching at the Air Force Academy, but the takeaway here is, be very careful what you think you're measuring, because if they had just stopped at the first analysis, not only would it have been wrong; it would have been the opposite of what appeared to actually be going on. And maybe if you studied them even longer, you might find a more nuanced result. But we have this tool. We have more capacity than ever before to make data-driven decisions. But it is like any other powerful tool, whether it's weapons, atomic energy, hair-removal cream, things that have very good uses. But if you use them wrong, you can end up causing some very serious harm. And that, I think, is probably the most important takeaway here, is the data aren't going away. Big data is real, and statistics will always be the tools that we use to make sense of it. But you need to be an informed consumer and user if you're going to use it in a way that actually advances your professional interests, social policy, or whatever it is you're trying to make better. Alright, so now I have the awkward -- I've got to start this over. While I was working on that, how is it that I decided we ought to start a third political party in the middle? The only statistical transition I can make is, if you look at the distribution of American voters, it does look like that normal bell curve that you find in every statistics book, alright? Which is you've got folks on the tails and you've got a lot of people who describe themselves as being somewhere near the middle, except that the tails seem to be running the show. So the evolution of this other book, the Centrist Manifesto, which as I said earlier argues for a third political party in the middle, is that we need some mechanism for kind of recapturing the political process for that big swath of voters in the middle. And this is a book that's born of my professional experiences. And I've kind of been in and around politics in various ways. So my first job out of college was writing speeches for the governor of Maine, who ^M00:29:31 at the time was Jock McKernan, who is now more famous for being married to Olympia Snowe. But he was at the time kind of this old New England Republican, the kind that have been hunted to extinction or well on their way. He was a pro-choice, pro-education, government-can-help Republican. So I had that experience. I decided I was more of a policy type, so I went back and studied public policy for a long time and got a Ph.D. So I also had an orientation around big problems. I think about entitlements and tax policy, and that's the stuff that actually I find kind of interesting. So a lot of times I look at those issues and say, "Why is that we're failing to make more progress on these really important issues?" I wrote for the Economist for about five years, which gave me a license to write about politics, particularly in the Midwest. As Illinois sends governor after governor to jail, right? So, and I, you know, Tommy Thompson was governor of Wisconsin, kind of got to watch politics from arm's-distance. And then the last piece was when I ran for office in Chicago, quite unexpectedly. I was teaching at the university of Chicago. Obama was elected, Rahm Emanuel was appointed his Chief of Staff. Rahm Emanuel was my congressman. Now, this was 2008; the financial crisis had just started. I was somebody who writes about policy and economics in particular, somebody that had been saying for decades, we need better quality political leadership. Nobody was queuing up to run for Rahm's seat because everybody assumed he wanted to be speaker of the house. So there was no air appearance. Because governor Blagojevich had just been indicted, the whole Chicago political establishment was frozen in place; everyone assumed everybody else was wearing a wire, and they just wouldn't talk about anything. So there is no, so the Chicago political establishment freezes, no air appearance, and in the last piece, is the country had just elected a professor from the University of Chicago to be president of the united states, and I said, "Wait a minute -- I'm a professor at the university of Chicago. You know, maybe I should run." And in fact, one of my lines on the campaign trail, the one that my opponent simply grew to despise, was, you know, over the last three weeks, or actually longer than that, the people of Illinois have elected a professor from the University of Chicago, and somebody who was a professor from the University of Chicago -- no, I'm sorry. We've elected somebody who went on to become President of the United States, and we elected somebody who went on to prison. But only one of them was from the University of Chicago, so we should stick with that model." You know, you always exploit the Blagojevich situation. But anyway, I ran as a Democrat in Chicago, as a fiscally-conservative democrat, with a wife who had run a software company, but was now teaching in an inner city school, and was confronting all the realities of the teachers' unions, and the constraints that put on what she wanted to do as a math teacher. And I'd go into the endorsement sessions, and they would say, " Well, how do you feel about performance based pay?" And everything I said about statistics notwithstanding, I still think it's potentially a tool that could be used, and that's kind of what I said, and then there's just kind of a silence. And it's kind of like, "Alright, well, you got 15 more minutes; I'm not sure how you want to use it, but we're not going to endorse you." So there's food in the back of the room if you want.: But it became very clear to me that I could never get elected, I could never get out of a Democratic primary, and I was not comfortable where the Republican party had gone, but that my set of beliefs were where a lot of people were. And indeed, if you just, what the book does -- there's two things is, it lays out what I believe is a coherent ideology for what a centrist party could look like that takes the best from each party, right? So at their best, and that's not to say that they're always at their best, but people who are Republicans care a lot about long-term fiscal responsibility. Something we have to do; the math does not work on our entitlement program, so let's be fiscally responsible. At their best, the Democrats are right about environmental policy. We ought to be environmentally responsible. And actually, I think the two of those things belong together, because they're both about living better than we should today, at the expense of people who come tomorrow. But if you put just put those two bumper stickers together -- environmentally responsible, fiscally responsible -- we've now eliminated both parties, right? That's not radical stuff. I would fold in social tolerance, because I think that there are a lot of people who draw the line about government intervention at anything that doesn't affect other people. So in that bucket, I would include things like gay marriage, which I can't credibly claim that my heterosexual marriage is diminished when two people I've never met get married to each other in California, and happen to be the same sex, right? It would almost be funny if it weren't serious, right? And, but I would also say it applies to things like guns, if you want to have a gun in your home, then I'm willing to let you have a gun in your home. When you bring it outside, we need to talk, right? But I think there is a logical way -- we're not going to solve these disagreements. But we can paper over some of them by drawing that line at where your personal behavior doesn't affect other people. And then the last piece I would add, which both parties talk about, and I don't think either deliver, is a true commitment to economic opportunity. Are we really giving every person born in this country, in different kinds of neighborhoods, a real opportunity to participate in what this country has to offer? And again, as someone married to a person who taught inner city schools, now teaches in a rural high school in New Hampshire, I think the answer is no. And I think we have to rethink how we do that. We have to invest more heavily in targete dearly childhood education. We have to continue to work at K through 12 education performance, and those kinds of things. But again, the bucket that I've outlined, I think, is a set of beliefs that a lot of people would buy into. So, can you make it happen? And the answer is, you'll never elect a centrist president. The Electoral College dooms those efforts, so forget that. Most of the third parties that haven't worked have come from the polls. It's very hard to win anything if you are more extreme than either party. Tea Party makes a lot of noise, but in terms of changing policy, remains to be seen, certainly on the accomplishment side. I don't think, right now, you're going to fix the House, because of Jerry Mandry and other problems. So the strategy the book lays out is built around electing a handful of US senators. And the way it plays out is this, which is, in one of the 22 states that have one Democratic senator and one Republican senator, or one senator from one party, and a governor of the other, which means that these are states where people in the middle are determining the outcome. In one of those states, when you have an open senate seat, and the two parties hold their primaries, and in the primaries they say the crazy talk, that they are going to try and walk back later, right? So, well, you know, over in the democratic primary, they're lowering the retirement age; and over in the republican primary, all the kindergarteners need guns. And you know this is making the base really happy, and then when they come to the general, they're going to do the Etch-a-Sketch, right? This is what Mitt gave to us, and they are going to walk it back, because that's not what the general electorate wants to hear, except that now, we're going to have a centrist candidate in that space that used to be a vacuum, who's looking at the general election from day one, and all that person has to do is hold the middle. You don't need 50% to win a senate seat; you need 34%. It's a plurality in a three-way race. That can happen in a lot of states. And then you roll it up. And you say, "Well, what if we elected five centrist senators from New England, from the Midwest, from Florida, North Carolina, from Virginia, and they went to the senate, and the senate was now 47 Republicans, five centrists and 48 Democrats. Who runs the world? The centrists do, including who's your majority leader, right? People ask me all the time, "Well, you know, how would they deal with Mitch McConnell, or Harry Reid?" And I would say, "They wouldn't be there," right? Because when it became time to pick a majority leader, one party puts up Mitch McConnell. Democrats are going to say, "Wait a minute -- we need somebody a little more in the middle." Then we'll get the majority leader. So, it becomes like baseball arbitration, where the two parties put up somebody much closer to the center. It changes the dynamic of the senate, in part because those five centrists can't look at everybody else as an enemy. You're never going to have a majority, so you look at the Republicans and you say, "Here are the issues on which we agree. You want to do corporate tax reform, we're with you; we got five votes. Let's do it." You look at the Democrats and say, if you want to get serious about climate change, we're with you. Everybody is a potential ally, because there's no other way to function. Now is that a fantasy? No, it's not, but is a possible way forward within the constraints of our current institutional structure? I think it is. I think it's consistent with the way we've laid things out, takes advantage of the way the senate is constructed. I think it taps into this large swath of voters who look at either party and vote for the one that they find the least offensive, right? And they pretend about many of the things of their own party that they find objectionable, it's really -- it's just kind of, what's your willingness to overlook what you don't like about your party? Because what you do care about is what they stand for. So, hopefully people could become excited about something. So that's the general strategy. I will stop there, and I will happily answer questions. We don't have a lot of time, but I'll be happy to take questions about either book or questions from anybody who can possibly come up with a question that weaves the two together. ^M00:39:30 Thank you very much for coming and for being here this afternoon. ^M00:39:34 [Applause] ^M00:39:42 >> Hi. A very fascinating presentation. In your Centrist Manifesto, one of the things that I hear from you, and I think a lot of centrists, is that it lacks a little color. And what I mean by that is, you look at things that are sort of noncontroversial, but there are still very important fights in the area of civil rights, women's rights, a whole variety of other issues. And it seems that a lot of, a lot of times I hear centrists say, "Well, we're going to just put those aside. We're only going to go for things that we can agree on." >> Charles Wheelan: That's a very good point, and I don't want to leave you with the impression that there are answers to these issues. But on a lot of issues, not all, right? I mean, the abortion issue is always going to be really, really tough, because there's just a there's a fundamental difference of opinion. Even some of the environmentalists are really tough, because there is just a fundamental ideological difference of opinion about how you evaluate the value of economic growth relative to environmental destruction; people just have different views about that. But I do think that one the things that characterizes centrists, I would say not setting these issues aside, but looking for pragmatic solutions to difficult problems. So I'll give you one that's in the book; it's a little fanciful, but I don't think terribly so, and that's guns. And again, here in Washington, in a very sad, awful way, it's come to the fore again and again. We're probably going to do nothing. I don't think any reasonable person can say that we don't have a gun problem, but as I look at that issue, I see one group of people saying, "Don't take away guns from legal, law-abiding people," and I look at another group of people, particularly those who live in cities, as I did, in Chicago for 20 years, and they say, "We don't care about law abiding citizens, just keep them out of the hands of the people who are dangerous." And background checks is an obviously a step forward, but I actually think that there's another step we could and should take, which is something, some kind of gun registration, and again the extreme gun folks are going to recoil there, but that's not who we're talking about. Whereby you get a gun, you can have 20 guns if you want, but they're registered to you, and they are ballistically fingerprinted, which we can now do, when you watch all those CSI shows. We could put a fingerprint on the gun. That's much better than the serial number, and harder to get rid of, so that any bullet that comes out of that gun be traced back to the gun that you fired. So this is your weapon, and if you sell it, or lose it, or anything else, you have to report it stolen. If you sell it legally, then the problem is like fine art, moves to the next owner. Alright, then what does that do about the 250 million guns that are already out there. Not enough. But I would say we'd have a period where people who are legal gun owners have got to bring in their guns and get them fingerprinted. But what it does do is allow us to figure out where the dangerous guns have come from. Is it going to stop what happened in Washington the other day? Probably not, but it will stop, or at least help us get a handle on a lot of the carnage in our cities, like how many other people have died in Washington D.C. aside from that shooting? Where, when people are shot in gang violence -- we just had a horrible shooting in Chicago -- we can say, where are these kind of guns coming from? And if you happen to be somebody who loses 20 guns a month, then we can say, "Wait a minute, this is kind of a problem. There's one gun shop that seems to be selling all the guns showing up on the streets of Detroit," we said that's a problem. So I think this idea of responsible gun ownership is a way that we can finesse that otherwise awful issue and actually prevent some carnage. It's not perfect, it won't be palatable to the extremes, but I think you can sell the middle on those kinds of solutions. But thank you for that question. Other side? >> Hello? >> Charles Wheelan: Yes. >> Thank you. Does your book address two critical issues that I believe will determine whether any third party could ever be successful, whether it's a centrist party or not? And number one, that is campaign finances. And related to that, of course, is the Citizens United decision. And the second one is voting systems. What do you think about preferential voting? >> Charles Wheelan: Yes. So it does acknowledge both. I don't, I think Citizens United is going to be very tough to get rid of, so I think you're going to have to work within that framework. I will tell you, as somebody who has run, that it's worse than you think. As bad as you think the system is, and everybody knows about buying influence and so on; what's less obvious is just the sheer opportunity cost of raising money. It just takes so much time that it makes you a less informed candidate. But I actually think that, unfortunately, the lack of campaign finance, any meaningful campaign finance, makes it easier for the party I've described, because it's easier to bundle money from around the country from centrists and channel it to the key races that I've described. So, you know, my first preference would be to roll that back. I'm not sure how you'd do it; I think it's actually going to have to be a constitutional amendment. But I would absolutely support anything that tries to roll back the pernicious effect of money in campaigns, but failing that, I think it actually helps us rather than hurting us. And I'm pragmatic enough that, if the tools are out there, I will use them, even if I don't agree with the tools that are at large, even as I would agree to change them in the long run. And the second was voting systems. Again, I think we're using it to our advantage. I don't think it's a good thing that you can win a senate race with a plurality. I would support runoffs. I would support instant runoff voting, because I think it's a better way to capture, to capture preferences. I think it is fairer to third parties, but in the interim, I think that we can take advantage of the fact that centrists can reassert themselves by using the rules of electing senate people. So, I do acknowledge them, but I don't actually advocate changing either of them, even if I think in the long run, changing them is probably a good idea. I think we probably have time for one or two more. Let's do one from each side. >> How would you, how would you ensure that, that caucus of half a dozen or so senators, once they actually came to the senate, stuck together and voted as a body above all else? Because once you have issues that are important to this state, for reelection, or this constituent, or whatever, once you start to peel off one or two on individual issues, it becomes real easy for the whole thing to just fall apart. >> Charles Wheelan: Right. I think this remains to be seen. First of all, I want to have that problem. I want to get the point where there's six centrists and they're not agreeing all the time; that would be a victory. They're not going to agree on every issue, even if they are part of a party. And I think honestly, part of this strategy would be electing some big C centrists, and also some independents like Angus King, who are in that space, but may not buy into the party. By the way, there are a lot of very smart people who believe in the senate strategy, but not necessarily the big C centrist party strategy, but at the end of the day, I want them all in the senate. So I think there's going to be some leakage and disagreements, and so on, but I do think, by disposition and process you're going to get people more likely to be in the middle on more issues. And when the senate works best, it is the gang of six, the gang of eight, the gang of 12, and it does work well sometimes. I just want to make that gang of six the gang of 12, or the gang of 12 the gang of 18. I want more people willing to make compromise in the middle, not on every issue, but I think if you send more people with this mindset, with this core set of beliefs to the senate, you're more likely to get that behavior. I can't guarantee that it's going to happen all the time. So I am over time, they're waving a sign, but I, I appreciate your interest and your time. Thank you very much. ^M00:47:21 [Applause] ^M00:47:25 >> This has been a presentation from the Library of Congress. Visit us at LOC.gov. ^E00:47:32