1 00:00:10,600 --> 00:00:14,240 Speaker 1: Hello, and welcome to another episode of the Odd Lots podcast. 2 00:00:14,440 --> 00:00:17,720 Speaker 1: My co host Joe Wisenthal is away today, so it's 3 00:00:17,800 --> 00:00:20,960 Speaker 1: just going to be me for a bit. And one 4 00:00:21,000 --> 00:00:24,279 Speaker 1: of the discussions we've been having on Odd Lots over 5 00:00:24,320 --> 00:00:27,840 Speaker 1: the past year or so is this idea that traditional 6 00:00:27,880 --> 00:00:32,320 Speaker 1: economics doesn't actually do a great job of taking into 7 00:00:32,360 --> 00:00:36,400 Speaker 1: account a lot of important parts of the world economy 8 00:00:36,520 --> 00:00:39,000 Speaker 1: right now. And what I mean by that is, if 9 00:00:39,000 --> 00:00:41,720 Speaker 1: you look at something like the supply chain issues we've 10 00:00:41,760 --> 00:00:43,760 Speaker 1: been having in the market. We've been talking a lot 11 00:00:43,800 --> 00:00:47,120 Speaker 1: about the shortage in semiconductors, or if you look at 12 00:00:47,120 --> 00:00:51,839 Speaker 1: things like transportation gridlock, We've spoken tons about the chaos 13 00:00:51,880 --> 00:00:55,080 Speaker 1: and container shipping this year, a lot of those sort 14 00:00:55,080 --> 00:01:01,240 Speaker 1: of factors or complexities don't actually make it into traditional economics. So, 15 00:01:01,320 --> 00:01:03,960 Speaker 1: for instance, one of the things I learned this year 16 00:01:04,200 --> 00:01:08,320 Speaker 1: was that the sort of classical definition of comparative advantage, 17 00:01:08,600 --> 00:01:11,520 Speaker 1: where you know, one nation is very good at making 18 00:01:11,560 --> 00:01:13,680 Speaker 1: guns and the other nation is very good at making 19 00:01:13,720 --> 00:01:17,760 Speaker 1: butter or wine versus cloth, something like that, and so 20 00:01:17,800 --> 00:01:21,360 Speaker 1: they're supposed to trade with each other, that classical model 21 00:01:21,400 --> 00:01:24,480 Speaker 1: doesn't actually take into account transport costs or at least 22 00:01:24,480 --> 00:01:28,440 Speaker 1: it didn't when it was first proposed. So if economics 23 00:01:28,480 --> 00:01:32,080 Speaker 1: is all about simplifying assumptions in order to make it useful, 24 00:01:32,240 --> 00:01:34,839 Speaker 1: you kind of have to ask about the trade off 25 00:01:34,920 --> 00:01:39,840 Speaker 1: between simplifying everything and then overlooking quite important things that 26 00:01:39,920 --> 00:01:43,640 Speaker 1: are impacting the economy. So it's something that both Joe 27 00:01:43,640 --> 00:01:46,119 Speaker 1: and I have been thinking about this year. We've been 28 00:01:46,160 --> 00:01:51,160 Speaker 1: exploring alternative types of economics. We had Professor Steve Keene 29 00:01:51,200 --> 00:01:53,880 Speaker 1: on on a recent episode where he was talking about 30 00:01:53,920 --> 00:01:57,120 Speaker 1: how economics doesn't actually do a very good job of 31 00:01:57,160 --> 00:02:00,480 Speaker 1: taking into account things like energy costs, and we are 32 00:02:00,520 --> 00:02:05,320 Speaker 1: going to be continuing that discussion today. So I'm very, 33 00:02:05,400 --> 00:02:10,320 Speaker 1: very excited to introduce Brian Arthur. He's a economist who 34 00:02:10,360 --> 00:02:15,520 Speaker 1: specializes in something called complexity economics, which automatically kind of 35 00:02:15,560 --> 00:02:17,800 Speaker 1: sounds like something that we need to talk about in 36 00:02:17,840 --> 00:02:21,079 Speaker 1: the current environment. He's a professor at the Santa Fe 37 00:02:21,200 --> 00:02:25,040 Speaker 1: Institute and also a visiting researcher over at Park in 38 00:02:25,200 --> 00:02:28,440 Speaker 1: Palo Alto. So, Brian, thank you so much for coming on. 39 00:02:29,200 --> 00:02:33,240 Speaker 1: Thank you. Indeed, I'm really looking forward to this. So 40 00:02:33,400 --> 00:02:36,360 Speaker 1: I guess my first question has to be what is 41 00:02:36,480 --> 00:02:42,160 Speaker 1: complexity economics? Yeah, I love to take a deep breath here. 42 00:02:44,120 --> 00:02:49,160 Speaker 1: Economics has a lot of simplifying assumptions. I'm a theorist 43 00:02:49,160 --> 00:02:52,560 Speaker 1: and economics meaning I like to look at the economy 44 00:02:52,800 --> 00:02:58,240 Speaker 1: theoretically or formally, and we use a lot of mathematics 45 00:02:58,320 --> 00:03:03,480 Speaker 1: to do that. Sta underd economics. It's called neoclassical economics 46 00:03:04,040 --> 00:03:10,400 Speaker 1: bring this in a lot of simplifying assumptions that particular questions. 47 00:03:11,000 --> 00:03:13,919 Speaker 1: They generally it's like clearing the clutter and just saying 48 00:03:13,919 --> 00:03:16,280 Speaker 1: we want to get right down to the nitty gritty 49 00:03:16,280 --> 00:03:20,760 Speaker 1: in any problem. And to do that they in the 50 00:03:20,840 --> 00:03:25,040 Speaker 1: standard economics, we assume that all the problems that the 51 00:03:25,120 --> 00:03:28,920 Speaker 1: players in the economy face there might be investors, or 52 00:03:28,960 --> 00:03:34,359 Speaker 1: banks or our shipping companies. We assume that they're all 53 00:03:34,400 --> 00:03:40,640 Speaker 1: facing well defined problems, that they're all optimizing, meaning they're 54 00:03:40,680 --> 00:03:44,800 Speaker 1: doing the best that any mathematician could. They're hyper rational, 55 00:03:45,680 --> 00:03:50,040 Speaker 1: and the solutions they come up with are to be 56 00:03:50,160 --> 00:03:56,480 Speaker 1: an equilibrium. So the pattern is an equilibrium. Nobody in 57 00:03:56,520 --> 00:04:03,800 Speaker 1: these solutions has to change complexity economics not so much deliberately, 58 00:04:03,840 --> 00:04:07,880 Speaker 1: but we were motivated by the whole idea of looking 59 00:04:07,880 --> 00:04:12,200 Speaker 1: at the economy much realistically, and so we might start 60 00:04:12,360 --> 00:04:17,440 Speaker 1: by assuming that the banks, or government departments or companies 61 00:04:18,360 --> 00:04:22,279 Speaker 1: are different. They're not all identical, but once you assume 62 00:04:22,360 --> 00:04:27,919 Speaker 1: they differ, then they don't quite know that other companies. 63 00:04:28,880 --> 00:04:31,880 Speaker 1: Other companies obviously aren't the same as them. They don't 64 00:04:31,960 --> 00:04:35,280 Speaker 1: quite know what they might do, how much resources they have, 65 00:04:35,800 --> 00:04:41,159 Speaker 1: what technologies are available to them. So, generally speaking, when 66 00:04:41,200 --> 00:04:45,640 Speaker 1: you don't know about other people or other players, you're 67 00:04:46,200 --> 00:04:50,600 Speaker 1: subject to what economs called fundamental uncertainty. It's not just 68 00:04:50,760 --> 00:04:55,479 Speaker 1: that you can't put probabilities and things you simply don't know. 69 00:04:56,400 --> 00:04:59,679 Speaker 1: So it turns out then you get yourself very quickly 70 00:05:00,120 --> 00:05:05,200 Speaker 1: to mess The problems then that any individual agents is 71 00:05:05,279 --> 00:05:10,480 Speaker 1: facing are not well defined. They're not subject to logic. 72 00:05:10,880 --> 00:05:14,280 Speaker 1: You don't quite know what situation you're in. It's like 73 00:05:14,520 --> 00:05:17,520 Speaker 1: quite often landing in a country. You might be a 74 00:05:17,600 --> 00:05:21,680 Speaker 1: very skilled negotiator, but you land in some East Asian 75 00:05:21,720 --> 00:05:24,200 Speaker 1: country you've never been in. You don't know how pro 76 00:05:24,279 --> 00:05:29,160 Speaker 1: seizures are, what customers or or how business operates there. 77 00:05:29,720 --> 00:05:34,839 Speaker 1: So the situation generally for agents to quite some degree 78 00:05:35,480 --> 00:05:40,400 Speaker 1: object to uncertainty and not well defined you're in not 79 00:05:40,600 --> 00:05:43,360 Speaker 1: so much a mathematical problem then you're in an ill 80 00:05:43,480 --> 00:05:49,799 Speaker 1: defined situation. So in complexy economics, part of the economy 81 00:05:49,839 --> 00:05:54,919 Speaker 1: we're looking they're in some situation isn't well defined, but 82 00:05:54,960 --> 00:05:59,559 Speaker 1: they're not helpless. We act as human beings all the time, 83 00:05:59,600 --> 00:06:03,800 Speaker 1: and it will the situations we meet someone who out 84 00:06:03,880 --> 00:06:07,599 Speaker 1: with them to figure out what they're like or to 85 00:06:07,640 --> 00:06:10,480 Speaker 1: get to know people. I'm not sure what we're doing, 86 00:06:11,000 --> 00:06:15,360 Speaker 1: but so we assume that in the economy, agents are 87 00:06:16,120 --> 00:06:21,960 Speaker 1: maybe adopting different ideas or hypothesis, but situation they're in, 88 00:06:22,560 --> 00:06:27,080 Speaker 1: they're exploring, they're trying new things. Over time, they may 89 00:06:27,160 --> 00:06:31,400 Speaker 1: repeat what they're trying to learn whether something works, and 90 00:06:31,440 --> 00:06:35,000 Speaker 1: in general they drop things that don't work and the 91 00:06:35,160 --> 00:06:39,120 Speaker 1: experiment things that might work, and in that way they're 92 00:06:39,200 --> 00:06:44,960 Speaker 1: kind of bootstrapping way up. M hm. So let me ask, So, 93 00:06:45,080 --> 00:06:49,760 Speaker 1: this idea of not assuming that everyone acts in the 94 00:06:49,839 --> 00:06:53,440 Speaker 1: same rational manner, I have to say, is intuitively attractive 95 00:06:53,480 --> 00:06:56,680 Speaker 1: to me because I feel like I encounter my fair 96 00:06:56,720 --> 00:06:59,680 Speaker 1: share of irrational people on a daily basis, Maybe because 97 00:06:59,720 --> 00:07:02,920 Speaker 1: I and too much time on social media, but I 98 00:07:02,960 --> 00:07:05,120 Speaker 1: feel like naturally, if you look at the world, you 99 00:07:05,120 --> 00:07:08,000 Speaker 1: can see that people aren't always acting with their best 100 00:07:08,040 --> 00:07:12,640 Speaker 1: interest in mind, or at least they're certainly not agreeing 101 00:07:12,840 --> 00:07:15,680 Speaker 1: on what that best interest is um compared to other 102 00:07:15,840 --> 00:07:20,200 Speaker 1: actors or participants in a certain event or economic transaction. 103 00:07:20,680 --> 00:07:23,600 Speaker 1: But everyone is sort of So I guess what I'm 104 00:07:23,600 --> 00:07:26,880 Speaker 1: getting at is, in traditional economics, you assume everyone's rational, 105 00:07:26,960 --> 00:07:29,800 Speaker 1: they're all sort of pulling together in the same way. 106 00:07:29,840 --> 00:07:33,200 Speaker 1: But in something like complexity economics, if you assume people 107 00:07:33,560 --> 00:07:36,160 Speaker 1: can be irrational, or at least they look at things 108 00:07:36,160 --> 00:07:40,200 Speaker 1: from different perspectives, they're looking at them from a variety 109 00:07:40,240 --> 00:07:42,920 Speaker 1: of different perspectives. Right, So how do you actually go 110 00:07:43,040 --> 00:07:49,920 Speaker 1: about incorporating that into a model. You might have pretty 111 00:07:49,920 --> 00:07:55,560 Speaker 1: good guidance from behavioral economics. You might have some good 112 00:07:55,640 --> 00:07:59,280 Speaker 1: idea you had to show on earlier that I very 113 00:07:59,320 --> 00:08:03,640 Speaker 1: much like about container shipping, And you can launch a 114 00:08:03,680 --> 00:08:08,280 Speaker 1: container ship summer, maybe in Singapore's something, try to bring 115 00:08:08,280 --> 00:08:13,320 Speaker 1: it to Rotterdam, and then somehow things break down in 116 00:08:13,360 --> 00:08:17,240 Speaker 1: a so as canal. You simply don't quite know when 117 00:08:17,280 --> 00:08:20,000 Speaker 1: the canal is going to be cleared. You can't really 118 00:08:20,440 --> 00:08:24,640 Speaker 1: rely on probabilities. You're not sure what people you're doing 119 00:08:24,880 --> 00:08:29,120 Speaker 1: will do, and so you start to form ideas. But 120 00:08:29,200 --> 00:08:33,960 Speaker 1: if everybody is doing this mutually, then the situation you're 121 00:08:34,040 --> 00:08:39,680 Speaker 1: in being caused by many agents payers together keep shifting 122 00:08:40,040 --> 00:08:43,280 Speaker 1: the stock markets like this. So it's not that there's 123 00:08:43,440 --> 00:08:47,839 Speaker 1: a national solution. When a problem is ill defined or 124 00:08:47,920 --> 00:08:52,240 Speaker 1: not well defined, there is no rational station. There's only 125 00:08:52,280 --> 00:08:57,439 Speaker 1: a rational solution if the problems rational or logical in 126 00:08:57,600 --> 00:09:01,760 Speaker 1: this sort of really hazy situation, and I would maintain 127 00:09:02,240 --> 00:09:07,760 Speaker 1: the norm everybody's mutually exploring. So take the stock market 128 00:09:07,880 --> 00:09:11,640 Speaker 1: for example. In this form of economics, there isn't a 129 00:09:11,720 --> 00:09:16,160 Speaker 1: rational solution. There might be if everyone was doing something perfect, 130 00:09:16,200 --> 00:09:19,600 Speaker 1: then you might lose money. Of course you're gonna only 131 00:09:19,720 --> 00:09:22,640 Speaker 1: but you might lose money because you're doing something different 132 00:09:23,120 --> 00:09:28,240 Speaker 1: from the smart people. But usually don't know what other 133 00:09:28,400 --> 00:09:32,200 Speaker 1: people are doing. You might have a good idea, you 134 00:09:32,320 --> 00:09:36,320 Speaker 1: might have some good guidance, but I don't quite know 135 00:09:36,559 --> 00:09:40,240 Speaker 1: what you're up against. What's going to happen. In this 136 00:09:40,400 --> 00:09:46,120 Speaker 1: sort of economics, you're not assuming there is a well defined, mathematical, logical, 137 00:09:46,760 --> 00:09:51,160 Speaker 1: rational solution. You're basically saying, we're all in this together 138 00:09:51,240 --> 00:09:54,199 Speaker 1: and we're mutually trying to get smart. Let me give 139 00:09:54,240 --> 00:09:58,839 Speaker 1: you a very quick example. You might be say training 140 00:09:59,559 --> 00:10:03,760 Speaker 1: multiple people to play chess or go. I'm thinking of 141 00:10:03,960 --> 00:10:10,520 Speaker 1: the big breakthrough in AI where Alpha Go learned how 142 00:10:10,600 --> 00:10:14,760 Speaker 1: to how to play really really smart games of Go. 143 00:10:15,920 --> 00:10:19,560 Speaker 1: And they started off by assuming that there are other 144 00:10:19,760 --> 00:10:23,800 Speaker 1: players that didn't know much, and they kept trying things 145 00:10:24,000 --> 00:10:27,240 Speaker 1: and seeing what worked. And it wasn't as if there 146 00:10:27,320 --> 00:10:31,560 Speaker 1: was a rational solution, or if there was that might 147 00:10:31,960 --> 00:10:38,760 Speaker 1: take many lifetimes of many, many, many universes to find. 148 00:10:39,360 --> 00:10:43,079 Speaker 1: It was much more that you're playing against something, You're 149 00:10:43,120 --> 00:10:47,200 Speaker 1: trying to figure out what would work well in that situation. 150 00:10:47,320 --> 00:10:49,720 Speaker 1: You're the only thing it can do is to look 151 00:10:49,760 --> 00:10:54,679 Speaker 1: for good strategies. The reason we all as complexity as 152 00:10:54,720 --> 00:10:57,840 Speaker 1: that other people are doing the same and as they're 153 00:10:58,040 --> 00:11:02,280 Speaker 1: learning and they're shifting and they're trying to try out 154 00:11:02,360 --> 00:11:08,520 Speaker 1: new ideas and strategies, then the problem you're in keeps changing. 155 00:11:08,920 --> 00:11:12,719 Speaker 1: So the market changing as people get smarter, but as 156 00:11:12,800 --> 00:11:16,480 Speaker 1: people get smarter, things are shifting and you have to 157 00:11:16,559 --> 00:11:22,080 Speaker 1: shift what you're exploring. So we're backing off from the 158 00:11:22,120 --> 00:11:27,280 Speaker 1: whole idea there's a perfect solution. It's rather saying that 159 00:11:27,320 --> 00:11:30,360 Speaker 1: we're trying to see what works and that keeps shifting. 160 00:11:32,080 --> 00:11:35,280 Speaker 1: So something like the stock market um, if you look 161 00:11:35,320 --> 00:11:38,440 Speaker 1: at the stock market. So I guess the implication here 162 00:11:38,640 --> 00:11:43,120 Speaker 1: is that you're never really reaching an optimum outcome in 163 00:11:43,160 --> 00:11:46,000 Speaker 1: the stock market because you don't know what that outcome 164 00:11:46,120 --> 00:11:49,400 Speaker 1: should actually look like. And so stocks are always moving, 165 00:11:49,640 --> 00:11:53,680 Speaker 1: people are always trading because they're trying new things, and 166 00:11:53,720 --> 00:11:57,040 Speaker 1: they're sort of bouncing off of each other. So maybe 167 00:11:57,200 --> 00:12:00,760 Speaker 1: you know, one year people are really into val you investing, 168 00:12:00,880 --> 00:12:04,480 Speaker 1: and then the next year it turns into momentum and 169 00:12:04,600 --> 00:12:07,679 Speaker 1: everyone's sort of um interacting with each other and the 170 00:12:07,720 --> 00:12:11,360 Speaker 1: system itself um the dominant way of acting in the 171 00:12:11,400 --> 00:12:15,640 Speaker 1: system changes over time. Yeah, I'd say it's very much 172 00:12:15,720 --> 00:12:18,400 Speaker 1: like that. But I want to stress something here, and 173 00:12:18,440 --> 00:12:22,400 Speaker 1: that is there isn't an optimal outcome if you don't know. 174 00:12:23,400 --> 00:12:28,199 Speaker 1: If you know that there's probability that copper futures or 175 00:12:28,760 --> 00:12:32,680 Speaker 1: our costs for containerships are going to do search and 176 00:12:32,760 --> 00:12:37,160 Speaker 1: search for something else. Yeah, you can figure out something rational, 177 00:12:37,520 --> 00:12:40,600 Speaker 1: but if you don't know, I don't know when there's 178 00:12:40,600 --> 00:12:43,360 Speaker 1: going to be a big hold up in the source canal. 179 00:12:43,640 --> 00:12:46,800 Speaker 1: I don't know what's fashionable and will come along. You 180 00:12:46,920 --> 00:12:50,480 Speaker 1: don't know there fur there isn't some lurking in the 181 00:12:50,559 --> 00:12:55,720 Speaker 1: back nd behind some curtains, some optimal solution. You're trying 182 00:12:55,760 --> 00:12:59,840 Speaker 1: to make your way in a situation that is create 183 00:13:00,000 --> 00:13:04,040 Speaker 1: it by other people who don't know trying to do 184 00:13:04,280 --> 00:13:23,080 Speaker 1: their best in the situation. We talked a little bit 185 00:13:23,120 --> 00:13:26,960 Speaker 1: about how traditional economics, neoclassical economics is all about these 186 00:13:26,960 --> 00:13:32,080 Speaker 1: simplifying assumptions and sort of framing the world into a 187 00:13:32,280 --> 00:13:36,000 Speaker 1: model that you can then use to tell you something 188 00:13:36,160 --> 00:13:39,000 Speaker 1: about it, or you know, about how people might act 189 00:13:39,040 --> 00:13:42,680 Speaker 1: and what the effects might be. It sounds like, given 190 00:13:42,720 --> 00:13:46,760 Speaker 1: the intricacies of complexity economics, um the fact that you're, 191 00:13:47,640 --> 00:13:51,280 Speaker 1: you know, not just dealing with these uncertain situations but 192 00:13:51,440 --> 00:13:54,120 Speaker 1: laser focused on them, and the fact that you're kind 193 00:13:54,160 --> 00:13:59,320 Speaker 1: of looking into the feedback loops created by different sorts 194 00:13:59,440 --> 00:14:03,400 Speaker 1: of behavior. If you're it just sounds really um, I'm 195 00:14:03,440 --> 00:14:06,040 Speaker 1: trying to come up with a synonym for complex but like, 196 00:14:06,440 --> 00:14:09,000 Speaker 1: it is all about the complexity. So I guess my 197 00:14:09,080 --> 00:14:13,240 Speaker 1: question is how difficult is it to do economics in 198 00:14:13,280 --> 00:14:17,400 Speaker 1: this way? Well, I don't think that my group at 199 00:14:17,440 --> 00:14:21,760 Speaker 1: Santa Fe was first to think this way of economics. 200 00:14:22,200 --> 00:14:24,520 Speaker 1: People could do this or think about this in the 201 00:14:24,600 --> 00:14:31,080 Speaker 1: past but in the nine eighties nine nineties, computation came along, 202 00:14:31,720 --> 00:14:36,680 Speaker 1: so we could of computers to do standard mathematics, you know, 203 00:14:36,720 --> 00:14:40,880 Speaker 1: writing down equations using algebra and calculus. You have to 204 00:14:41,320 --> 00:14:46,320 Speaker 1: simplify an awful lot, and so I've no argument against 205 00:14:46,360 --> 00:14:48,920 Speaker 1: what was done for about a hundred and fifty years, 206 00:14:49,480 --> 00:14:54,280 Speaker 1: hundred and twenty years. At that time, economists wrote down equations, 207 00:14:54,320 --> 00:14:59,040 Speaker 1: they made very strong simplifying assumptions, and they've got really 208 00:14:59,120 --> 00:15:06,000 Speaker 1: good results. However, the enarmy has changed and we got computation. 209 00:15:06,600 --> 00:15:10,240 Speaker 1: Let me give you an example one that's it is 210 00:15:10,280 --> 00:15:14,880 Speaker 1: probably easy to picture with complexity. You're basically looking at 211 00:15:15,000 --> 00:15:20,440 Speaker 1: elements anywhere over time, forming patterns that those elements, in 212 00:15:20,520 --> 00:15:25,000 Speaker 1: turn are trying to react to. The elements might parse 213 00:15:25,600 --> 00:15:31,000 Speaker 1: in traffics densely packed in along some freeway or something, 214 00:15:31,680 --> 00:15:34,920 Speaker 1: and the elements are creating something that we choose to 215 00:15:34,960 --> 00:15:40,520 Speaker 1: call traffic the local cars around them, and they're reacting 216 00:15:40,720 --> 00:15:45,480 Speaker 1: to the traffic. Now, there isn't an optimal strategy for 217 00:15:45,520 --> 00:15:48,400 Speaker 1: any car. You don't know what the other cars you're 218 00:15:48,400 --> 00:15:52,040 Speaker 1: gonna do, but can watch what they're doing, and you 219 00:15:52,080 --> 00:15:56,600 Speaker 1: can start to have simple roots. If this car gets 220 00:15:56,640 --> 00:16:01,320 Speaker 1: too close, I'm going to change lanes. This was very 221 00:16:01,400 --> 00:16:04,480 Speaker 1: hard to do with the standard mathematical set up, too 222 00:16:04,480 --> 00:16:10,600 Speaker 1: many moving parts. So once compute came along, we could 223 00:16:10,680 --> 00:16:14,800 Speaker 1: look at situations like that in real time. We could 224 00:16:14,880 --> 00:16:19,160 Speaker 1: give the cars maybe simple rules and say, well, if 225 00:16:19,200 --> 00:16:22,160 Speaker 1: the cars in front get closer, then I'm going to 226 00:16:22,800 --> 00:16:29,120 Speaker 1: break slightly until the situations restored. And so we're looking 227 00:16:29,160 --> 00:16:33,000 Speaker 1: at how patterns like that move and change. It's a 228 00:16:33,040 --> 00:16:35,560 Speaker 1: bit the same in the stock market or maybe in 229 00:16:35,680 --> 00:16:42,920 Speaker 1: some complicated trading situation. You can't unload your freighters in 230 00:16:43,280 --> 00:16:46,840 Speaker 1: say Cape Town. You have to put in there maybe 231 00:16:47,080 --> 00:16:50,840 Speaker 1: to offload, but to find there's delays you didn't even 232 00:16:50,880 --> 00:16:55,200 Speaker 1: think of. So everything's adjusting, readjusting, and you're trying to 233 00:16:55,520 --> 00:16:59,280 Speaker 1: learn along the way what works, what doesn't work. We 234 00:16:59,320 --> 00:17:04,000 Speaker 1: can study that by computers, be almost impossible to set 235 00:17:04,080 --> 00:17:09,760 Speaker 1: up fixed equations to try to study that. So this 236 00:17:09,880 --> 00:17:14,840 Speaker 1: sort of economics. I have beliefs, our actions or strategies 237 00:17:15,240 --> 00:17:20,399 Speaker 1: driving my car, but other players have other drivers as well, 238 00:17:21,000 --> 00:17:24,480 Speaker 1: and I'm trying to do my best in that. But 239 00:17:24,600 --> 00:17:28,320 Speaker 1: there is no optimizing to do some because you don't 240 00:17:28,359 --> 00:17:31,480 Speaker 1: fully know that people are going to do this sort 241 00:17:31,480 --> 00:17:37,120 Speaker 1: of economics. Complexity economics is viewing the world. The situation 242 00:17:37,200 --> 00:17:40,959 Speaker 1: has a little of I'm trying out actions or strategies 243 00:17:41,440 --> 00:17:46,200 Speaker 1: in a world created by other people trying out actions 244 00:17:46,240 --> 00:17:50,200 Speaker 1: and strategies, but I'm not sure what they're trying out 245 00:17:50,320 --> 00:17:53,200 Speaker 1: or what they will think of next. So how does 246 00:17:53,359 --> 00:17:57,040 Speaker 1: all that operate? So let me ask you a question 247 00:17:57,040 --> 00:18:00,240 Speaker 1: about UM. I guess second order effects because I think 248 00:18:00,240 --> 00:18:05,040 Speaker 1: it will help understand complexity economics. But does the school 249 00:18:05,080 --> 00:18:10,760 Speaker 1: of thinking lead to different policies or different policy recommendations 250 00:18:10,800 --> 00:18:15,120 Speaker 1: compared to neo classical economics and what would what would 251 00:18:15,119 --> 00:18:18,240 Speaker 1: the difference be? Well, I'll give you two instances where 252 00:18:18,400 --> 00:18:23,280 Speaker 1: it certainly does. We can talk about economies. We can 253 00:18:23,320 --> 00:18:28,119 Speaker 1: talk about situations where people are trying to do their best, 254 00:18:28,400 --> 00:18:34,360 Speaker 1: trying to learn what works, trying to explore an experiment mutually, 255 00:18:34,960 --> 00:18:39,640 Speaker 1: and that changes and recreates a situation. And to do that, 256 00:18:39,720 --> 00:18:44,280 Speaker 1: I said, you have to contract that if you're brilliant 257 00:18:44,320 --> 00:18:48,399 Speaker 1: maybe and remember everything. But normally we use computers to 258 00:18:49,240 --> 00:18:55,720 Speaker 1: track quote happening, and this allows us. Once we use 259 00:18:55,880 --> 00:19:01,240 Speaker 1: computers and we're not using equations us, what we can 260 00:19:01,280 --> 00:19:06,080 Speaker 1: do is set up models in our computers, so this 261 00:19:06,280 --> 00:19:11,320 Speaker 1: immediately allows us to use more details. Your readers are 262 00:19:11,359 --> 00:19:16,199 Speaker 1: probably are sorry. Your listeners are probably familiar with the 263 00:19:16,200 --> 00:19:20,639 Speaker 1: whole idea that there are very good mathematical models safe 264 00:19:21,080 --> 00:19:26,760 Speaker 1: epidemics and pandemics, but they're pretty simple, because all mathematics 265 00:19:26,800 --> 00:19:29,680 Speaker 1: has to be kept simple if you want to turn 266 00:19:29,760 --> 00:19:33,760 Speaker 1: the crank and get solutions. At the start of the 267 00:19:33,840 --> 00:19:37,760 Speaker 1: pandemic about a year ago or in the early days, 268 00:19:38,359 --> 00:19:42,480 Speaker 1: we assumed that people were either affected or they were 269 00:19:42,520 --> 00:19:47,080 Speaker 1: not infected. We assume that they interacted certain rates and 270 00:19:47,200 --> 00:19:51,199 Speaker 1: infect each other. Turned out that detail didn't give you 271 00:19:51,320 --> 00:19:57,640 Speaker 1: enough realism and you could start. Then once you look 272 00:19:57,640 --> 00:20:00,959 Speaker 1: at things by computer, you kind of much more detailed 273 00:20:01,119 --> 00:20:06,720 Speaker 1: models and say, oh, well, people in retirement homes are 274 00:20:06,760 --> 00:20:10,760 Speaker 1: not partying every night, but in their twenties might be, 275 00:20:11,320 --> 00:20:14,920 Speaker 1: and so the dynamics might be very different. There might 276 00:20:14,920 --> 00:20:20,800 Speaker 1: be networks of interaction. Once we agree to be realistic, 277 00:20:21,440 --> 00:20:26,159 Speaker 1: what do we gave vaccines to older people? Well, obviously 278 00:20:26,280 --> 00:20:30,320 Speaker 1: deaths will go down because they're most at risk, But 279 00:20:30,440 --> 00:20:34,080 Speaker 1: what do we gave vaccines to younger people because they 280 00:20:34,119 --> 00:20:36,320 Speaker 1: may be the ones that are doing most of the 281 00:20:36,440 --> 00:20:43,520 Speaker 1: these transmission late at night in out at parties and bars. 282 00:20:43,720 --> 00:20:46,800 Speaker 1: We can use the computer as what we call a 283 00:20:46,880 --> 00:20:52,320 Speaker 1: policy lab. We can do some realistically that the agents 284 00:20:52,359 --> 00:20:55,200 Speaker 1: in the economy. What we're looking at in this case, 285 00:20:55,359 --> 00:21:00,520 Speaker 1: say COVID that agents differ their different ages. Maybe they 286 00:21:00,560 --> 00:21:05,399 Speaker 1: have their own networks that we can describe and we 287 00:21:05,480 --> 00:21:10,040 Speaker 1: can get much better detail. Let me give you one other, 288 00:21:11,560 --> 00:21:18,359 Speaker 1: one other thing that might interest your listeners, very different example, 289 00:21:19,400 --> 00:21:22,040 Speaker 1: if you're doing standard economics, you want to write down 290 00:21:22,080 --> 00:21:26,119 Speaker 1: a few simple equations, not very many, otherwise it gets 291 00:21:26,160 --> 00:21:30,600 Speaker 1: too unwieldy logically, and very often, as I said, we 292 00:21:30,640 --> 00:21:36,359 Speaker 1: as soon everybody's the same. So maybe the year is nine, 293 00:21:37,000 --> 00:21:44,919 Speaker 1: and there's a possibility of having things computers, television's and 294 00:21:45,000 --> 00:21:49,640 Speaker 1: so on manufactured abroad, possibly in China somewhere like that, 295 00:21:50,080 --> 00:21:53,320 Speaker 1: and you want to see if that's that's a good 296 00:21:53,400 --> 00:21:57,000 Speaker 1: trade policy. Standard trade theory would say, oh, yeah, have 297 00:21:57,119 --> 00:22:01,679 Speaker 1: the Chinese can do that more cheaply, We should have that, 298 00:22:01,800 --> 00:22:07,240 Speaker 1: and then we can sell them. Soy beans two make 299 00:22:07,240 --> 00:22:10,880 Speaker 1: the revenue, so we conmpare them. So on. But when 300 00:22:10,920 --> 00:22:15,280 Speaker 1: we did that sort of modeling around, just to keep 301 00:22:15,480 --> 00:22:19,760 Speaker 1: the whole problem simple, we tended to assume that everybody 302 00:22:19,760 --> 00:22:24,040 Speaker 1: in the US was the same. What we didn't do, 303 00:22:24,359 --> 00:22:27,880 Speaker 1: and what you can do now if you're willse computation, 304 00:22:28,800 --> 00:22:32,520 Speaker 1: is to say, well, maybe the agents different. Maybe there's 305 00:22:33,280 --> 00:22:39,160 Speaker 1: farmers in the middle of America, maybe there's manufacturing sectors 306 00:22:39,240 --> 00:22:43,320 Speaker 1: saying the what now called the rust belt. If all 307 00:22:43,359 --> 00:22:48,200 Speaker 1: these jobs go to China, it doesn't affect the United 308 00:22:48,240 --> 00:22:53,480 Speaker 1: States homelessly, but we may be differentially put out of jobs. 309 00:22:53,560 --> 00:22:58,080 Speaker 1: Everybody in California might be much better off. Everybody in Hawaii, 310 00:22:58,359 --> 00:23:04,040 Speaker 1: in Ohio maybe art because they've lost their manufacturing jobs. 311 00:23:04,200 --> 00:23:08,480 Speaker 1: So what we're finding said, if we had had better 312 00:23:08,560 --> 00:23:15,600 Speaker 1: models around, mathematical models or economic models, we're been so 313 00:23:15,800 --> 00:23:19,320 Speaker 1: quick to say, oh yeah, let's shift all the textiles 314 00:23:19,440 --> 00:23:25,280 Speaker 1: and the electronics off to Asia, and they're cheaper labor, 315 00:23:25,760 --> 00:23:29,200 Speaker 1: so everybody in the US will be better off. That 316 00:23:29,400 --> 00:23:32,080 Speaker 1: wasn't what happened. People in the middle of the country 317 00:23:32,160 --> 00:23:34,760 Speaker 1: were put out of work. And then if you had 318 00:23:34,800 --> 00:23:37,359 Speaker 1: detailed models, you might have seen if they were put 319 00:23:37,359 --> 00:23:42,000 Speaker 1: out of work, you might have massive social disruptions. You 320 00:23:42,119 --> 00:23:47,800 Speaker 1: might have quite turbulent politics, which happened. You might have 321 00:23:48,240 --> 00:23:54,560 Speaker 1: opioid crises, suicides, all of which showed up regionally, but 322 00:23:54,680 --> 00:23:58,520 Speaker 1: weren't taken account of in the models. I think the 323 00:23:58,600 --> 00:24:02,280 Speaker 1: policy is very important. If you permit, let me give 324 00:24:02,280 --> 00:24:06,879 Speaker 1: you one other example. Yeah, go for it. This is fascinating. Okay, 325 00:24:07,200 --> 00:24:13,600 Speaker 1: there's another thing I want to iterate that standard economics 326 00:24:13,640 --> 00:24:16,960 Speaker 1: I think does an excellent job. I'm trained as a 327 00:24:17,000 --> 00:24:21,040 Speaker 1: neoclassical economist, and I think it does a very good job. 328 00:24:21,080 --> 00:24:26,119 Speaker 1: But what standard economics stores is it simplifies will, so 329 00:24:26,359 --> 00:24:29,800 Speaker 1: everybody is the same will as soon that the outcome 330 00:24:29,840 --> 00:24:34,280 Speaker 1: will be an equilibrium, meaning that there's no player, no 331 00:24:34,560 --> 00:24:38,600 Speaker 1: agent involved, no bank that would want to do anything 332 00:24:38,680 --> 00:24:45,960 Speaker 1: different because all the incentives are in balance. And I 333 00:24:46,000 --> 00:24:52,080 Speaker 1: think that standard economics is really really good at allowing 334 00:24:52,160 --> 00:24:56,480 Speaker 1: us to understand the economy, not just to control and 335 00:24:56,600 --> 00:25:03,560 Speaker 1: manipulate what happens politically or economically, but to really understand things. 336 00:25:03,680 --> 00:25:08,199 Speaker 1: But and this is a huge but standard economics hasn't 337 00:25:08,200 --> 00:25:14,159 Speaker 1: been able to deal with crises, financial collapses like in 338 00:25:14,200 --> 00:25:19,320 Speaker 1: two thousand and eight, energy market disruptions like what happened 339 00:25:19,359 --> 00:25:24,160 Speaker 1: in the two thousands in the electricity market here in California. 340 00:25:24,800 --> 00:25:30,080 Speaker 1: Why don't we see these crises and collapses with the 341 00:25:30,240 --> 00:25:35,399 Speaker 1: standard economics of course a few economists do. But if 342 00:25:35,440 --> 00:25:41,560 Speaker 1: we're doing standard theory, you don't see financial collapse the coming, say, 343 00:25:41,680 --> 00:25:46,160 Speaker 1: like in two thousand and eight with the subprime lending market. 344 00:25:46,800 --> 00:25:52,800 Speaker 1: Why the reason is subtle thing is an equilibrium. Nobody 345 00:25:52,880 --> 00:25:56,200 Speaker 1: has any incentive to do anything different. If you make 346 00:25:56,240 --> 00:26:01,040 Speaker 1: that assumption, all your models will bear that out there, 347 00:26:01,119 --> 00:26:07,920 Speaker 1: for nobody can come up with some new strategy they 348 00:26:07,960 --> 00:26:11,720 Speaker 1: think of to manipulate the system, or to rig the 349 00:26:11,840 --> 00:26:16,080 Speaker 1: system in their own favor, precisely what we saw happen 350 00:26:16,160 --> 00:26:21,560 Speaker 1: in two thousand and eight. So we're assuming that the economy, 351 00:26:21,760 --> 00:26:25,080 Speaker 1: go back to thinking about the stock market is an 352 00:26:25,080 --> 00:26:29,440 Speaker 1: open system that people are discovering new things all the time. 353 00:26:30,000 --> 00:26:34,199 Speaker 1: They're discovering new ways, possibly to manipulate the system in 354 00:26:34,240 --> 00:26:40,719 Speaker 1: their favor. Under complexity economics, you're not assuming the outcome 355 00:26:40,960 --> 00:26:45,240 Speaker 1: is an equilibrium. Minobody has an incentive to do anything different. 356 00:26:46,119 --> 00:26:50,360 Speaker 1: The way I would summarize complexity economics is it's viewing 357 00:26:51,520 --> 00:26:57,199 Speaker 1: the economy as an evolving system. The economy is like 358 00:26:57,280 --> 00:27:02,480 Speaker 1: an ecology. Every so often, new players discover new strategies. 359 00:27:03,040 --> 00:27:08,600 Speaker 1: Just like in a real ecology, new species arise, and 360 00:27:08,840 --> 00:27:13,760 Speaker 1: things that looked to be an equilibrium before suddenly thrown 361 00:27:13,760 --> 00:27:19,360 Speaker 1: out of equilibrium. It's like introducing a new fish into 362 00:27:19,480 --> 00:27:25,200 Speaker 1: Lake Victoria suddenly that really upsets the system and everything. 363 00:27:25,240 --> 00:27:31,399 Speaker 1: That's just by assuming that we're not necessarily in equilibrium, 364 00:27:31,480 --> 00:27:35,480 Speaker 1: where by assuming that problems are not necessarily well defined, 365 00:27:35,920 --> 00:27:42,440 Speaker 1: we're basically looking at the economy ecologically. We can see 366 00:27:42,440 --> 00:28:03,160 Speaker 1: all kinds of phenomena arising. So it sounds almost like 367 00:28:03,840 --> 00:28:09,760 Speaker 1: complexity economics is about, rather than displacing other schools of 368 00:28:09,800 --> 00:28:13,320 Speaker 1: economics or neo classical economics, it's more about sort of 369 00:28:13,440 --> 00:28:18,919 Speaker 1: augmenting or complementing the methodology and making the whole the 370 00:28:18,960 --> 00:28:22,480 Speaker 1: model is more robust and more sophisticated in being able 371 00:28:22,520 --> 00:28:26,879 Speaker 1: to take into account changing um situations. Yeah, I would 372 00:28:26,920 --> 00:28:30,679 Speaker 1: say that, but I wouldn't say it's an add on 373 00:28:31,320 --> 00:28:37,400 Speaker 1: standard economics because standard nomics does have standard assumptions, all 374 00:28:37,560 --> 00:28:42,640 Speaker 1: problems are well defined, everything happens at an equilibrium, and 375 00:28:42,680 --> 00:28:47,000 Speaker 1: so on. It's more like every so often in mathematics 376 00:28:47,080 --> 00:28:52,840 Speaker 1: or physics, to throw up the basic axioms. In mathematics, 377 00:28:52,880 --> 00:28:56,400 Speaker 1: you could throw out the parallel acts. You could skip 378 00:28:56,520 --> 00:29:00,360 Speaker 1: facts um and say what mathematics look like if if 379 00:29:00,400 --> 00:29:04,080 Speaker 1: you didn't have that axiom, and you get very different 380 00:29:04,120 --> 00:29:09,000 Speaker 1: sort of mathematics. So what we're really doing is trying 381 00:29:09,040 --> 00:29:14,440 Speaker 1: to move towards realism, saying okay, people are not just 382 00:29:14,680 --> 00:29:18,520 Speaker 1: muddling along. They might be doing quite sophisticated things, but 383 00:29:18,840 --> 00:29:23,120 Speaker 1: everybody is doing that, and as they do that, the 384 00:29:23,280 --> 00:29:27,320 Speaker 1: patterns are forming differently. So I would not say this 385 00:29:27,640 --> 00:29:32,400 Speaker 1: is an addition to the standard approach. I think the 386 00:29:33,240 --> 00:29:38,080 Speaker 1: subtle up that says some problems in the economy, in fact, 387 00:29:38,360 --> 00:29:44,800 Speaker 1: very many are perfectly well looked at with equilibrium economics. 388 00:29:45,720 --> 00:29:50,480 Speaker 1: It might be that the markets, for example, from day 389 00:29:50,560 --> 00:29:55,760 Speaker 1: to day the price is roughly an equilibrium markets. Clear 390 00:29:56,640 --> 00:30:01,080 Speaker 1: sort of economics is perfectly appropriate. So it's more like 391 00:30:01,320 --> 00:30:05,000 Speaker 1: saying that if the wind doesn't blow, the stea will 392 00:30:05,080 --> 00:30:10,000 Speaker 1: go flat. There's that doesn't contradict anything, but there are 393 00:30:10,880 --> 00:30:15,200 Speaker 1: different sorts of questions. If the economy is just talking 394 00:30:15,240 --> 00:30:20,840 Speaker 1: about how quantities are produced and production and consumption and 395 00:30:20,960 --> 00:30:24,640 Speaker 1: patterns of that, the standard approach I think is pretty good. 396 00:30:24,800 --> 00:30:27,920 Speaker 1: I have no quarrel with it. However, if you're asking 397 00:30:28,520 --> 00:30:32,160 Speaker 1: how do new products come along? How do new strategy 398 00:30:32,320 --> 00:30:35,920 Speaker 1: is developed? I'm sitting here in Silicon Valley, and there's 399 00:30:35,960 --> 00:30:40,640 Speaker 1: no such thing really in the tech business as equilibrium, 400 00:30:41,280 --> 00:30:46,040 Speaker 1: where exploring here how machine learning and AI work, how 401 00:30:47,240 --> 00:30:52,800 Speaker 1: types of biotech and proteomics work, and that's changing month 402 00:30:52,920 --> 00:30:57,840 Speaker 1: by month. So how do you maneuver and how does 403 00:30:57,960 --> 00:31:02,720 Speaker 1: the economy work? If they sentience are changing around you 404 00:31:03,040 --> 00:31:08,400 Speaker 1: and you have to readjust and do things differently. It's 405 00:31:08,440 --> 00:31:12,720 Speaker 1: a bit like surfing years ago. I used to believe 406 00:31:12,760 --> 00:31:16,600 Speaker 1: it or not, I used to surf in Hawaii. It's 407 00:31:16,680 --> 00:31:21,040 Speaker 1: not that there's an optimal approach to a wave. If 408 00:31:21,120 --> 00:31:23,320 Speaker 1: there is, I haven't seen it yet because you don't 409 00:31:23,480 --> 00:31:27,760 Speaker 1: know where the wave is going to go next. So 410 00:31:27,960 --> 00:31:32,600 Speaker 1: what you're doing is to adjusting your balance, adjusting your direction. 411 00:31:33,120 --> 00:31:37,760 Speaker 1: You're looking ahead maybe twenty feet, maybe fifty ft or 412 00:31:37,760 --> 00:31:40,720 Speaker 1: a hundred feet, and you're trying to be better to 413 00:31:40,800 --> 00:31:43,320 Speaker 1: be over there. You want to avoid the white water 414 00:31:43,440 --> 00:31:47,480 Speaker 1: instead in green water, and so on. So this is 415 00:31:47,680 --> 00:31:53,080 Speaker 1: really an economics where you're looking at adjustment, things forming, 416 00:31:53,760 --> 00:31:56,960 Speaker 1: and you start to ask questions like where the labor 417 00:31:57,080 --> 00:32:02,000 Speaker 1: unions come from, where the insurance companies come from? Historically, 418 00:32:02,720 --> 00:32:07,920 Speaker 1: where do legal arrangements come from? You can begin to 419 00:32:08,080 --> 00:32:12,200 Speaker 1: see where in that murky uncertain world. We don't know, 420 00:32:12,520 --> 00:32:15,600 Speaker 1: for example, what sort of legal arrangements there should be 421 00:32:16,400 --> 00:32:21,720 Speaker 1: for large platform companies. So it's not as if there's 422 00:32:21,760 --> 00:32:26,920 Speaker 1: an optimal strategy out there we will get somewhere that 423 00:32:27,160 --> 00:32:30,680 Speaker 1: might chick two or three decades. We're trying this, we're 424 00:32:30,720 --> 00:32:36,000 Speaker 1: trying that, we're thinking about this, learning that. So complexity 425 00:32:36,080 --> 00:32:41,640 Speaker 1: economics applies to these situations where this are forming where 426 00:32:41,640 --> 00:32:44,520 Speaker 1: you've never seen them. So it's not that this is 427 00:32:44,600 --> 00:32:49,160 Speaker 1: an add on to do more detail to standard economics. 428 00:32:49,520 --> 00:32:54,480 Speaker 1: Complexity economics isn't an add on two standard economics. It's 429 00:32:54,760 --> 00:32:58,800 Speaker 1: um the type of economics that would be appropriate if 430 00:32:58,920 --> 00:33:02,000 Speaker 1: new things are coming along but you don't know what 431 00:33:02,200 --> 00:33:05,880 Speaker 1: they are, if people are strategizing in different ways but 432 00:33:06,040 --> 00:33:09,600 Speaker 1: you simply don't know what that's going to bring. It's 433 00:33:09,600 --> 00:33:13,400 Speaker 1: a bit like you could say standard economists would be 434 00:33:13,480 --> 00:33:19,320 Speaker 1: a bit like fighting standard battles, saying six hundreds, you 435 00:33:19,480 --> 00:33:23,000 Speaker 1: set up your army, the Duke of Marlborough sets up 436 00:33:23,280 --> 00:33:26,640 Speaker 1: is you pretty much to know how the rules are, 437 00:33:27,360 --> 00:33:31,360 Speaker 1: you let things interact and so on. Now you're in 438 00:33:31,400 --> 00:33:34,760 Speaker 1: a situation where you don't know who you're up against. 439 00:33:35,680 --> 00:33:38,000 Speaker 1: You don't quite know what the rules are, you don't 440 00:33:38,040 --> 00:33:41,320 Speaker 1: know how things will work out, you don't know what 441 00:33:41,920 --> 00:33:45,000 Speaker 1: new weapons will be used, and so you're backing and 442 00:33:45,160 --> 00:33:48,200 Speaker 1: filling all the time. How do you make an economic 443 00:33:48,320 --> 00:33:52,760 Speaker 1: side of that? So, just on that note, let me 444 00:33:52,880 --> 00:33:57,560 Speaker 1: ask you um a question, and I'm really hoping that 445 00:33:58,000 --> 00:34:00,640 Speaker 1: you're able to talk about this, But on this idea 446 00:34:00,720 --> 00:34:06,120 Speaker 1: that complexity economics is better equipped to deal with new things, 447 00:34:06,960 --> 00:34:12,640 Speaker 1: new situations, new behaviors that might evolve, what does complexity 448 00:34:12,680 --> 00:34:17,680 Speaker 1: economics say about our current situation? So we just had 449 00:34:17,719 --> 00:34:22,640 Speaker 1: a global pandemic um very different to previous global pandemics, 450 00:34:22,760 --> 00:34:26,879 Speaker 1: We had a policy response um from governments, at least 451 00:34:26,920 --> 00:34:30,320 Speaker 1: in the US that was very very different to previous 452 00:34:30,440 --> 00:34:34,320 Speaker 1: policy responses, and we basically had an economic crisis that 453 00:34:34,480 --> 00:34:38,359 Speaker 1: was quite unusual compared to other ones. So what does 454 00:34:38,440 --> 00:34:43,640 Speaker 1: complexity economics tell us about the current situation? To my mind, 455 00:34:43,719 --> 00:34:48,160 Speaker 1: complexity economics is not set up to tell us what 456 00:34:48,320 --> 00:34:51,960 Speaker 1: we should do in any new situation like that, or 457 00:34:52,040 --> 00:34:55,400 Speaker 1: any new crisis. It's set up as a way to 458 00:34:55,719 --> 00:35:03,560 Speaker 1: understand how players mute actually adjust to a situation they're 459 00:35:03,840 --> 00:35:09,080 Speaker 1: co creating that they don't really understand. So it's a 460 00:35:09,160 --> 00:35:12,960 Speaker 1: way to look at the economy, it's way to understand something, 461 00:35:13,480 --> 00:35:17,200 Speaker 1: and I think that the COVID crisis is pretty good 462 00:35:17,400 --> 00:35:23,799 Speaker 1: illustration of this. At work in March, there was an 463 00:35:23,840 --> 00:35:27,600 Speaker 1: awful lot of fear about COVID, and I think rightly so, 464 00:35:28,280 --> 00:35:30,400 Speaker 1: because we didn't know how it worked, We didn't know 465 00:35:30,480 --> 00:35:33,640 Speaker 1: how the infections would play out, we didn't know how 466 00:35:33,800 --> 00:35:37,320 Speaker 1: bad it would be, so we were kind of groping 467 00:35:37,400 --> 00:35:42,480 Speaker 1: in the dark. And complexity economics tries to look at 468 00:35:42,640 --> 00:35:47,000 Speaker 1: situations like that. It may not say there's an optimal strategy, 469 00:35:47,520 --> 00:35:51,200 Speaker 1: but we did come up with coping strategies. We tested, 470 00:35:51,360 --> 00:35:54,960 Speaker 1: we tried. We we thought that fist masks weren't a 471 00:35:55,040 --> 00:35:59,399 Speaker 1: good idea because it would deprive hospitals, and we thought 472 00:35:59,480 --> 00:36:02,520 Speaker 1: fast my masks were a great idea, and so on. 473 00:36:03,040 --> 00:36:06,800 Speaker 1: We coped. We were in the middle of a situation 474 00:36:07,800 --> 00:36:14,160 Speaker 1: that people were creating as well as being part of people. 475 00:36:14,400 --> 00:36:20,560 Speaker 1: Human beings co created the pandemic by interacting and infecting 476 00:36:20,680 --> 00:36:25,360 Speaker 1: each other, not deliberately, of course, and we were trying, 477 00:36:25,840 --> 00:36:30,600 Speaker 1: everybody was trying mutually to cope with this. And then finally, 478 00:36:31,239 --> 00:36:34,440 Speaker 1: by December or so last year, we thought we could 479 00:36:34,480 --> 00:36:38,440 Speaker 1: figured things out. But then along came vaccination, and that 480 00:36:38,640 --> 00:36:42,120 Speaker 1: brought a whole new set of things to understand. What 481 00:36:42,280 --> 00:36:45,040 Speaker 1: if there weren't enough vaccines, what we hadn't made the 482 00:36:45,160 --> 00:36:50,280 Speaker 1: right arrangements, like the EU hadn't really in the early days. 483 00:36:50,760 --> 00:36:55,400 Speaker 1: What of the vaccinations didn't work? What if you can 484 00:36:55,560 --> 00:36:59,800 Speaker 1: get hold of vaccines, say like in South Africa, some 485 00:37:00,000 --> 00:37:03,280 Speaker 1: are like that. And so we were learning and everybody 486 00:37:03,440 --> 00:37:08,359 Speaker 1: was mutually adjusting and readjusting. The economy isn't one thing 487 00:37:08,520 --> 00:37:13,000 Speaker 1: or the other. It's not a well oil perfect equilibrium 488 00:37:13,800 --> 00:37:17,480 Speaker 1: machine where everything's tacking. And to the degree it's that 489 00:37:17,719 --> 00:37:23,759 Speaker 1: standard economic supplies, the economy isn't always exploring new territory 490 00:37:24,520 --> 00:37:28,000 Speaker 1: at the other extreme, and we're learning and trying to 491 00:37:28,160 --> 00:37:32,160 Speaker 1: adapt and co adapt somewhere in between. So I would 492 00:37:32,200 --> 00:37:36,919 Speaker 1: say that when you're replicating strategy, or when you're replicating 493 00:37:37,560 --> 00:37:40,279 Speaker 1: the same situation from day to day, you could talk 494 00:37:40,320 --> 00:37:47,080 Speaker 1: about optimizing well defined problems, optimal behavior standard economics. When 495 00:37:47,080 --> 00:37:51,480 Speaker 1: the situations being created by the strategies you are not 496 00:37:52,239 --> 00:37:57,320 Speaker 1: should we test for COVID, should we separate people? Should 497 00:37:57,360 --> 00:38:02,120 Speaker 1: we have social distancing? Then you're in a different situation. 498 00:38:02,960 --> 00:38:11,360 Speaker 1: So complexity economics is really about people co adapting, co learning, 499 00:38:12,280 --> 00:38:16,640 Speaker 1: and figuring out what works. I'd like to make two 500 00:38:16,760 --> 00:38:20,320 Speaker 1: points about industry, because I think your show has a 501 00:38:20,360 --> 00:38:24,879 Speaker 1: lot to do with the industry, if I may, Yeah, 502 00:38:25,000 --> 00:38:27,680 Speaker 1: we've certainly been talking about it a lot this year 503 00:38:27,760 --> 00:38:31,120 Speaker 1: again because I think one of the unexpected things from 504 00:38:31,239 --> 00:38:34,000 Speaker 1: COVID was the degree to which it would cause these 505 00:38:34,080 --> 00:38:38,320 Speaker 1: supply disruptions, not necessarily because borders were shut, although that 506 00:38:38,480 --> 00:38:40,320 Speaker 1: was part of it, but just because we had this 507 00:38:40,520 --> 00:38:44,759 Speaker 1: really unexpected um import boom over in the States with 508 00:38:44,880 --> 00:38:48,319 Speaker 1: everyone staying at home and ordering a bunch of goods. 509 00:38:49,000 --> 00:38:54,640 Speaker 1: So please, complexity economics is web looking at the economy. 510 00:38:55,480 --> 00:38:58,680 Speaker 1: How do you look at industry this way? What I 511 00:38:58,960 --> 00:39:05,640 Speaker 1: say is if industries fairly constant, so we're turning out steal, 512 00:39:05,840 --> 00:39:10,640 Speaker 1: the years nine and one year, one month is roughly 513 00:39:11,040 --> 00:39:14,960 Speaker 1: not too different from the previous one. Standard economics would apply. 514 00:39:15,800 --> 00:39:20,239 Speaker 1: You optimize inputs and outputs, and you optimize the technology, 515 00:39:20,360 --> 00:39:25,759 Speaker 1: and prices come into equilibrium. But we're not as much 516 00:39:25,840 --> 00:39:31,320 Speaker 1: in that situation. So the years now say one and 517 00:39:32,080 --> 00:39:35,680 Speaker 1: many industries, and I think this is very much illustrated 518 00:39:35,880 --> 00:39:44,120 Speaker 1: in your show here, Industries may try to optimize. Say, okay, 519 00:39:44,239 --> 00:39:48,520 Speaker 1: I have this fleet of oil tankers, and I can 520 00:39:49,000 --> 00:39:54,360 Speaker 1: sit in some office saying wherever Hong Kong or London 521 00:39:54,480 --> 00:40:00,200 Speaker 1: or summer New York and optimize their schedule, thing s 522 00:40:00,480 --> 00:40:06,640 Speaker 1: or inter if they're complicated and there's new unexpected uncertain 523 00:40:07,800 --> 00:40:13,120 Speaker 1: events coming along the time Sue Canal closes, you're held 524 00:40:13,239 --> 00:40:18,239 Speaker 1: up at some port unexpectedly, and so on. So optimization 525 00:40:19,239 --> 00:40:23,520 Speaker 1: may not be quite appropriate. The strategies that are more 526 00:40:23,600 --> 00:40:28,239 Speaker 1: appropriate are not just pure optimization. They have to take 527 00:40:28,320 --> 00:40:31,520 Speaker 1: a kind of resilience. How do I recover? How do 528 00:40:31,640 --> 00:40:35,919 Speaker 1: I cope with the unexpected? So I'm running the entire 529 00:40:36,160 --> 00:40:41,480 Speaker 1: US or California electricity grid, my strategy should not be 530 00:40:41,560 --> 00:40:45,279 Speaker 1: how to optimize that. There maybe forest fires, there may 531 00:40:45,360 --> 00:40:49,279 Speaker 1: be disruptors, there may be more energy available, and I 532 00:40:49,440 --> 00:40:56,480 Speaker 1: expect my strategy and industry isn't just optimizing. It is 533 00:40:57,080 --> 00:41:00,920 Speaker 1: how do I cope with these unexpected, un thought of things. 534 00:41:01,560 --> 00:41:05,160 Speaker 1: What if there's players who come up with something nobody's 535 00:41:05,200 --> 00:41:10,320 Speaker 1: ever thought of. You can't optimize, You're in an unknown 536 00:41:10,560 --> 00:41:15,280 Speaker 1: situation or it's partially unknown. What you can do. Those 537 00:41:15,520 --> 00:41:20,480 Speaker 1: put things like this make computer models. It's a bit 538 00:41:20,600 --> 00:41:25,160 Speaker 1: like the COVID strategy a year ago. What if what 539 00:41:25,320 --> 00:41:27,760 Speaker 1: if this gets out of control? What if that happens? 540 00:41:28,239 --> 00:41:30,640 Speaker 1: What if we don't get a vaccine for three years? 541 00:41:30,800 --> 00:41:33,719 Speaker 1: And go on? And you can test all those strategies. 542 00:41:34,239 --> 00:41:39,839 Speaker 1: The industry itself is moving from just simply being repetitive, 543 00:41:40,040 --> 00:41:44,600 Speaker 1: which you can optimize in, to being resilient where you're 544 00:41:44,680 --> 00:41:50,680 Speaker 1: looking for reactions. So complexity economics is basically saying, how 545 00:41:50,760 --> 00:41:56,880 Speaker 1: do firms react to unexpected things in a reasonably smart 546 00:41:57,000 --> 00:42:01,040 Speaker 1: way and what would industry look like? Flat were the norm, 547 00:42:02,000 --> 00:42:07,360 Speaker 1: And we're rapidly shifting from what I call a standard 548 00:42:07,800 --> 00:42:13,640 Speaker 1: productive economy. There's inputs, there's factories their outputs, there are 549 00:42:13,920 --> 00:42:17,920 Speaker 1: human beings in the middle, there's planners, there's workers, and 550 00:42:18,000 --> 00:42:22,480 Speaker 1: we're shifting more and more into what I call in 551 00:42:22,560 --> 00:42:30,640 Speaker 1: an autonomous economy. The trading system used in finances largely autonomous. 552 00:42:31,560 --> 00:42:37,960 Speaker 1: Supply chains are partially autonomous. As you would know, autonomous 553 00:42:38,080 --> 00:42:42,120 Speaker 1: means that they're self regulating. There there isn't a human 554 00:42:42,360 --> 00:42:48,040 Speaker 1: being planning each step. We're moving towards getting air traffic 555 00:42:48,160 --> 00:42:53,680 Speaker 1: control systems that will be autonomous. That's already being pointed 556 00:42:53,760 --> 00:43:01,520 Speaker 1: out usage of things like blockchain bitcoin, that type of 557 00:43:01,760 --> 00:43:09,759 Speaker 1: trading and finance is partially autonomous. And in cases like that, 558 00:43:10,160 --> 00:43:15,200 Speaker 1: I wouldn't say elements are acting on their own. We're 559 00:43:15,280 --> 00:43:20,640 Speaker 1: moving into an economy or are an industrial setup if 560 00:43:20,719 --> 00:43:24,200 Speaker 1: things are autonomous, where the elements and that's set up 561 00:43:24,560 --> 00:43:30,640 Speaker 1: are in conversation with other elements. So if you're using blockchain, 562 00:43:31,800 --> 00:43:35,520 Speaker 1: or if you're using some of these new financial arrangements 563 00:43:35,600 --> 00:43:42,040 Speaker 1: that you've talked about in your show, different players, different contracts, 564 00:43:42,840 --> 00:43:49,640 Speaker 1: maybe automatically or autonomously in conversation with other elements. That 565 00:43:50,360 --> 00:43:57,160 Speaker 1: is exactly the whole domain of complexity. Again, go back 566 00:43:57,200 --> 00:44:01,440 Speaker 1: to cars and traffic. Maybe I have a driverless traffic 567 00:44:01,600 --> 00:44:06,640 Speaker 1: system in ten years time. I'm in say Los Angeles, 568 00:44:07,640 --> 00:44:10,520 Speaker 1: but I'm not driving in a car sitting in a 569 00:44:10,600 --> 00:44:13,920 Speaker 1: car that's driverless. I'm sitting in a car it's in 570 00:44:14,040 --> 00:44:20,200 Speaker 1: a driverless convoy convoy driverless cars, and each car is 571 00:44:20,320 --> 00:44:26,759 Speaker 1: in conversation of cars around it. So what complexity economics 572 00:44:26,880 --> 00:44:29,840 Speaker 1: is trying to do is to say, what if you 573 00:44:29,960 --> 00:44:34,560 Speaker 1: have a sort of autonomous semi autonomous system where the 574 00:44:34,719 --> 00:44:40,719 Speaker 1: elements and that economy, they might be trading strategies trading systems. 575 00:44:41,400 --> 00:44:45,440 Speaker 1: They may be cars and driverless convoys, they may be 576 00:44:46,560 --> 00:44:53,520 Speaker 1: different electricity generating systems. What these are in conversations to 577 00:44:53,719 --> 00:44:58,399 Speaker 1: other systems? How can we understand something like that? How 578 00:44:58,480 --> 00:45:04,399 Speaker 1: can we control it? What policies would be necessary. That's 579 00:45:04,560 --> 00:45:10,680 Speaker 1: very different from saying we have a century economy. So again, 580 00:45:10,880 --> 00:45:15,240 Speaker 1: I would say we're moving fairly fast into a system 581 00:45:15,400 --> 00:45:21,000 Speaker 1: where the players, the elements, the the cars, the planes 582 00:45:21,080 --> 00:45:25,919 Speaker 1: that are landing in an autonomous air traffic control situation 583 00:45:26,320 --> 00:45:32,200 Speaker 1: are automatically in conversation with other like elements. How does 584 00:45:32,239 --> 00:45:34,560 Speaker 1: that work? How can we think of it? How should 585 00:45:34,600 --> 00:45:38,080 Speaker 1: we control it? We can't optimize it because you never 586 00:45:38,239 --> 00:45:40,600 Speaker 1: quite know what's going to happen next, but you can 587 00:45:40,680 --> 00:45:45,400 Speaker 1: certainly make a Brazilian I do like that. Complexity economics 588 00:45:45,520 --> 00:45:49,600 Speaker 1: is making an attempt to understand the real world economy 589 00:45:49,800 --> 00:45:54,040 Speaker 1: as it kind of matures and becomes technologically more advanced. 590 00:45:54,080 --> 00:45:57,719 Speaker 1: And it's true if you look over classical economics um 591 00:45:57,880 --> 00:46:00,640 Speaker 1: the literature there, so much of it just seems so 592 00:46:01,560 --> 00:46:05,080 Speaker 1: old fashioned nowadays, not in the sense necessarily of what 593 00:46:05,239 --> 00:46:07,840 Speaker 1: it's actually saying about economics, but just the idea of, 594 00:46:08,040 --> 00:46:10,959 Speaker 1: you know, this country makes wine and this country makes 595 00:46:11,000 --> 00:46:13,319 Speaker 1: cloth and they're going to trade with each other. Um, 596 00:46:13,560 --> 00:46:17,560 Speaker 1: you know, never mind changes in transport technology or manufacturing 597 00:46:17,600 --> 00:46:22,200 Speaker 1: technology and things like that. Anyway, um, Professor Brian Arthur, 598 00:46:22,239 --> 00:46:24,360 Speaker 1: we're going to have to leave it there. But it 599 00:46:24,520 --> 00:46:28,200 Speaker 1: has been an absolutely fascinating conversation and I really appreciate 600 00:46:28,280 --> 00:46:32,080 Speaker 1: you taking the time to walk us through complexity economics. 601 00:46:32,680 --> 00:46:37,200 Speaker 1: Thank you so much, and I'm delighted to have been 602 00:46:37,280 --> 00:46:55,319 Speaker 1: with you. Thank you, Bunty, Thank you so much. Well, 603 00:46:55,920 --> 00:46:59,239 Speaker 1: Joe isn't here to bounce off of, so UM, I'll 604 00:46:59,280 --> 00:47:01,440 Speaker 1: just go ahead and give you, I guess my two 605 00:47:01,560 --> 00:47:05,600 Speaker 1: big takeaways from this. Number one, I think it's fairly 606 00:47:05,680 --> 00:47:09,440 Speaker 1: clear that there are shortcomings in traditional economics, and you 607 00:47:09,480 --> 00:47:11,440 Speaker 1: can argue that they're there for a reason that you 608 00:47:11,560 --> 00:47:14,239 Speaker 1: have to have simplifying models. You have to have simplifying 609 00:47:14,400 --> 00:47:18,759 Speaker 1: assumptions about how the world works in order to you know, 610 00:47:19,400 --> 00:47:23,640 Speaker 1: make policy recommendations and make them relatively quickly. But I 611 00:47:23,800 --> 00:47:28,279 Speaker 1: also think that given the advancement in technology, given the 612 00:47:28,360 --> 00:47:32,280 Speaker 1: advancement in computer systems um that the professor was describing, 613 00:47:32,719 --> 00:47:36,279 Speaker 1: it does seem natural that we now have the capacity 614 00:47:36,520 --> 00:47:41,320 Speaker 1: to model the real world on an altogether different and 615 00:47:41,560 --> 00:47:44,640 Speaker 1: much more advanced scale. And I think it's interesting to 616 00:47:44,800 --> 00:47:48,600 Speaker 1: see a school of economics that's really trying to take 617 00:47:48,680 --> 00:47:52,320 Speaker 1: that technology and use it um to contribute to the 618 00:47:52,400 --> 00:47:56,120 Speaker 1: literature in an interesting way. And then the second thing 619 00:47:56,200 --> 00:47:58,600 Speaker 1: that sort of stood out for me is related to 620 00:47:58,680 --> 00:48:01,680 Speaker 1: that point, but just the notion that in THEES, when 621 00:48:01,719 --> 00:48:05,480 Speaker 1: we were talking about free trade and outsourcing this idea, 622 00:48:05,560 --> 00:48:09,600 Speaker 1: that we didn't have the computational power to actually model 623 00:48:09,680 --> 00:48:14,239 Speaker 1: what that would mean for every single segment of society 624 00:48:14,600 --> 00:48:18,160 Speaker 1: um in every different country. We could only look at 625 00:48:18,200 --> 00:48:21,799 Speaker 1: it sort of from a broad bird's eye level view, going, well, 626 00:48:22,000 --> 00:48:25,680 Speaker 1: obviously we want cheaper goods, and obviously they want cheaper goods, 627 00:48:26,120 --> 00:48:27,759 Speaker 1: and so we're just gonna trade with each other and 628 00:48:27,800 --> 00:48:31,480 Speaker 1: everyone's going to be happy. And twenty or thirty years later, 629 00:48:31,600 --> 00:48:36,120 Speaker 1: it's very very clear that that wasn't necessarily what happened. 630 00:48:36,400 --> 00:48:40,120 Speaker 1: The outcome was rather different, And it's extremely interesting to think, 631 00:48:40,480 --> 00:48:43,480 Speaker 1: what if you had had something like complexity economics, What 632 00:48:43,640 --> 00:48:48,279 Speaker 1: if you had had more advanced, more sophisticated models, what 633 00:48:48,440 --> 00:48:52,840 Speaker 1: sort of policy decisions would you have made? So I 634 00:48:52,880 --> 00:48:56,239 Speaker 1: guess I'll leave it there. This has been another episode 635 00:48:56,400 --> 00:48:59,319 Speaker 1: of the All Thoughts podcast. I'm Tracy Alloway. You can 636 00:48:59,400 --> 00:49:02,840 Speaker 1: follow me on Twitter at Tracy Halloway. You can follow 637 00:49:03,200 --> 00:49:07,560 Speaker 1: Joe at The Stalwart. You can follow our producer Laura 638 00:49:07,680 --> 00:49:12,080 Speaker 1: Carlson at Laura M. Carlson. And you can follow the 639 00:49:12,239 --> 00:49:17,279 Speaker 1: head of Bloomberg podcast, Francesco Levi at Francesco Today. Thanks 640 00:49:17,320 --> 00:49:17,800 Speaker 1: for listening.