Exploring Complexity Ep. 1

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 to Exploring Complexity, a new video series from Artificiality that aims to explore the complexity of minds meeting machines. In this series, we'll combine complexity science, artificial intelligence and the human sciences.

Helen Edwards 0:22
So Dave, what is complexity?

Dave Edwards 0:24
Let's start off with that. That should be the first question, shouldn't it? Well, I like to think about it in terms of what complexity is versus other things that people might think of as complexity come, the word complex, and complexity gets used colloquially, in a sort of imprecise way. For anything, that's hard. That's hard. And so what we do, and I like this is we separate it out in terms of three different kinds of problems or opportunities. And I think that helps put the frame around what complex is. So the first of those three is simple, right? So you have a simple problem, a simple opportunity. It's something that is known. You know, there's something that where the, the goal of what you're trying to accomplish is clear. It's, it's can be difficult, like a simple problem, or simple opportunity doesn't mean easy. It does usually mean though, a single step in that process. And we think about it as something that someone who's tackling that kind of simple problem or opportunity is a decider, because you have to make a decision. That's what you're that's the outcome that you're aiming for. So that's simple. Second, is complicated. And this is the one that usually sort of, sometimes people use complicated and complex interchangeably, but we separate them out for an important reason. Because complicated, the problem or the opportunity is knowable, and the goal can be defined, you know exactly what that is. It's, it can be complicated, because it has lots of dependencies, lots of things depend on something else, there's an interconnected parts, the process of solving that problem or tackling that opportunity can be a multi step process. And when you're in that mode, the persona we talked about is as opposed to being a designer and a simple process is to be a designer. And because you're designing some sort of solution to this complicated problem, you know, a complicated problem could be designing a new piece of software, or it could be designing something like the iPhone, it's it's it's complicated. There's lots of dependencies. But the what you're doing is you're you can those problems and opportunities yield to those classic tools of design and engineering, you can look out across the end state outcome, then you can predict what what's going to happen, you know, when you put this thing out in the wild, and how it's going to work. Complex, though, is different. Complex, the problem is open it there is no boundary to the end of it. The the goal in when tackling one of these problems or opportunities to start with is just making it tractable, you know, the opposite of intractable, can I get my hands around this enough? Can I can I can I define enough of this thing that I can actually, I can actually start working with it. The challenge is multifaceted. But the biggest one that we always talk about is emergence, right that things emerge from the edges of the system. And so it isn't something that you can design or engineer a solution to it, the process has to be dynamic. And the the, the the outcome isn't a decision or design, but a path. Because this system continues to change, even when you get into it, and you're making changes, it changes with you. And the so the persona we talked about is instead of being a designer, or or a decider, we thought we talked about being Wayfinders. Finding your way in this system that changes that adapts. That's a dynamic things that emerge. And we like that word Wayfinding, because it's actually a word that was frequently used to describe people who are sailing across the Pacific. And if you think about that, you're trying to navigate with the stars and storms come up and all kinds of things that happen that you have to be continually adjusting to and trying to stay on the path and continuing to aim towards whatever islands you're trying to get to. And that isn't something that you can just, you know, make a single decision on that I'm gonna go, you know, cross the ocean, and I'm done. Or it's not something that you can design of head because you can't design for all of the different things that might happen. Well,

Helen Edwards 4:46
you might not even have been aiming for a particular eye you might

Dave Edwards 4:50
not have been you might just be exploring true. So wayfinding to us is this sort of, you know, anchoring concept. And that's why, for years now we've referred Do ourselves as Wayfinders. And I think we have found our way into this intersection of complexity and AI. So that's how I would describe it now and tell me if you want to add anything to that, if you want to adjust any of my definitions or descriptions, or and also talk a little bit about, you know, where we see complexity, like, I think it'd be helpful to give our viewers some sort of some ideas of where to see complexity around them. Sure.

Helen Edwards 5:32
Well, there's a couple of things that I think that come out of what you described. First of all, one of the sort of Hallmark difference between the way you laid out complicated and complex is this very definition of, of emergence. And emergence is very much a hallmark of complex systems, that what it means is that you could understand in detail every single component part in the system, in a highly reductionist way, but then you put them together. And something unpredictable happens, some behavior emerges, that could never have been predicted by understanding each of these individual systems. And there's part of that that also means in say, a business context is flipping back to the descriptions you you ran through is that complex systems, you're part of the system, you're not aside from it. So that idea, you know, that concept of say, designing the iPhone, very complicated, but you're outside of the system, when a change is made, when you make a change to the system, you're not actually just part

Dave Edwards 6:52
of it, no matter what Tony was not actually in the phone, no, he's not actually in

Helen Edwards 6:56
the phone. And whenever though, one of those whenever an iPhone comes out of the factory, and they're all exactly the same, the minute they're given to someone, and the minute you turned it on, and you start configuring your own apps, you are part of that system. And every single one of those interactions, every single one of those, like component parts of the human and the iPhone, are all different, immediately. They're all different. And that's a tricky concept to get your head around at the outset. But then, in many respects, when you go to the places that you see complexity and where complexity sort of arose as a, as a scientific discipline, a lot of it is quite intuitive, because this is the world we live in. It's the difficulty is that we're trained through our education system, and through the way that businesses are run. And the expectations that are set, essentially, at the very top of an organization, we're trained, we have that sort of complexity mindset trained out of us. We're trained to think in in linear kind of ways, we're trained to think in particular linear cause and effect where in some ways, we're trying to kind of remove that curiosity and wonder from thinking about the way the natural world behaves, and, and discouraged from applying those intuitions, because they make everything a little bit harder. It makes everything more uncertain. And we're not, we don't really like uncertainty, that ambiguity is quite unsettling, and is really hard to manage in an organizational context. But what we have now, I think, is a, as a significant shift underway, where, you know, 20 years ago, I think it was Stephen Hawkins, who said that, that 20, the the year to the 2000s, the 21st century, would be the century of complexity, and has taken 20 odd years to get to the point that we have so much complexity and in the world that people feel, and that people respond to that people are really thirsting and seeking a different way of understanding and thinking about some of these systems. So, complexity we see in human systems, humans, in fact, and it's pretty hard not it's pretty hard to find a human system that isn't complex. We see it in economies we, which is a very early field where complexity Sciences has been specifically applied. We see it in ecosystems and biological systems, we see it and things like the electricity grid. That's a complex system. And we'll we'll talk about why the electricity grid is a complex system. But the thing that really put complexity in front of all of us was the internet. That was the thing that really, I think, made the made all of our connections more complex. And the interesting part of that is those interactions are machine readable. And that's where algorithms and AI come in. And now we're on the cusp of a different kind of AI. And that generative AI really complexify as the system even fought more by adding more agency. Now, these are still unprovable, they're very much sort of theoretical or conceptual concepts, but they experience the lived experience of people and organizations trying to solve problems and trying to innovate and trying to understand what direction to take their organizations in and now benefiting from having a deeper appreciation of complexity ideas and how to apply them. Awesome.

Dave Edwards 11:14
hope you appreciate that, that intro episode, if you will, the first episode of figuring out about complexity. Click on to the next one where we're going to talk a bit about why the world feels more complex and why that's so hard.

Transcribed by https://otter.ai