1 00:00:04,120 --> 00:00:07,160 Speaker 1: Get in touch with technology with tech Stuff from how 2 00:00:07,200 --> 00:00:13,720 Speaker 1: stuff works dot com. Hey there, welcome to tech Stuff. 3 00:00:13,720 --> 00:00:16,279 Speaker 1: I'm your host, Jonathan Strickland. I'm an executive producer and 4 00:00:16,320 --> 00:00:19,880 Speaker 1: I love all things tech. And in a recent episode 5 00:00:20,200 --> 00:00:23,400 Speaker 1: I mentioned a guy named John von Neuman, and that 6 00:00:23,520 --> 00:00:25,840 Speaker 1: said I should probably do an episode about him, and 7 00:00:25,920 --> 00:00:28,760 Speaker 1: several of you wrote in and urged that I should 8 00:00:28,760 --> 00:00:31,440 Speaker 1: do this sooner rather than later. So today we're going 9 00:00:31,480 --> 00:00:35,400 Speaker 1: to learn more about von Neuman and his numerous contributions 10 00:00:35,400 --> 00:00:37,879 Speaker 1: to science and technology. This is the first part of 11 00:00:37,920 --> 00:00:41,960 Speaker 1: a two part episode. His achievements were remarkable, perhaps made 12 00:00:41,960 --> 00:00:44,560 Speaker 1: even more astonishing by the fact that he only lived 13 00:00:44,680 --> 00:00:47,960 Speaker 1: to his mid fifties, and yet he was an incredibly 14 00:00:48,040 --> 00:00:52,360 Speaker 1: prolific thinker. But he also had his flaws, and I'll 15 00:00:52,400 --> 00:00:54,400 Speaker 1: talk about those as well, because I think it would 16 00:00:54,400 --> 00:00:58,160 Speaker 1: be a disservice to just gloss over them. So while 17 00:00:58,200 --> 00:01:03,760 Speaker 1: he was a genuine whenly intelligent, brilliant man, he had 18 00:01:03,840 --> 00:01:07,559 Speaker 1: some some flaws to his character as well. So John 19 00:01:07,640 --> 00:01:12,679 Speaker 1: von Neumann was born Neuman Ya nash Law Josh margin 20 00:01:12,800 --> 00:01:18,440 Speaker 1: Tie in Budapest, Hungary, in December. And I know, I know, 21 00:01:18,520 --> 00:01:20,760 Speaker 1: I butchered the pronunciation of that, but I'm doing the 22 00:01:20,760 --> 00:01:25,080 Speaker 1: best I can. He was born into a nonpracticing Jewish family, 23 00:01:25,560 --> 00:01:30,640 Speaker 1: so uh, ethnic Jewish family, but not a practicing Jewish family. Now, 24 00:01:30,680 --> 00:01:33,319 Speaker 1: according to the biography, as I read, the household liberally 25 00:01:33,400 --> 00:01:37,360 Speaker 1: mixed in Jewish and Christian traditions together. His father was 26 00:01:37,400 --> 00:01:40,920 Speaker 1: a successful banker. His mother came from a prosperous family, 27 00:01:41,319 --> 00:01:44,760 Speaker 1: so in those biographies they also mentioned that he came 28 00:01:44,800 --> 00:01:49,280 Speaker 1: from a wealthy background. He was the oldest of three boys. 29 00:01:49,400 --> 00:01:51,480 Speaker 1: His younger brothers grew up to be a doctor and 30 00:01:51,520 --> 00:01:55,320 Speaker 1: a lawyer, respectively, and the family would employ governesses to 31 00:01:55,400 --> 00:01:59,360 Speaker 1: look after the children and from them, Vaughn Neuman began 32 00:01:59,440 --> 00:02:02,760 Speaker 1: to learn French and German and English and other languages 33 00:02:02,800 --> 00:02:07,240 Speaker 1: as well. Even as a kid, he was obviously gifted. 34 00:02:07,320 --> 00:02:09,920 Speaker 1: He could talk with his father in Greek and tell 35 00:02:10,040 --> 00:02:13,880 Speaker 1: jokes in Greek. He could memorize an entire page out 36 00:02:13,919 --> 00:02:15,760 Speaker 1: of a phone book in just a few minutes and 37 00:02:15,800 --> 00:02:18,840 Speaker 1: answer questions about who had which number or what a 38 00:02:18,919 --> 00:02:22,320 Speaker 1: person's street address was. They would do this as like 39 00:02:22,440 --> 00:02:25,760 Speaker 1: party tricks. When he was six years old, John von 40 00:02:25,840 --> 00:02:29,120 Speaker 1: Neumann was an apt student in school, and he attended 41 00:02:29,160 --> 00:02:32,200 Speaker 1: the Lutheran High School starting in nineteen thirteen. This was 42 00:02:32,520 --> 00:02:36,320 Speaker 1: one of the best schools in Hungary. He earned the 43 00:02:36,440 --> 00:02:39,720 Speaker 1: award of being the best Mathematician of the fifth class 44 00:02:39,760 --> 00:02:42,800 Speaker 1: in nineteen eighteen and he won Best Hungarian Student in 45 00:02:42,840 --> 00:02:47,079 Speaker 1: Mathematics in nineteen twenty. Now, in between that time there 46 00:02:47,120 --> 00:02:50,359 Speaker 1: were some bumps in the road, but it wasn't due 47 00:02:50,440 --> 00:02:54,000 Speaker 1: to his academics, was due to world politics. In nineteen 48 00:02:54,080 --> 00:02:57,920 Speaker 1: nineteen you had the end of World War One. Hungary 49 00:02:58,000 --> 00:03:01,040 Speaker 1: fell under the governance of a commune. This leader named 50 00:03:01,120 --> 00:03:06,240 Speaker 1: bellah Kun, and he and his Hungarian Soviet Republic moved 51 00:03:06,320 --> 00:03:09,600 Speaker 1: to nationalize a lot of private property in other words, 52 00:03:10,040 --> 00:03:14,200 Speaker 1: sees the property of wealthy individuals in order to redistribute 53 00:03:14,240 --> 00:03:18,480 Speaker 1: that to the rest of the population. Now, the Noumans 54 00:03:18,960 --> 00:03:21,799 Speaker 1: didn't really like the sound of that, and so they 55 00:03:21,840 --> 00:03:26,520 Speaker 1: fled temporarily to Austria. After about a month, they came 56 00:03:26,560 --> 00:03:31,160 Speaker 1: back to Budapest and the Soviet Republic didn't last very long. It, 57 00:03:31,600 --> 00:03:35,200 Speaker 1: I mean it ultimately it fell. But in the wake 58 00:03:35,360 --> 00:03:39,800 Speaker 1: of its failure, that added more problems for this family. 59 00:03:40,080 --> 00:03:44,600 Speaker 1: So namely, the failing government had many Jewish representatives in it. 60 00:03:44,600 --> 00:03:48,320 Speaker 1: It was a largely Jewish government, and so public opinion 61 00:03:48,640 --> 00:03:53,400 Speaker 1: towards Jewish people in general turned very, very negative. So 62 00:03:53,440 --> 00:03:58,040 Speaker 1: it didn't matter that John's family had been in opposition 63 00:03:58,160 --> 00:04:01,320 Speaker 1: to that government. The fact that they were Jewish meant 64 00:04:01,400 --> 00:04:06,080 Speaker 1: that they would receive a lot of the ill will 65 00:04:06,320 --> 00:04:09,840 Speaker 1: of the people. He would go on to study mathematics 66 00:04:09,880 --> 00:04:14,640 Speaker 1: in one university and chemistry at another at the same time. 67 00:04:15,200 --> 00:04:17,680 Speaker 1: Sort of all right, so here's the story behind that. 68 00:04:18,080 --> 00:04:21,280 Speaker 1: His dad didn't want him to pursue a career that 69 00:04:21,440 --> 00:04:25,520 Speaker 1: wasn't going to accumulate wealth, and he felt that an 70 00:04:25,560 --> 00:04:29,039 Speaker 1: advanced degree in pure mathematics wasn't going anywhere. It wasn't 71 00:04:29,040 --> 00:04:32,760 Speaker 1: a real money maker. So he and John sat down 72 00:04:33,040 --> 00:04:36,800 Speaker 1: and together they agreed upon the subject of chemistry. There 73 00:04:36,800 --> 00:04:39,320 Speaker 1: were a lot of rising stars in the world of 74 00:04:39,400 --> 00:04:42,640 Speaker 1: chemistry out of this part of Europe, and so John 75 00:04:42,720 --> 00:04:46,000 Speaker 1: von Neumann agreed he would study chemistry. So he enrolled 76 00:04:46,040 --> 00:04:49,960 Speaker 1: in the University of Berlin. However, at the same time 77 00:04:50,000 --> 00:04:54,520 Speaker 1: he also enrolled in the University of Budapest for mathematics. Now, 78 00:04:54,600 --> 00:04:58,720 Speaker 1: Hungary's universities had really strict limitations on the number of 79 00:04:58,839 --> 00:05:03,120 Speaker 1: Jewish students would be allowed to attend at any one time. However, 80 00:05:03,640 --> 00:05:07,400 Speaker 1: von Neuman's academic record was beyond impressive, so he was 81 00:05:07,440 --> 00:05:10,440 Speaker 1: able to get in. And then he did something pretty 82 00:05:10,520 --> 00:05:14,440 Speaker 1: darn baller. He would attend classes at the University of 83 00:05:14,440 --> 00:05:19,280 Speaker 1: Berlin learning about chemistry, and he would skip nearly all 84 00:05:19,320 --> 00:05:22,360 Speaker 1: of the lectures and classes at the University of Budapest 85 00:05:22,520 --> 00:05:25,400 Speaker 1: in mathematics. He would just come back to the University 86 00:05:25,400 --> 00:05:28,880 Speaker 1: of Budapest to take exams or whenever he was absolutely 87 00:05:28,920 --> 00:05:32,640 Speaker 1: required to be there, and he aced those exams even 88 00:05:32,640 --> 00:05:35,560 Speaker 1: though he wasn't going to the lectures. He graduated with 89 00:05:35,640 --> 00:05:39,000 Speaker 1: a pH d in mathematics from the University of Budapest 90 00:05:39,040 --> 00:05:43,120 Speaker 1: in ninety without really going to lectures there. He was 91 00:05:43,200 --> 00:05:47,160 Speaker 1: twenty three years old. He transferred out of the University 92 00:05:47,160 --> 00:05:50,080 Speaker 1: of Berlin as he was studying chemistry, and he would 93 00:05:50,200 --> 00:05:54,840 Speaker 1: ultimately receive a diploma in chemical engineering in ninety six 94 00:05:55,160 --> 00:05:58,680 Speaker 1: from a school in Zurich, Switzerland. I wish I could 95 00:05:58,680 --> 00:06:01,400 Speaker 1: tell you the name of that school, but I'm looking 96 00:06:01,440 --> 00:06:04,560 Speaker 1: at it and it would do such a terrible job 97 00:06:04,600 --> 00:06:07,680 Speaker 1: with this one. I don't even dare attempt to pronounce it. 98 00:06:07,960 --> 00:06:10,240 Speaker 1: So I'm just gonna leave it be now. When he 99 00:06:10,279 --> 00:06:12,920 Speaker 1: was twenty. When he was still in school, John von 100 00:06:13,000 --> 00:06:18,880 Speaker 1: Neuman published a definition for ordinal numbers, and an ordinal 101 00:06:19,000 --> 00:06:22,320 Speaker 1: number is a way to describe the position of an 102 00:06:22,360 --> 00:06:26,480 Speaker 1: object within a sequence of objects that are inside a set. So, 103 00:06:26,600 --> 00:06:31,400 Speaker 1: for example, if you consider a set to be people 104 00:06:31,440 --> 00:06:33,760 Speaker 1: who are in line for pizza, and there are four 105 00:06:33,800 --> 00:06:37,240 Speaker 1: people ahead of me, I am the fifth person in 106 00:06:37,400 --> 00:06:41,560 Speaker 1: that line or set. So the ordinal number that is 107 00:06:41,560 --> 00:06:44,839 Speaker 1: my designation is five because I'm the fifth person in line. 108 00:06:45,440 --> 00:06:48,440 Speaker 1: Von Neuman's definition of ordinal numbers is the same one 109 00:06:48,600 --> 00:06:52,240 Speaker 1: that we use to this day now. Von Neuman's dissertation 110 00:06:52,480 --> 00:06:56,240 Speaker 1: for his PhD had the title The Axiomatic System of 111 00:06:56,320 --> 00:07:02,480 Speaker 1: set theory. Set theory concerns collections of objects, typically mathematical objects, 112 00:07:02,600 --> 00:07:07,040 Speaker 1: as opposed to you know, like hammers and Set theory 113 00:07:07,200 --> 00:07:11,080 Speaker 1: was established in the late nineteenth century by George Cantor 114 00:07:11,160 --> 00:07:14,080 Speaker 1: in an article titled on a property of the Collection 115 00:07:14,160 --> 00:07:18,880 Speaker 1: of all real algebraic numbers. Basically, this is the theory 116 00:07:18,960 --> 00:07:22,360 Speaker 1: that can be described like this. Sets are collections of 117 00:07:22,400 --> 00:07:25,640 Speaker 1: objects or elements. So in a real world example, the 118 00:07:25,680 --> 00:07:30,680 Speaker 1: classification of mammals includes all animals that are vertebrates, that 119 00:07:30,760 --> 00:07:34,120 Speaker 1: have for that typically give birth to live young, and 120 00:07:34,160 --> 00:07:38,400 Speaker 1: they produce milk for offspring. So a cat fits that definition. 121 00:07:38,480 --> 00:07:44,360 Speaker 1: A cat fits the set of mammals. All cats are mammals. However, 122 00:07:44,680 --> 00:07:48,560 Speaker 1: sets themselves can be objects that belong to larger sets. 123 00:07:48,680 --> 00:07:51,840 Speaker 1: So in this example, mammals is a set, but it's 124 00:07:51,880 --> 00:07:54,680 Speaker 1: also an object. It belongs to the larger set of 125 00:07:54,800 --> 00:07:58,560 Speaker 1: all animals. So a cat belongs to the set mammal 126 00:07:58,960 --> 00:08:02,520 Speaker 1: as well as to the set animals, and mammals are 127 00:08:02,560 --> 00:08:06,280 Speaker 1: a subset of animals. If you've seen a Venn diagram 128 00:08:06,360 --> 00:08:09,680 Speaker 1: in which you have two circles that overlap in some way, 129 00:08:09,920 --> 00:08:13,520 Speaker 1: you've seen a representation of one aspect of set theory. 130 00:08:13,920 --> 00:08:16,320 Speaker 1: So let's give an example of a Venn diagram. Let's 131 00:08:16,320 --> 00:08:20,080 Speaker 1: say we have two circles. One circle represents people who 132 00:08:20,120 --> 00:08:23,640 Speaker 1: love they might be giants, and the second circle represents 133 00:08:23,840 --> 00:08:27,920 Speaker 1: people who love Andrew w K. These circles each represent 134 00:08:28,280 --> 00:08:32,640 Speaker 1: different sets. The overlap, or the intersection of those two 135 00:08:32,640 --> 00:08:37,359 Speaker 1: sets is where you have people who fit both categories. 136 00:08:37,800 --> 00:08:41,200 Speaker 1: They love they might be giants and they love Andrew 137 00:08:41,360 --> 00:08:43,920 Speaker 1: w K. We could even give this group a new name. 138 00:08:44,080 --> 00:08:47,559 Speaker 1: We could call it something else. Like weirdos like Jonathan 139 00:08:47,600 --> 00:08:50,959 Speaker 1: Strickland because I love both, they might be giants and 140 00:08:50,960 --> 00:08:53,960 Speaker 1: Andrew w K. But we could also talk about the 141 00:08:54,080 --> 00:08:57,679 Speaker 1: set difference of this Venn diagram. The set difference for 142 00:08:57,760 --> 00:09:00,439 Speaker 1: the people who love they might be giants would include 143 00:09:00,520 --> 00:09:04,080 Speaker 1: all the people who only love Andrew w K. And 144 00:09:04,080 --> 00:09:06,480 Speaker 1: the opposite would be true for the set difference for 145 00:09:06,559 --> 00:09:09,280 Speaker 1: the people who love Andrew w K. You also have 146 00:09:09,360 --> 00:09:12,840 Speaker 1: symmetric differences. The symmetric difference of these two sets would 147 00:09:12,840 --> 00:09:16,200 Speaker 1: include all the people who only loved one of the 148 00:09:16,240 --> 00:09:19,480 Speaker 1: two bands, but not both. There are many other ways 149 00:09:19,520 --> 00:09:21,960 Speaker 1: you can describe sets, but you get the general idea. 150 00:09:22,240 --> 00:09:26,559 Speaker 1: As for axioms, those are statements that are self evidently true, 151 00:09:27,080 --> 00:09:29,839 Speaker 1: things that are true because of common sense. We can 152 00:09:29,880 --> 00:09:33,000 Speaker 1: declare them to be true. It's about as fundamental as 153 00:09:33,040 --> 00:09:34,920 Speaker 1: you can get. In fact, it is as fundamental as 154 00:09:34,920 --> 00:09:36,959 Speaker 1: you can get with truth. So one of those might 155 00:09:37,000 --> 00:09:42,160 Speaker 1: be parallel lines will never intersect. By definition, parallel lines 156 00:09:42,200 --> 00:09:44,880 Speaker 1: will never intersect. That is an axiom. It is a 157 00:09:44,920 --> 00:09:49,760 Speaker 1: fundamental truth. It's a common sense statement. It's not based 158 00:09:49,840 --> 00:09:55,440 Speaker 1: on earlier or or even more granular statements. So these 159 00:09:55,440 --> 00:09:59,600 Speaker 1: axioms can be used to deduce further conclusions. But doing 160 00:09:59,640 --> 00:10:03,960 Speaker 1: that hand be tricky. If you build deductions on axioms 161 00:10:04,000 --> 00:10:08,080 Speaker 1: and you find that two different deductions you have based 162 00:10:08,080 --> 00:10:11,040 Speaker 1: off the same axiom end up contradicting each other, then 163 00:10:11,080 --> 00:10:13,640 Speaker 1: you've got a problem on your hands. So let's say 164 00:10:13,679 --> 00:10:17,440 Speaker 1: you've got your axiom A. This is your fundamental statement, 165 00:10:17,520 --> 00:10:20,840 Speaker 1: the one that you've declared to be true. Then from A, 166 00:10:21,559 --> 00:10:25,920 Speaker 1: you deduce that because A is true, statement B, which 167 00:10:25,960 --> 00:10:29,320 Speaker 1: is based on A, must also be true. And then 168 00:10:29,520 --> 00:10:34,400 Speaker 1: from statement B you deduce that statement P is also true. 169 00:10:34,800 --> 00:10:36,839 Speaker 1: Now let's get back to A. Let's say that we 170 00:10:37,400 --> 00:10:39,680 Speaker 1: start from A again, and now we're making a different 171 00:10:39,720 --> 00:10:42,679 Speaker 1: deduction and we deduce a new statement. We're calling this 172 00:10:42,840 --> 00:10:46,760 Speaker 1: statement D, and that one must be true. But now 173 00:10:47,280 --> 00:10:49,960 Speaker 1: from statement D we make a deduction, and from statement 174 00:10:50,080 --> 00:10:53,199 Speaker 1: D we deduce that statement P has to be false. 175 00:10:54,200 --> 00:10:56,480 Speaker 1: So this is a problem. You have one line of 176 00:10:56,520 --> 00:11:00,240 Speaker 1: reasoning that states P has to be true because A 177 00:11:00,280 --> 00:11:03,280 Speaker 1: is true, B is true, P is true. Then you 178 00:11:03,320 --> 00:11:05,120 Speaker 1: have another one that says P has to be false 179 00:11:05,160 --> 00:11:07,839 Speaker 1: because A is true, D is true. That means P 180 00:11:08,080 --> 00:11:11,840 Speaker 1: must be false. This is a paradox or a contradictory statement, 181 00:11:12,120 --> 00:11:14,840 Speaker 1: and it means we have to look over the entire system. 182 00:11:15,040 --> 00:11:16,880 Speaker 1: We have to look at the axioms to make sure 183 00:11:16,920 --> 00:11:19,319 Speaker 1: that they are actually sound, and we have to look 184 00:11:19,320 --> 00:11:22,600 Speaker 1: at the process we've used to deduce the truth or 185 00:11:22,640 --> 00:11:27,679 Speaker 1: falsehood of the statements that followed from this axiom. This 186 00:11:27,720 --> 00:11:30,400 Speaker 1: falls into an area of logic that I absolutely loved 187 00:11:30,520 --> 00:11:34,200 Speaker 1: studying in college. Now, I'm no von Neumann, not by 188 00:11:34,280 --> 00:11:36,400 Speaker 1: a long shot, but I got a brag for just 189 00:11:36,480 --> 00:11:38,760 Speaker 1: a second. So when I was in college, I took 190 00:11:38,800 --> 00:11:41,760 Speaker 1: a course in symbolic logic, and I found that my 191 00:11:41,800 --> 00:11:45,160 Speaker 1: professor was teaching directly from the textbook. So I made 192 00:11:45,200 --> 00:11:47,960 Speaker 1: a tough decision. I decided to stop going to classes. 193 00:11:48,400 --> 00:11:52,679 Speaker 1: I only took the exams and I aced the course. Now, granted, 194 00:11:53,200 --> 00:11:56,079 Speaker 1: the version of logic I was studying was the most 195 00:11:56,120 --> 00:12:00,480 Speaker 1: basic version of symbolic logic. It was child's play for 196 00:12:00,600 --> 00:12:02,760 Speaker 1: someone like von Neuman. He would have breathed through the 197 00:12:02,880 --> 00:12:05,280 Speaker 1: class back when he was six years old. So I 198 00:12:05,679 --> 00:12:08,840 Speaker 1: can't brag too much, but it did give me a 199 00:12:08,840 --> 00:12:11,920 Speaker 1: little bit of insight into his mind, at least in 200 00:12:11,960 --> 00:12:14,200 Speaker 1: that aspect. I've got a lot more to say about 201 00:12:14,280 --> 00:12:16,720 Speaker 1: John von Neuman. But first, let's take a quick break 202 00:12:16,840 --> 00:12:27,160 Speaker 1: to thank our sponsor. Set theory would become one of 203 00:12:27,360 --> 00:12:30,720 Speaker 1: many areas that von Neuman would continue to study and 204 00:12:30,760 --> 00:12:34,800 Speaker 1: develop over the course of his life. There's a concept 205 00:12:34,880 --> 00:12:38,440 Speaker 1: in mathematics called the von Neuman universe. In fact, although 206 00:12:38,679 --> 00:12:41,480 Speaker 1: some scholars like Gregory H. Moore have gone on to 207 00:12:41,559 --> 00:12:45,320 Speaker 1: say that this attribution is somewhat misleading, but we'll leave 208 00:12:45,360 --> 00:12:48,400 Speaker 1: that for now, because would otherwise be diving into an 209 00:12:48,440 --> 00:12:51,959 Speaker 1: area of mathematics so far outside of my expertise and 210 00:12:52,120 --> 00:12:55,520 Speaker 1: understanding that I would just be reading from textbooks or 211 00:12:55,559 --> 00:12:58,559 Speaker 1: history books, and I don't think that makes very good podcasting. 212 00:12:59,120 --> 00:13:03,559 Speaker 1: In addition to mathematics and chemistry, the young von Neumann 213 00:13:03,640 --> 00:13:08,120 Speaker 1: was also fascinated by technology and aviation, and it began 214 00:13:08,160 --> 00:13:10,560 Speaker 1: to work in an area that would have a really 215 00:13:10,679 --> 00:13:17,000 Speaker 1: big effect on many different different industries, different careers moving forward. 216 00:13:17,559 --> 00:13:21,760 Speaker 1: That would be game theory. Now, personally, I find the 217 00:13:21,880 --> 00:13:25,040 Speaker 1: term game theory to be a little misleading because it 218 00:13:25,160 --> 00:13:29,559 Speaker 1: undersells what it's all about. You could use game theory 219 00:13:29,600 --> 00:13:32,720 Speaker 1: to describe how people play a game like poker, but 220 00:13:33,000 --> 00:13:37,319 Speaker 1: it's actually way more than that. In psychology, you might 221 00:13:37,360 --> 00:13:40,760 Speaker 1: refer to it as the theory of social situations, and 222 00:13:40,800 --> 00:13:44,080 Speaker 1: it really comes down to how human beings interact with 223 00:13:44,120 --> 00:13:47,640 Speaker 1: one another in specific types of situations. And generally you 224 00:13:47,679 --> 00:13:51,520 Speaker 1: can break it down into two large branches, cooperative game 225 00:13:51,559 --> 00:13:56,000 Speaker 1: theory and non cooperative game theory, and the names kind 226 00:13:56,040 --> 00:13:59,439 Speaker 1: of are self explanatory. Cooperative game theory describes how people 227 00:13:59,440 --> 00:14:03,160 Speaker 1: will work together to achieve a common goal. How will 228 00:14:03,200 --> 00:14:07,360 Speaker 1: they leverage their strengths, how will they compensate for their weaknesses, 229 00:14:08,080 --> 00:14:12,319 Speaker 1: how do they manage to go after this goal together. 230 00:14:12,840 --> 00:14:17,360 Speaker 1: Non Cooperative game theory, you could call it competitive game theory, 231 00:14:17,679 --> 00:14:20,880 Speaker 1: describes how intelligent people will interact with each other as 232 00:14:20,920 --> 00:14:25,840 Speaker 1: they each are working toward achieving their individual goals. Now, 233 00:14:25,880 --> 00:14:30,040 Speaker 1: those individual goals might be the same, so it may 234 00:14:30,040 --> 00:14:32,840 Speaker 1: be that everyone's trying to go after the same prize 235 00:14:32,840 --> 00:14:35,400 Speaker 1: and only one person can get it. Or it might 236 00:14:35,440 --> 00:14:39,120 Speaker 1: be that each person has a different individual goal, and 237 00:14:39,120 --> 00:14:41,960 Speaker 1: it may be that some of those individual goals are 238 00:14:42,000 --> 00:14:46,120 Speaker 1: at conflict with one another. For example, maybe my goal 239 00:14:46,240 --> 00:14:49,280 Speaker 1: is to get a certain trophy and someone else's goal 240 00:14:49,480 --> 00:14:52,320 Speaker 1: is to get a certain medal. But the problem is 241 00:14:52,720 --> 00:14:56,840 Speaker 1: that the when one person achieves one of those goals, 242 00:14:56,880 --> 00:14:59,400 Speaker 1: the path to achieving the other one is cut off, 243 00:14:59,760 --> 00:15:03,360 Speaker 1: so that would be another example. Now, John von Neumann 244 00:15:03,440 --> 00:15:07,160 Speaker 1: was not the first mathematician to suggest using mathematics to 245 00:15:07,840 --> 00:15:11,320 Speaker 1: describe game theory, or to study game theory, or to 246 00:15:11,360 --> 00:15:16,200 Speaker 1: come up with various strategies in game theory. Numerous thinkers 247 00:15:16,240 --> 00:15:21,120 Speaker 1: had worked on various applications, some for specific games like chess, 248 00:15:21,560 --> 00:15:24,360 Speaker 1: before von Neuman had ever come onto the scene. But 249 00:15:25,360 --> 00:15:28,800 Speaker 1: von Neuman's work was some of the first general purpose 250 00:15:29,040 --> 00:15:34,080 Speaker 1: game theory work not dedicated to a specific implementation. His 251 00:15:34,160 --> 00:15:38,920 Speaker 1: scholarship effectively established game theory as its own distinct field 252 00:15:39,000 --> 00:15:42,920 Speaker 1: of study. John von Neumann published his first paper on 253 00:15:43,000 --> 00:15:46,680 Speaker 1: game theory in nineteen twenty eight. It had the title 254 00:15:47,080 --> 00:15:50,880 Speaker 1: Theory of Parlor Games. He recognized that a game like 255 00:15:50,960 --> 00:15:54,600 Speaker 1: poker had a lot more going on than just probabilities. 256 00:15:55,080 --> 00:15:59,720 Speaker 1: So if poker just was reliant upon chance, then you 257 00:15:59,760 --> 00:16:03,960 Speaker 1: could memorize all the possible outcomes of a round of cards, 258 00:16:04,600 --> 00:16:07,280 Speaker 1: and you would have a good chance of being able 259 00:16:07,320 --> 00:16:11,680 Speaker 1: to play your hand to the best of its effectiveness. Right, 260 00:16:11,760 --> 00:16:14,080 Speaker 1: you would know that the odds of someone having a 261 00:16:14,080 --> 00:16:17,440 Speaker 1: better hand would be higher or lower than um any 262 00:16:17,520 --> 00:16:19,880 Speaker 1: given hand that you have and that would help you 263 00:16:19,920 --> 00:16:23,960 Speaker 1: make a decision. However, that does not take into account 264 00:16:24,120 --> 00:16:28,480 Speaker 1: the human element of bluffing. So with bluffing, a person 265 00:16:28,520 --> 00:16:31,440 Speaker 1: can act as if his or her hand is stronger 266 00:16:31,640 --> 00:16:34,840 Speaker 1: than it really is, or maybe they are giving off 267 00:16:35,280 --> 00:16:39,560 Speaker 1: the implication that they aren't working with a very strong 268 00:16:39,640 --> 00:16:43,040 Speaker 1: hand and they're hoping that you will get out of 269 00:16:43,040 --> 00:16:45,800 Speaker 1: the game. There's a lot of psychology in their doubt 270 00:16:46,360 --> 00:16:50,040 Speaker 1: enters into the equation. So von Neyman started to work 271 00:16:50,080 --> 00:16:51,720 Speaker 1: on this idea and he saw how it could be 272 00:16:51,840 --> 00:16:54,800 Speaker 1: applicable to all sorts of stuff, not just games, but 273 00:16:55,080 --> 00:16:59,080 Speaker 1: stuff like economics, and he partnered with an Austrian economist 274 00:16:59,280 --> 00:17:02,920 Speaker 1: who was at print Sston University named Oscar Morgan Stern, 275 00:17:03,000 --> 00:17:06,280 Speaker 1: and together they would publish a book titled Theory of 276 00:17:06,400 --> 00:17:10,280 Speaker 1: Games and Economic Behavior. In the introduction of that book, 277 00:17:10,600 --> 00:17:13,720 Speaker 1: they lay out the fact that economics is a really 278 00:17:13,840 --> 00:17:17,359 Speaker 1: complicated science. There are a lot of contributing factors to 279 00:17:17,520 --> 00:17:21,080 Speaker 1: economic outcomes, and not all of them are identified, let 280 00:17:21,160 --> 00:17:24,800 Speaker 1: alone understood. So out of the factors that we can 281 00:17:24,800 --> 00:17:30,680 Speaker 1: say yes, this definitely impacts economics, we don't necessarily understand how, 282 00:17:31,240 --> 00:17:33,680 Speaker 1: but we know it happens, and then there are others 283 00:17:33,680 --> 00:17:36,760 Speaker 1: that we may not have identified yet. So the authors 284 00:17:36,800 --> 00:17:40,240 Speaker 1: maintained that because of that, because of this uncertainty, this 285 00:17:40,400 --> 00:17:44,159 Speaker 1: lack of knowledge, this gap in our knowledge, it's pretty 286 00:17:44,240 --> 00:17:46,960 Speaker 1: much the case that anyone who claims to have a 287 00:17:47,080 --> 00:17:51,119 Speaker 1: universal theory of economics has got to be wrong because 288 00:17:51,119 --> 00:17:54,040 Speaker 1: we don't have that full understanding of all the factors 289 00:17:54,080 --> 00:17:57,200 Speaker 1: and how they interact with one another in any given situation, 290 00:17:57,280 --> 00:18:01,000 Speaker 1: how they're weighted in any given situation. So in a way, 291 00:18:01,359 --> 00:18:04,920 Speaker 1: this would mirror another big challenge von Neuman would encounter later, 292 00:18:05,040 --> 00:18:07,960 Speaker 1: which would involve predicting the weather. I'll talk a bit 293 00:18:08,000 --> 00:18:10,600 Speaker 1: about that in our next episode. Now, one of the 294 00:18:10,680 --> 00:18:15,400 Speaker 1: central concepts of von Neumann's game theory was called mini max. 295 00:18:15,880 --> 00:18:20,119 Speaker 1: Emil Borel had previously theorized about mini max, and this 296 00:18:20,200 --> 00:18:23,919 Speaker 1: is all about minimizing the possible loss in the event 297 00:18:24,200 --> 00:18:29,040 Speaker 1: of a worst case scenario. So, considering considering a scenario 298 00:18:29,080 --> 00:18:34,240 Speaker 1: where the absolute worst happens, the maximum bad happens, how 299 00:18:34,240 --> 00:18:37,800 Speaker 1: do you minimize the impact to you in that event? 300 00:18:38,359 --> 00:18:41,080 Speaker 1: And this could be applied to all sorts of situations. 301 00:18:41,440 --> 00:18:43,639 Speaker 1: How do you limit the setbacks you're going to suffer 302 00:18:43,960 --> 00:18:48,480 Speaker 1: should the worst happen. There's also a concept called maximn 303 00:18:48,800 --> 00:18:51,000 Speaker 1: This is sort of the opposite. How can you make 304 00:18:51,040 --> 00:18:56,200 Speaker 1: the absolute most gains with the minimum success you might 305 00:18:56,280 --> 00:19:00,399 Speaker 1: have in any given scenario. So these two on steps 306 00:19:00,440 --> 00:19:03,320 Speaker 1: together would become part of game theory, and game theory 307 00:19:03,400 --> 00:19:07,040 Speaker 1: wasn't the only scholarly work von Neumann was pursuing in 308 00:19:07,080 --> 00:19:10,359 Speaker 1: the nineteen twenties. At the same time, he was also 309 00:19:10,440 --> 00:19:14,720 Speaker 1: studying quantum mechanics, which would ultimately form the foundation of 310 00:19:14,760 --> 00:19:19,720 Speaker 1: his book, The Mathematical Foundations of Quantum Mechanics. So I've 311 00:19:19,720 --> 00:19:22,840 Speaker 1: talked about quantum mechanics before, but what the heck doesn't 312 00:19:22,840 --> 00:19:25,679 Speaker 1: actually mean. Well, the simple answer is that it's a 313 00:19:25,720 --> 00:19:32,040 Speaker 1: branch of physics that's concerned with the very very very small. 314 00:19:32,520 --> 00:19:37,199 Speaker 1: We're talking atomic and subatomic levels generally. So at that scale, 315 00:19:37,359 --> 00:19:40,399 Speaker 1: the physics that we observe in our day to day lives. 316 00:19:40,440 --> 00:19:44,120 Speaker 1: The behavior of larger stuff, you know, stuff like tractors 317 00:19:44,119 --> 00:19:47,919 Speaker 1: and puppy dogs and skyscrapers and people. The physics that 318 00:19:47,960 --> 00:19:50,520 Speaker 1: we encounter day to day that breaks down when you 319 00:19:50,560 --> 00:19:54,199 Speaker 1: get down to this atomic and subatomic level. So in 320 00:19:54,240 --> 00:19:57,439 Speaker 1: our day to day world, I cannot walk up to 321 00:19:57,520 --> 00:20:00,840 Speaker 1: a wall and then in an instant appear on the 322 00:20:00,880 --> 00:20:02,800 Speaker 1: other side of the wall. I would have to have 323 00:20:02,840 --> 00:20:05,600 Speaker 1: a door to walk through or a window decline through, 324 00:20:05,840 --> 00:20:08,199 Speaker 1: or I'd have to burst kool aid or hulk like 325 00:20:08,480 --> 00:20:10,920 Speaker 1: through the barrier. There would have to be an opening, 326 00:20:11,200 --> 00:20:13,000 Speaker 1: or I would have to make one. Those are the 327 00:20:13,040 --> 00:20:15,399 Speaker 1: only two options if I want to get onto the 328 00:20:15,400 --> 00:20:17,040 Speaker 1: other side of a wall, or I guess I could 329 00:20:17,040 --> 00:20:19,720 Speaker 1: walk around it if if that's an option, but you 330 00:20:19,760 --> 00:20:23,160 Speaker 1: get what I mean. On the quantum level, however, this 331 00:20:23,240 --> 00:20:26,480 Speaker 1: is not the case. You can actually have a quantum 332 00:20:26,560 --> 00:20:30,720 Speaker 1: particle come up to a barrier and sometimes appear on 333 00:20:30,760 --> 00:20:32,720 Speaker 1: the other side of the barrier as if it had 334 00:20:32,760 --> 00:20:36,240 Speaker 1: just passed through, without even having to pass through. This 335 00:20:36,960 --> 00:20:40,960 Speaker 1: tendency can have consequences in our macro world. So take 336 00:20:40,960 --> 00:20:44,800 Speaker 1: electrons for example. So for convenience sake, we talk about 337 00:20:44,800 --> 00:20:48,919 Speaker 1: electrons inhabiting an orbit around a nucleus of an atom, 338 00:20:49,000 --> 00:20:51,520 Speaker 1: and we usually depict this in some way that makes 339 00:20:51,640 --> 00:20:55,000 Speaker 1: sense to us on a macro scale. And you might 340 00:20:55,080 --> 00:20:58,520 Speaker 1: have a very simple drawing where you've got the the 341 00:20:58,640 --> 00:21:01,359 Speaker 1: very uh icon drawing of an atom where you've got 342 00:21:01,359 --> 00:21:04,080 Speaker 1: the nucleus as a big dot in the center, and 343 00:21:04,080 --> 00:21:07,639 Speaker 1: they have a circle around the nucleus, and around in 344 00:21:07,680 --> 00:21:10,040 Speaker 1: that circle you have a dot that represents an electron. 345 00:21:10,520 --> 00:21:14,080 Speaker 1: So that's sort of saying, in this moment of time, 346 00:21:14,080 --> 00:21:16,960 Speaker 1: the electron is right here. But that's misleading. That's not 347 00:21:17,160 --> 00:21:23,680 Speaker 1: really what we can definitively say. Electrons have wave like properties. 348 00:21:24,119 --> 00:21:27,040 Speaker 1: They don't act just as particles. They also connect as 349 00:21:27,040 --> 00:21:30,800 Speaker 1: a wave, and waves don't just abruptly end when they 350 00:21:30,880 --> 00:21:34,600 Speaker 1: hit a barrier. They actually taper off. If the barrier 351 00:21:34,800 --> 00:21:38,320 Speaker 1: is thin enough, some of the wave will continue through 352 00:21:38,440 --> 00:21:42,360 Speaker 1: the barrier to the other side. Now, the wave represents 353 00:21:42,400 --> 00:21:46,560 Speaker 1: a probability function. Now essentially that tells us the chance 354 00:21:46,720 --> 00:21:50,840 Speaker 1: of the electron inhabiting any part along that wave at 355 00:21:50,880 --> 00:21:55,920 Speaker 1: any given time. So that means there is a probability, 356 00:21:56,000 --> 00:22:00,199 Speaker 1: albeit a small one, that the electron could exist on 357 00:22:00,240 --> 00:22:03,600 Speaker 1: the other side the barrier. Because it's it still exists 358 00:22:03,640 --> 00:22:06,440 Speaker 1: on the other side, it represents a probability, and as 359 00:22:06,480 --> 00:22:08,720 Speaker 1: long as there's a probability, it means that sooner or 360 00:22:08,840 --> 00:22:11,960 Speaker 1: later it'll happen. So that means that if you have 361 00:22:12,080 --> 00:22:15,359 Speaker 1: enough electrons near a barrier like this one, some of 362 00:22:15,400 --> 00:22:19,200 Speaker 1: those electrons will just from probability, appear on the other 363 00:22:19,240 --> 00:22:21,440 Speaker 1: side of the barrier as if it had passed through, 364 00:22:21,520 --> 00:22:24,760 Speaker 1: and we call it electron tunneling. Now there's no actual 365 00:22:24,840 --> 00:22:29,040 Speaker 1: tunnel created, there's no hole made in the barrier. It 366 00:22:29,200 --> 00:22:33,840 Speaker 1: just was a fact of probability. There was a small 367 00:22:33,920 --> 00:22:36,399 Speaker 1: probability that the electron could be on the other side, 368 00:22:36,720 --> 00:22:40,280 Speaker 1: and so sometimes that happens. This is one of the 369 00:22:40,320 --> 00:22:46,320 Speaker 1: big challenges that microprocessor manufacturers make when they miniaturize elements 370 00:22:46,440 --> 00:22:51,040 Speaker 1: on the chips because if the gates, the actual gates 371 00:22:51,040 --> 00:22:55,400 Speaker 1: that are controlling the pathway of electrons are thin enough, 372 00:22:55,960 --> 00:22:59,680 Speaker 1: then it's possible for the electron probability function to overlap 373 00:22:59,800 --> 00:23:02,960 Speaker 1: the barrier, and then you have electrons passing through these 374 00:23:03,040 --> 00:23:06,000 Speaker 1: gates as if they were open even when they're closed, 375 00:23:06,080 --> 00:23:09,080 Speaker 1: and that creates errors. So that's why this has real 376 00:23:09,119 --> 00:23:14,240 Speaker 1: world uh impact, even though we don't see this kind 377 00:23:14,280 --> 00:23:16,480 Speaker 1: of behavior in the macro world. Like I said, I 378 00:23:16,480 --> 00:23:20,280 Speaker 1: can't walk up to a wall and then magically appear 379 00:23:20,320 --> 00:23:23,480 Speaker 1: on the other side of it just because of probability. 380 00:23:23,520 --> 00:23:27,000 Speaker 1: There's zero probability that that will happen. Concepts like these 381 00:23:27,000 --> 00:23:29,400 Speaker 1: are hard to wrap our minds around because we occupy 382 00:23:29,440 --> 00:23:32,639 Speaker 1: a world in which quantum mechanics do not apply. This 383 00:23:32,720 --> 00:23:35,679 Speaker 1: is also or if they do apply, they apply, it's 384 00:23:35,760 --> 00:23:39,879 Speaker 1: such a tiny, tiny amount that it's imperceptible to us. 385 00:23:40,000 --> 00:23:42,320 Speaker 1: So this is why I get grouchy when I see 386 00:23:42,359 --> 00:23:45,320 Speaker 1: people try to use these concepts from quantum mechanics to 387 00:23:45,400 --> 00:23:48,920 Speaker 1: describe or predict stuff in our real world macro environments, 388 00:23:48,960 --> 00:23:51,800 Speaker 1: because it really doesn't apply there, at least not on 389 00:23:51,840 --> 00:23:56,440 Speaker 1: a level that is at all, you know, noticeable. At 390 00:23:56,440 --> 00:24:01,960 Speaker 1: this point, you're either dangerously close to using pseudoscience or 391 00:24:02,200 --> 00:24:05,760 Speaker 1: you've fully jumped into the pseudo science science boat. So 392 00:24:06,160 --> 00:24:09,800 Speaker 1: be where people who try to describe real world scenarios 393 00:24:09,840 --> 00:24:15,760 Speaker 1: in quantum mechanics um you know, methods or approaches. I've 394 00:24:15,800 --> 00:24:17,560 Speaker 1: got more to say about John von Neuman in just 395 00:24:17,640 --> 00:24:20,480 Speaker 1: a second, but first let's take another quick break to 396 00:24:20,560 --> 00:24:30,600 Speaker 1: thank our sponsor. Now, John von Neumann was working on 397 00:24:30,680 --> 00:24:34,879 Speaker 1: quantum mechanics at a really exciting time. Heisenberg had just 398 00:24:34,960 --> 00:24:38,560 Speaker 1: proposed his uncertainty principle. Now that's largely based off the 399 00:24:38,600 --> 00:24:40,800 Speaker 1: fact that matter can act as both a wave and 400 00:24:40,840 --> 00:24:43,399 Speaker 1: a particle, and that would mean there's a limit to 401 00:24:43,480 --> 00:24:47,399 Speaker 1: how precisely. We might know a particle's properties, like an 402 00:24:47,400 --> 00:24:50,680 Speaker 1: electron's position and speed, for example. So the more precisely 403 00:24:50,760 --> 00:24:53,199 Speaker 1: we know one of those two things, the less we 404 00:24:53,280 --> 00:24:55,480 Speaker 1: know about the other. So the more precisely we can 405 00:24:56,080 --> 00:24:59,080 Speaker 1: talk about the electron's position, the less we know about 406 00:24:59,080 --> 00:25:03,200 Speaker 1: its speed, and vice versa. John von Neumann's contributions were 407 00:25:03,359 --> 00:25:07,600 Speaker 1: unsurprisingly related to the application of mathematics when it comes 408 00:25:07,640 --> 00:25:12,439 Speaker 1: to quantum mechanics. Von Neumann's emphasis was on mathematical rigor. 409 00:25:12,840 --> 00:25:16,200 Speaker 1: That is, his approach emphasized the degree to which a 410 00:25:16,280 --> 00:25:21,600 Speaker 1: mathematical representation of a concept and quantum physics is logically sound. 411 00:25:21,800 --> 00:25:26,560 Speaker 1: He wanted the math to be as strong and a 412 00:25:26,800 --> 00:25:31,000 Speaker 1: method of proof as possible to logically support these various 413 00:25:31,040 --> 00:25:34,159 Speaker 1: principles and quantum mechanics. Now, that put his approach in 414 00:25:34,359 --> 00:25:38,600 Speaker 1: contrast with a another physicist named Paul de rac who 415 00:25:38,720 --> 00:25:43,480 Speaker 1: argued for a more pragmatic approach that was less mathematically rigorous, 416 00:25:43,720 --> 00:25:46,280 Speaker 1: but it was also more efficient. It was easier to apply, 417 00:25:46,640 --> 00:25:49,280 Speaker 1: and it would lead to conclusions that were easier to 418 00:25:49,440 --> 00:25:53,760 Speaker 1: understand than these very complicated mathematical formulas. So you had 419 00:25:53,800 --> 00:25:57,760 Speaker 1: these two very different styles coming at quantum mechanics at 420 00:25:57,760 --> 00:26:01,160 Speaker 1: the same time. So John VA Neuman. By the late 421 00:26:01,240 --> 00:26:05,480 Speaker 1: nineteen twenties was already something of an intellectual celebrity, at 422 00:26:05,520 --> 00:26:09,040 Speaker 1: least in academic circles, and he was doing groundbreaking work 423 00:26:09,240 --> 00:26:13,679 Speaker 1: in game theory and quantum mechanics. In ninety nine, he 424 00:26:13,760 --> 00:26:17,400 Speaker 1: was invited to lecture at Princeton University on the subject 425 00:26:17,480 --> 00:26:19,760 Speaker 1: of quantum theory, and he said he would be happy 426 00:26:19,840 --> 00:26:22,320 Speaker 1: to do so, but first he had to attend to 427 00:26:22,400 --> 00:26:26,359 Speaker 1: a small personal matter. That small personal matter was a wedding. 428 00:26:26,960 --> 00:26:31,040 Speaker 1: His wedding, he got married to a woman named Marietta Covechi. 429 00:26:31,440 --> 00:26:35,119 Speaker 1: Now Covechi and von Neumann had known each other since childhood. 430 00:26:35,320 --> 00:26:39,879 Speaker 1: Covechi was a talented economics student at the University of Budapest. 431 00:26:40,240 --> 00:26:44,359 Speaker 1: She was also something of a socialite in Hungary. She 432 00:26:44,520 --> 00:26:48,440 Speaker 1: was known for appearing at parties and being very glamorous. 433 00:26:48,680 --> 00:26:52,680 Speaker 1: Von Neumann was also a fan of the nightlife. Apparently, 434 00:26:52,680 --> 00:26:55,119 Speaker 1: he was quite well known as a patron of the 435 00:26:55,160 --> 00:26:58,840 Speaker 1: cabaret circuit in Berlin. He would teach in the daytime 436 00:26:58,880 --> 00:27:00,960 Speaker 1: and go out for a night on the town in 437 00:27:01,000 --> 00:27:04,160 Speaker 1: the evening, and his love of parties and alcohol would 438 00:27:04,200 --> 00:27:07,720 Speaker 1: follow him as he relocated to the United States. In 439 00:27:07,760 --> 00:27:12,080 Speaker 1: addition to marrying Covechi, von Neumann converted from being a 440 00:27:12,119 --> 00:27:16,119 Speaker 1: nonpracticing Jew to a Catholic. Now this was not an 441 00:27:16,200 --> 00:27:20,040 Speaker 1: indication that he had found religion. He was agnostic through 442 00:27:20,200 --> 00:27:22,440 Speaker 1: most of his life. I'll talk a little bit about 443 00:27:22,440 --> 00:27:25,120 Speaker 1: that in the next episode as well. It was more 444 00:27:25,200 --> 00:27:28,119 Speaker 1: of a practical decision so that he could actually marry Covechi. 445 00:27:28,320 --> 00:27:31,560 Speaker 1: So he converts to Catholicism and then he and his 446 00:27:31,720 --> 00:27:36,600 Speaker 1: newlywed wife move over to the United States. Now, von 447 00:27:36,720 --> 00:27:40,679 Speaker 1: Neuman would become a professor at Princeton, but reportedly it 448 00:27:40,760 --> 00:27:43,400 Speaker 1: was one of the few things in academia that he 449 00:27:43,440 --> 00:27:46,160 Speaker 1: was not great at, or at least people didn't really 450 00:27:47,040 --> 00:27:49,439 Speaker 1: like his style. So the trouble mostly appeared to be 451 00:27:49,520 --> 00:27:54,280 Speaker 1: that von Neuman was super duper wicked smart, and he 452 00:27:54,320 --> 00:27:57,360 Speaker 1: had a phenomenal memory as well, so he could work 453 00:27:57,400 --> 00:28:00,800 Speaker 1: out complex equations in his head, and he would leap 454 00:28:00,840 --> 00:28:04,440 Speaker 1: around the topic quickly, which left a lot of students 455 00:28:04,560 --> 00:28:08,119 Speaker 1: struggling in his wake. They couldn't keep up, they didn't 456 00:28:08,160 --> 00:28:10,639 Speaker 1: weren't able to connect the dots like he was. He 457 00:28:10,760 --> 00:28:14,159 Speaker 1: got a reputation for scribbling out important equations hurriedly on 458 00:28:14,200 --> 00:28:16,840 Speaker 1: a chalkboard and then erasing them before anyone knew what 459 00:28:16,880 --> 00:28:20,719 Speaker 1: they meant or could even copy them down. However, he 460 00:28:20,840 --> 00:28:24,159 Speaker 1: also had a reputation for being able to communicate complicated 461 00:28:24,200 --> 00:28:27,240 Speaker 1: ideas in a very straightforward way in a one on 462 00:28:27,240 --> 00:28:31,520 Speaker 1: one setting that would allegedly make sense even to dullards 463 00:28:31,560 --> 00:28:34,560 Speaker 1: like myself if I had been given the opportunity. Now, 464 00:28:34,600 --> 00:28:38,120 Speaker 1: since von Neuman died decades before I was born, I 465 00:28:38,160 --> 00:28:40,680 Speaker 1: can't actually put this claim to the test, but by 466 00:28:40,680 --> 00:28:44,520 Speaker 1: many accounts, he was talented at explaining complicated ideas to 467 00:28:44,560 --> 00:28:47,680 Speaker 1: people who didn't have the expertise in mathematics to understand 468 00:28:47,720 --> 00:28:50,720 Speaker 1: all of the bells and whistles. In nineteen thirty three, 469 00:28:50,880 --> 00:28:53,680 Speaker 1: he was named a mathematics professor for the Institute for 470 00:28:53,760 --> 00:28:58,640 Speaker 1: Advanced Study in Princeton. That was a brand new department. 471 00:28:59,000 --> 00:29:02,040 Speaker 1: He was one of the six experts in the original 472 00:29:02,160 --> 00:29:06,479 Speaker 1: group of professors. He was also the youngest of those six. Uh. 473 00:29:06,600 --> 00:29:11,000 Speaker 1: Those professors included some really smart people, including one that 474 00:29:11,080 --> 00:29:14,400 Speaker 1: I'm sure you've all heard about. That would be Albert Einstein, 475 00:29:14,520 --> 00:29:18,280 Speaker 1: so he was in really good company. Three was also 476 00:29:18,320 --> 00:29:20,479 Speaker 1: the last year that von Neumann would lecture for a 477 00:29:20,600 --> 00:29:23,200 Speaker 1: term in Germany. He was going back and forth. He 478 00:29:23,240 --> 00:29:25,280 Speaker 1: would do a term in Germany, he would come back 479 00:29:25,280 --> 00:29:26,920 Speaker 1: and do a term in the United States, and so 480 00:29:27,000 --> 00:29:30,719 Speaker 1: on and so forth. The Nazi Party, however, was starting 481 00:29:30,760 --> 00:29:35,040 Speaker 1: to consolidate power in Europe around this time, so Neuman 482 00:29:35,120 --> 00:29:38,440 Speaker 1: withdrew to work solely in the United States. Now, some 483 00:29:38,520 --> 00:29:41,280 Speaker 1: of his peers would leave continental Europe in an effort 484 00:29:41,320 --> 00:29:44,240 Speaker 1: to escape the Nazi regime as it got more powerful, 485 00:29:44,480 --> 00:29:47,240 Speaker 1: but von Neuman had already relocated in an effort to 486 00:29:47,280 --> 00:29:50,000 Speaker 1: find steady employment as an academic. Now I say this 487 00:29:50,520 --> 00:29:53,440 Speaker 1: only because as I was researching von Neumann, I came 488 00:29:53,480 --> 00:29:56,520 Speaker 1: across differing accounts, some of which said, you know, he 489 00:29:56,560 --> 00:29:59,440 Speaker 1: was fleeing the Nazi regime. But from the information I 490 00:29:59,480 --> 00:30:02,120 Speaker 1: could find, it sounded more like he was looking for 491 00:30:02,160 --> 00:30:05,080 Speaker 1: a steady gig and he got one at Princeton, and 492 00:30:05,120 --> 00:30:08,040 Speaker 1: that was the guiding force in his decision. It just 493 00:30:08,280 --> 00:30:12,000 Speaker 1: happened to pre date the rise of Nazis in Europe, 494 00:30:12,280 --> 00:30:14,280 Speaker 1: so he had already left by the time the Nazi 495 00:30:14,320 --> 00:30:18,160 Speaker 1: Party was starting to pick up steam in Germany in 496 00:30:18,200 --> 00:30:21,640 Speaker 1: the mid nineteen thirties. Von Neumann would become interested in 497 00:30:21,680 --> 00:30:25,920 Speaker 1: the problem of hydro dynamic turbulence and the theory of shocks. 498 00:30:26,520 --> 00:30:30,800 Speaker 1: This would become really important the next decade. This area 499 00:30:30,840 --> 00:30:34,600 Speaker 1: of interest was also really complicated. Is so complicated even 500 00:30:34,680 --> 00:30:38,440 Speaker 1: von Neuman's mind couldn't tackle some of these equations because 501 00:30:38,680 --> 00:30:43,360 Speaker 1: hydro dynamics is very counterintuitive, especially when it comes into 502 00:30:43,600 --> 00:30:46,600 Speaker 1: shock waves. So he would need a device to help 503 00:30:46,680 --> 00:30:50,840 Speaker 1: him suss out the more complicated nuances, and that began 504 00:30:51,000 --> 00:30:54,400 Speaker 1: von Neuman's interest in computer science. I'll talk a lot 505 00:30:54,480 --> 00:30:56,920 Speaker 1: more about that in the next episode as well. Now, 506 00:30:56,960 --> 00:31:00,280 Speaker 1: in his personal life, John and his wife Marriott had 507 00:31:00,320 --> 00:31:03,680 Speaker 1: a daughter named Marina, but von Neuman's private life was 508 00:31:03,760 --> 00:31:09,040 Speaker 1: not one of matrimonial bliss. According to biographies I researched, 509 00:31:09,240 --> 00:31:12,640 Speaker 1: he was affectionate towards his daughter, but he wasn't really 510 00:31:12,680 --> 00:31:16,040 Speaker 1: involved in her upbringing at all, or in the care 511 00:31:16,160 --> 00:31:19,160 Speaker 1: of the household in general. He considered that to be 512 00:31:19,240 --> 00:31:21,760 Speaker 1: the work for his wife, and that he was going 513 00:31:21,800 --> 00:31:25,840 Speaker 1: to just dedicate himself to his scholarly work and then 514 00:31:25,880 --> 00:31:30,000 Speaker 1: tying one on occasionally getting rip roaring drunk at parties. 515 00:31:30,480 --> 00:31:33,240 Speaker 1: That was his Those were his two interests. So his 516 00:31:33,280 --> 00:31:36,840 Speaker 1: relationship was strained. Now, Eventually Marriott would leave him and 517 00:31:37,000 --> 00:31:41,280 Speaker 1: the two would divorce. Interestingly, Marriette would go on to 518 00:31:41,360 --> 00:31:45,920 Speaker 1: Mary again. She married a physicist named James Brown Horner Cruper, 519 00:31:46,640 --> 00:31:50,920 Speaker 1: sometimes known as Desmond for some reason. This guy Cooper. 520 00:31:50,960 --> 00:31:54,000 Speaker 1: He was part of the radiation laboratory at m I T. 521 00:31:54,560 --> 00:31:57,080 Speaker 1: And you might remember I talked a lot about that 522 00:31:57,120 --> 00:32:01,000 Speaker 1: particular lab on my episodes about Alfred Loomis. So if 523 00:32:01,000 --> 00:32:03,040 Speaker 1: you want to learn more about that, look into the 524 00:32:03,040 --> 00:32:06,040 Speaker 1: Tech Stuff archives for the Alfred Loomis stories. Now, von 525 00:32:06,160 --> 00:32:09,320 Speaker 1: Neuman would be married a second time. His second wife 526 00:32:09,640 --> 00:32:14,320 Speaker 1: was Clara Dan. Clara was, like von Neuman, from Budapest. 527 00:32:14,680 --> 00:32:18,000 Speaker 1: She was also from a wealthy Jewish family. She was 528 00:32:18,040 --> 00:32:21,320 Speaker 1: born in nineteen eleven. As a teenager, she had become 529 00:32:21,360 --> 00:32:26,720 Speaker 1: a championship figure skater. She also had been married twice already. 530 00:32:26,720 --> 00:32:28,680 Speaker 1: She got married in nineteen thirty one to a man 531 00:32:28,760 --> 00:32:32,240 Speaker 1: named Farrank Ingle, but they were divorced a few years later. 532 00:32:32,960 --> 00:32:35,520 Speaker 1: Her second marriage was in ninety six to a man 533 00:32:35,640 --> 00:32:40,200 Speaker 1: named and Or Rapos. He was still married to her 534 00:32:40,280 --> 00:32:44,800 Speaker 1: when von Neuman struck up a relationship with her, so 535 00:32:44,960 --> 00:32:47,800 Speaker 1: they were technically they were both having affairs because von 536 00:32:47,880 --> 00:32:51,160 Speaker 1: Neuman's marriage had not come to an indiet the divorce 537 00:32:51,240 --> 00:32:55,880 Speaker 1: was still in process, so they end up getting into 538 00:32:55,920 --> 00:32:59,640 Speaker 1: a relationship with each other. Clara ends up divorcing her husband, 539 00:33:00,040 --> 00:33:03,120 Speaker 1: then Mary's von Neumann, and together they immigrate to the 540 00:33:03,160 --> 00:33:06,520 Speaker 1: United States. Clara was a remarkable woman in her own right. 541 00:33:06,640 --> 00:33:10,960 Speaker 1: Absolutely she made significant contributions. She would become the head 542 00:33:11,000 --> 00:33:14,760 Speaker 1: of statistical computing over at Princeton. She would become one 543 00:33:14,840 --> 00:33:19,480 Speaker 1: of the early computer programmers of the Mathematical Analyzer, Numerical Integrator, 544 00:33:19,520 --> 00:33:23,240 Speaker 1: and computer a k A. Maniac. More on that in 545 00:33:23,200 --> 00:33:28,440 Speaker 1: the next episode two. And she was also a tragic figure. 546 00:33:28,920 --> 00:33:33,040 Speaker 1: So John von Neumann died in nineteen fifty seven not 547 00:33:33,120 --> 00:33:36,080 Speaker 1: a spoiler alert, happened decades ago, but we'll talk about 548 00:33:36,120 --> 00:33:39,400 Speaker 1: that more in the next episode two. So after his death, 549 00:33:40,040 --> 00:33:42,480 Speaker 1: she would go on to Mary for a fourth time. 550 00:33:42,560 --> 00:33:45,480 Speaker 1: This time it was to a physicist named Carl Eckhart, 551 00:33:45,880 --> 00:33:48,880 Speaker 1: and in nineteen sixty three, she drove out to a 552 00:33:48,880 --> 00:33:53,600 Speaker 1: secluded beach in California. She walked out into the surf 553 00:33:54,280 --> 00:33:57,920 Speaker 1: and she drowned. The San Diego Coroner's office would rule 554 00:33:58,000 --> 00:34:01,760 Speaker 1: her death a suicide, so a very tragic ending for 555 00:34:01,800 --> 00:34:06,520 Speaker 1: her back to von Neuman to To wrap up this episode, 556 00:34:06,800 --> 00:34:09,560 Speaker 1: I've covered a lot of his work, his early work 557 00:34:09,600 --> 00:34:12,560 Speaker 1: in mathematics. In our next episode, we're going to learn 558 00:34:12,560 --> 00:34:16,920 Speaker 1: more about his involvement in the Manhattan Project. That's the 559 00:34:17,120 --> 00:34:20,600 Speaker 1: of course, the super secret project that was dedicated to 560 00:34:20,640 --> 00:34:23,719 Speaker 1: designing the atomic bomb. We'll also learned about how he 561 00:34:23,719 --> 00:34:27,319 Speaker 1: helped design computer systems, and we'll learn more about some 562 00:34:27,360 --> 00:34:30,879 Speaker 1: of his contributions to tech and science, as well as 563 00:34:31,800 --> 00:34:35,360 Speaker 1: some of what people have generously described as his personality quirks. 564 00:34:35,880 --> 00:34:39,000 Speaker 1: I would call them severe character flaws. We'll talk more 565 00:34:39,000 --> 00:34:41,400 Speaker 1: about those in the next episode. If you want to 566 00:34:41,480 --> 00:34:43,400 Speaker 1: learn more about the show, including how to get in 567 00:34:43,440 --> 00:34:46,279 Speaker 1: touch with me, go over to our website the addresses 568 00:34:46,320 --> 00:34:50,160 Speaker 1: tech Stuff Podcast dot com, and don't forget we have 569 00:34:50,200 --> 00:34:52,880 Speaker 1: a cool merchandise store. 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