Exploring Complexity Ep 3

Exploring Compexity is where we explore the complexity of minds meeting machines by combining complexity science, artificial intelligence, and the human sciences.

Transcript

Dave Edwards 0:07
Welcome back to Exploring Complexity from Artificiality, where we aim to explore the complexity of minds meeting machines, bringing together complexity science, artificial intelligence, and the human sciences. In this episode, we're going to talk a bit about applying complexity science. So I want to start off by asking you, what is complexity science?

Helen Edwards 0:30
Well, complexity science is, first of all, completely, like totally interdisciplinary transdisciplinary. And as important to understand, because when you're thinking about defining fields of science, you think about like buildings in a university or something like that. But this is different. It's about going across all of these disciplines, and looking for things that are common ways that click that complex systems behave. And those principles can then be discovered and applied into other systems. So it's very much a sort of principles first view, but it's coming from the perspective of not totally questioning reductionism and science. But more saying, there's more in the connections and the interactions between components of a system, then there then we account for in disciplinary based reductionist science. So the core of it is being able to discover and mathematize these first principles and then apply math and programs from one area of study into another. Interesting. So it's almost like so have you discovered a principle of complexity science, it's the best way to interrogate another system. So

Dave Edwards 2:05
we can make some examples of what in complexity science has fascinated you so much?

Helen Edwards 2:13
Well, there's the the biological, ecological aspects that are sort of more intuitively around, you know, just the way the world works. It's just, it's so complex to see, say, the flock of starlings, literally. I mean, that's, there's sort of these canonical complex systems, the beehives, the termite mounds, the, you know, those kinds of complex systems. But I think what really captivates me is, there's great explanatory power, when you when you break out of thinking in a particular language of a particular discipline, and apply these principles from complexity across multiple different places. I'm fascinated and continue to be fascinated by how just a couple of simple rules can create these immensely complex behaviors inside of a complex system. Think that's sort of the number one thing for me that is so fascinating, that something that at a really, micro scale can be incredibly simple, you know, if, and there's multiple examples of these when, and that you can look at the game of life. And it's a very simple set of rules. Or, again, the flock of the flock of birds, that only each bird is only paying attention to. A couple of birds close by, and you get these huge floggings and murmurings that these complex behaviors that that that happen as a result of just simple rules between a couple of individuals. So

Dave Edwards 4:02
there's a quote that we saw recently, and you said, it was awesome. So I'm gonna read it and ask you why you think it's such a good definition? You think because you understand one, you must also understand two, because one and one, make two. But you must also understand, and now tell me why you find that to be such a compelling quote, in trying to help people understand complexity.

Helen Edwards 4:30
Three reasons. One, it's ancient, what 12th century 13th century two, it's incredibly simple. So it is sort of a metaphor for that baseline. I was saying before, and three. When I when I say one on one makes two, it sounds so obvious and easy. And because you only think about one you don't think about and and the minute you flip it around and say I need to start thinking about and then you become much more sensitive to the fact that what really matters in an organization or any of these systems, stuff that individuals are not the ones, it's actually the end. And I can't think of a more parsimonious definition of what sort of, philosophically complexity encompasses. That's

Dave Edwards 5:19
a good breaking point later, we'll take a moment and look at some visualizations of complexity. Let's take a look at two examples of complexity that have been created by the Santa Fe Institute. These are fascinating because you can actually get a visualization and sort of play around with them and change some of the features to be able to see how these different complex systems react. These are remarkable because they have been able to mathematize certain things. And that gives us a much closer appreciation. But we use them to illustrate how the complexity works. And then also use these two examples as potential metaphors for things. So let's look at this first one, this is coffee porosity. And as you can see, as you press play, the blue water flows through the coffee. And as you change the porosity, you can see how the water flow changes, increase the porosity, and the water flows through faster and more thoroughly, and decrease the porosity and it flows through more slowly and doesn't reach all of the coffee grounds. If you make coffee, you've had this experience where if you pack down and tamp it down too much, you won't actually get to be able to pull a good shot from your espresso machine. And because the water doesn't flow very well. So why is this helpful? I mean, first of all, it's kind of fascinating to see how the coffee works. But more importantly, it's interesting to use as a metaphor. So when we work with companies and organizations to think about solving complex problems, we look at what their problem is, we try to find something else that might act as a metaphor for it. And we'll talk more about metaphor it throughout this series about why metaphor so useful and so helpful in using in solving complex problems and looking at complexity. In this case, you can think about water flowing through something but then you could say, well, that what else might that serve as a metaphor for like How about, say, information flowing through an organization, sometimes it flows through too quickly. Sometimes it doesn't flow quickly enough, or it doesn't flow through thoroughly enough. And so you could solve that information flow problem by thinking about coffee, and maybe there's some insight there that might actually help you out. Let's look at the second one, this is traffic flow, or in this case, it will be traffic congestion. Shortly, you'll notice that there's a squiggly road, the squiggles don't mean anything in terms of speed, or they're not actual curves, it's just a visual illustration to be more road surface in the same small square. When I press play, you'll notice that all of the cars are trying to optimize towards 120 kilometers per hour. This is designed obviously, for someone with a drive or in kilometers, where their speed is measured in kilometers. As you change the speed variability though, and cars are trying to optimize towards different speeds, what happens, things start to slow down and you start having some traffic congestion, you actually have full on traffic jams. When you change the inertia and acceleration that just enhances the challenge of the problem, and you get a completely gummed up. And this is fascinating to watch and play with. This is actually a lot of fun to move these things around. When you bring everything back and reset it back to the beginning, it takes a while for those traffic jams to to, to release, I guess would be the word. But the This is fascinating to play with. It's also great if you have small children who have ever sat in the back of the car and said, Hey, why aren't why doesn't everybody just keep driving, you might have some level explanation. You could also use this as a metaphor, maybe something physical moving around, like people moving in a store. Or maybe you think of something that's not physical, but like say, people trying to make a decision. If some people are trying to make a decision in a day or other people trying to make a decision in two weeks, that will be a lot slower and create a lot of traffic jams as opposed to everybody being designing, deciding to make that decision on the same timeline. So these are really useful and helpful. They're cool things to explore, check out a lot more in the Santa Fe Institute website. Thanks to them for posting these. And we'd love to grabbing these these little examples and sharing them with you. Thanks a lot. So last topic for this video is I'd like you to talk a little bit about the capabilities that people need to develop and build in order to be able to use complexity science to be able to leverage this power. So why don't you walk through that sort of seven skills that we talked about? Yeah,

Helen Edwards 9:36
we think about this as being very much skills and capability driven. The vast majority of people who want to use ideas from complexity, and some of the discoveries and complexity science and want to use those sort of in their everyday life, never going to be able to get across the complex. math that's required because it really is a very mathematically driven way of looking at the world. But that's not to say that there aren't incredibly useful ways to apply complexity just in everyday life. The one that we focus on first and foremost is mind, right? At learning enough about the fundamentals of complexity to have a different kind of mindset. When you understand the key features and principles of complexity, science, complexity more broadly, as its applied different examples, what it allows you to do is approach a new opportunity or any kind of system at all compared to just how you manage your family. It allows you to apply those features in such a way that you just look at the system differently. And because you look at the system differently, you learn things differently, you're sensitive to different things. Our minds don't take in everything that's very context driven, our attention is driven to things that are meaningful to us. When you when complexity is meaningful, you will look for different things, and you'll learn things. So I think that that's sort of the first thing. And once you go through, I've seen it, and multiple people who have gone through the same process of developing a complexity mindset, really describe it as once you've got it, you can never go back, and you just can't. The second is, we think about managing different ways have different methods and techniques for actually managing systems. That that command and control process recognizing that you can try, you can tell yourself that that's what you're doing. But been since been aware that that's not never going to happen. It allows you to have a different view of just basic sort of standards of management. In some ways there are, there are already quite good, sort of new KPI tools that have a little bit more stochasticity in them, they then move around a little bit more, they're a little bit more flexible, they account for the fact that you can't sort of know everything. So there's sort of manat those, those sort of core management tools are super important. There's also things like getting to deeper levels of meaning be more able to focus on the things that matter, there's a lot of noise. And recognizing that noise can be quite valuable. Because it can give us it can actually help make a system more robust. But you get a greater sense of when do I really care about this and where to where I don't, which relates a lot to sort of the the idea of sense making. And we're so this is a sort of a real hack, because we're so driven to since make to make sense of things. And we look generally for stories and cause and effect. That's how stories are the things that help us make sense of the world. But with complexity, you've now got another thing, you don't just have to rely on simple models of cause and effect or stories that tell you about that, you can have a richer experience of what it means to sort of make sense of something. We look at different ways with measurement. That's a different kind of capability. Measuring complex systems is is not without its challenges. But by looking at complexity, science and how people think about different ways of measuring what they're sensitive to, you become a lot more sensitive in some ways to time, you're more sensitive to the dynamics, and the the timing of things actually becomes what matters rather than the things themselves. So I think you'll get a different set of skills and capabilities around measurement. And the probably the biggest one that is the most useful for people when we teach this and when we run workshops, is this whole concept of metaphor. Analogical Reasoning and metaphorical reasoning is so foundational to the way that we think when we have a metaphor or a structure in our mind that and we apply it somewhere else. It brings so much richness across the price of using metaphor as constant vigilance because you're going to get out of your skis over At some point, and if the metaphor crumbles underneath you, and you don't recognize it,

Dave Edwards 15:06
kind of tidal force getting out over your skis.

Helen Edwards 15:08
Yes, well, that was yes, yeah. Maybe I got out of my skis on that metaphor, but metaphorical thinking, as part of attacking a problem or an opportunity. It's just, it's so empowering. And across my career, I found a couple of techniques that are incredibly powerful in a very human way, for unlocking different levels of insight and, and different levels of sort of visceral commitment to action. And one of them is this idea of applying metaphor like this. models. Models are sort of the next up from metaphor, because if you could push things to math, right, you know, it helps you simulate. And simulation is so important because we just our imaginations are amazing. But there's something about putting something into a simulation that brings it down to a level of concreteness again. And so getting to the level that you can model, something that's meaningful enough and with with some of the AI technologies are allowing us to do this, right. So you can, people can build models of knowledge networks in graphs across organizations, and they can learn totally different things about the sorts of information that people share. But in some ways, the models of just using generative AI are like this, too, we simulate. Now, I use generative AI to simulate different stories for me to simulate a persona or to simulate different ways of attacking a problem. That kind of simulation is a much more complex way of us understanding the world if you like. And the last one was math, and it is sort of the ultimate goal. The way that I think about math as a capability is, I think it's really important to have minimum viable math, slash physics slash anything but Minimum Viable ability to understand to navigate the world of math, whether it's basic statistics, whether it's the core and the core algorithms that underpin AI, just having some, some good sound reasoning skills mathematically, being able to understand how to do a statistical test those kinds of things, so that we're less pushed and pulled by lies, damned lies and statistics. So that's sort of how we think about those seven core pillars of of capability building. Awesome.

Dave Edwards 17:59
Well, thanks for joining us again, and stick around when next we're gonna move into describing each of the 14 features of complexities, the 14 that we focus on. So hope you'll stick around and join us thanks a lot.

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