Desiring Ecologies: Meaning-Making in the Network Wilderness

As networked information ecologies get more complex, interdependent, and unpredictable, designers must focus on the simple, foundational, and emergent. Connected networks are growing beyond our ability to grasp them as a whole. The big changes will catch us by surprise. Our best chance to positively impact these systems is by influencing the creation of effective small pieces that work as part of a holistic ecology.

This talk will explore the changing role of the information architect in the emerging wave of connected computing. It will propose strategies for reframing the way we approach information design in order to better create enriching and empowering experiences for users. We’ll look at examples from technology, cultural movements, and nature in order to frame a set of guidelines for creating systems that desire collaboration and clarity as an innate function of their underlying nature.

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The Wilderness. It’s beautiful. And it’s dangerous. You and I, who work in the information spaces of the internet, work in the midst of just such a wilderness. It’s complex; it’s unpredictable. There’s beauty – that’s what drew most of us there to begin with – but there’s also danger. We, and our users, can become lost, confused; we can go down the wrong path (sometimes never to be seen again). Our wilderness – just like the wilderness of trees and paths – is something that we’ll never know in its entirety. We’ll never know every hollow, every last little sunny patch. But we can learn how to navigate within it. And we can learn how to thrive. In some cases, these are the spaces in which we thrive the most.

Many of the tools that we bring to our wilderness, however, evolved in different spaces, in different environments: in the walled gardens of brochureware or in isolated valleys, neatly parceled off in 1028 by 764 pixel plots. The current environment of our information spaces regularly extends beyond these limits. The complexity of this kind of wilderness – of both of these wildernesses – is neatly summed up in a story that Peter Morville tells in his recent book Intertwingeled. It’s the story of Isle Royale National Park, in the NW corner of lake superior.

Isle Royale is remarkable because, due to its isolation, it’s the site of one of the longest continuous studies of the predator/prey relationship, in this case between moose and wolf. Back in the 50’s, researchers believed that they had discovered the key mechanisms of the predator/prey relationship – and from that some of the key mechanisms of the cyclical balance of nature, how these kinds of ecosystems work together.

One of these researchers, however, Durwood Allen, looked at these cycles longer than anyone else did. What Durwood Allen found was that every 5 year period was different than every 5 year period before it – even after a period of 50 years. In fact, what at first looked like a clear -cut cycle was considerably more complex. It was a constantly changing array of stocks and flows, modified by feedback loops and shocks from the outside.

It’s these outside shocks that I find particularly interesting. Peter gives examples of several of these in his account. One of these is a lone visitor with a dog who comes onto the island and unwittingly – and, illegally, as it were – brings with them a canine virus that decimates a huge chunk of the wolf population. Another of these shocks is an ice bridge that forms between the island and the mainland, occasionally allowing a wolf from the mainland to come over to the island and reintroduce genetic diversity to the wolf population. On the moose side of things, there are periodic outbreaks of moose tick and climate variations, both short terms and long term, that affect the food supply.

Our position in the network wilderness is similar to this discovery about Isle Royale in two ways. First, what we thought we knew is constantly remaking itself. Second, that remaking is caused by individual, desiring agents acting on intrinsic motivations. In this case, we see the visitor with his dog, the wolf on the ice bridge – even the moose tick – acting on these intrinsic motivations. This similarity offers us a clue about our own situation: our information ecologies are also built by individual agents, acting on intrinsic motivations. This offers us a model that we can use to design for these emerging information spaces. I’d like to share with you today an example of one of these models, as well as some of the tools that you can use to integrate it into our your practice.

Now, we all know that when you get a bunch of Information Architects together, and you talk about Information Architecture, no matter where the conversation starts, it alway eventually devolves into an argument about what IA is. So let’s start by agreeing on something that should be very obvious and pretty simple: It’s the job of information architects to make connected environments intelligible to everyday users.

Where we likewise often disagree on the specific methods we use to achieve this goal, we often agree that designing for this goal generally involves designing for search and designing for navigation. Search, of course, is the process we normally think of as designing metadata structures, balancing recall and precision, fine-tuning our search taxonomy, our SEO techniques. In terms of meaning-making, i.e. how we make content intelligible, I like to think of search as mapping what we know to what we find. As Andrew Hinton puts it in his recent book Designing for Context, search is one of the ways that we “Poke at the environment with words, giving our words to the environment to see what it says back to us.” We then take that response and we incorporate it with our own understanding and create new meaning as a result.

Orienteering, or navigation design, serves a complimentary role. We normally think of this in terms of menus, page layout, and information hierarchy. In terms of meaning-making, I like to think of orienteering as mapping what we find to what we know. In orienteering we leverage our embodied perception of the environment around us, and we make sense of that environment, and of these new places, on their own terms. Orienteering is a function of how we relate to our physical world as embodied individuals, and of how we represent that world in language – both to other people, and to ourselves.

Both of these interpretations of what we do as IAs share an emphasis on creating new meaning in context. In his seminal 1989 Design Quarterly article “Hats,” Richard Saul Wurman writes that “the organization of new information creates new information.” This, of course, is the core of what we do as IAs. We structure orienteering and, in a related way, search in order to frame the way we want our visitors to understand the space that we’ve created and the content that they can find there. We create bounded and simplified models of larger, complex systems and, like Isle Royale, that framing creates a particular kind of understanding.

In the world of the web, that framing has often looked a bit like this. This has worked in our limited models – in our walled gardens, in our isolated valley intranets. Many of these tools, however, treat information as if it’s isolated. And while this may have at one time at least have been operationally true, the increasing connectivity of our information spaces is quickly breaking down the neat borders of these closed systems.

We’re all familiar with the proliferation of devices – virtually everything in “smart” these days. We’re beginning to come up with a pretty good initial understandings of how to design for these spaces – responsive web design is certainly a shining example here – but we’re also seeing an increasing fluidity of data. Information is increasingly breaking free of pages, articles, and screens. As designers for these spaces, it is incumbent upon us to move beyond our labels and categories. We need to – and have started to – think about how these things come together in context. All this said, however, the classic trap in the wilderness is that we miss the forest because of our focus on the trees. Connected content on diverse and distribute devices constitutes networks. But those networks then go on to create new and surprising consequences all on their own. And this is happening at an increasing exponential rate. What this means for us is that we currently stand at the threshold of an incalculably large emergent organic information network.

This network is emergent because it’s a case where the behavior and the composition of the whole is fundamentally different than that of each of its individual parts. It’s where simple entities combine to create effects that no single one of them could do on its own. It is organic both due to the interdependence of its component parts, and due to their differentiation. Both of these elements together are what make these emerging networks powerful. But they’re also what makes them surprising. And this is the tricky part: because of the exponential nature of network growth, these emergent organic networks are fundamentally non-linear and, as a result, they are deeply unintuitive.

In their book Trillions, Joe Lucas, Peter Ballay, and Micky McManus illustrate this surprising side of exponential growth with the story of the mathematician and the king. Once upon a time a king offers his court mathematician a reward for a deed well done in the service of the kingdom. Instead of asking for land or gold, the mathematician asks for the quantity of rice it would take to fill a chessboard by first placing a single grain on the first square, then doubling that amount for the second square, then doubling that amount for the third square, and so on, until the chess board is full.

The king bursts out laughing; he thinks his mathematician is a fool after all, but for his own amusement, he orders the request fulfilled. What he finds, however – not even halfway through the chessboard – is that there is not enough rice in the kingdom to satisfy the request. The mathematician at this point corrects him and he says, “Sire, you’re mistaken – there is not enough rice in the universe to fulfill this request.”

The story has one of two endings: Either the king is so impressed with the mathematician’s acumen that he marries him to his beautiful daughter and they live happily ever after in a castle on the hill. The alternate ending, of course, is that the king is now kind of miffed that his mathematician is being a smart-ass, he chops his head off, and the story is over. No matter what the outcome, the mathematician’s point is clear: we consistently, and intuitively, underestimate the power of network growth.

I have an epilogue to this story: we do this even when we know that’s what’s going on. Many of you have heard this story (or version of it) before. One where we’re supposed to learn how “surprising” network growth can be. As I was putting this example together I thought to myself “I wonder how much rice that actually is?” … more than there is in the universe? That didn’t really seem possible.

So I did some research – which means that I Googled it – and what I found was that with one year of world production, you would have one one-thousandth of the grains needed to fill the last square. By another measure, that same quantity of rice would cover the entire surface of the earth several inches deep. This is the same kind of growth that is going on in the network all around us. It is difficult to understand and impossible to predict.

The outcome of this, of course, could be catastrophe. And there are plenty of dystopian stories to help us imagine what that might look like. It could, however, as Lucas, Ballay, and McManus put it, look a lot like life. The challenge for us, then, is to find ways to get beyond our isolated boxes and arrows models. Our islands are quickly connecting up with others. Up until recently, we’ve redesigned entire websites just to get them to work on mobile phones. Our strategy for wearable is still very much to approach them from the ground up – particularly if they don’t involve a screen (which, let’s face it, is relying on print technology that is hundreds of years old). The rate of change of both the devices and the information that flows across them is, of course, speeding up.

This is a daunting challenge: these networks are not only moving faster than we are, they are also accelerating. The benefit here, though, is potentially breathtaking. These networks, in loosely coupled ecologies, can perform in ways that would be difficult, if not impossible to architect from the outset.

Certainly one of the most famous recent examples of this is the Arab Spring. This is were Facebook and Twitter played unprecedented roles in organizing social action. Philip Howard from Wired writes that “Social media became a critical part in toolkit for greater freedom”. It was essential to sharing videos and commentaries, and was a catalyst for spanning dialogues through regions and across borders. These tools allowed for a forum for issues thee couldn’t be debated -– and sometimes, couldn’t even be discussed – in public.

Likewise, in Hong Kong the mesh networking app Firechat allowed protestors to coordinate in the absence of net infrastructure. This is case where a relatively simple app turned otherwise typical network limitations upside down. Mica Benoliel of Open Gardens writes that “ usually, the more people in the same location, the less connectivity you get. With Firechat, it’s the opposite.”

In each of these cases, the individual components that make up each of these systems are simple at their core. Twitter, a Facebook newsfeed, a text messaging app, each combines in context, organically, as interdependent component parts to form a whole that is fundamentally different than its pieces. In each of these cases, these component pieces want to do just a couple of things well. Just as with Isle Royale, these ecologies are formed by desiring agents. This convergence of technology and exponential growth begs a new approach – or at least a new perspective – for how we design for these kinds of spaces. In effect, designing for emergence means designing for desire.

This is what we saw in the examples we just looked at. We saw how social media emerged into networked border crossing that transcends state control. We saw how text messaging emerged into mesh networks that transcend infrastructure. Adam Greenfield, in his book Everywhere, gives some additional examples of these kinds of agents that are simple at their core, but complex in their effects. One of these examples are Radio Frequency ID chips. Greenfield writes that RFID chips want to be everywhere because of their cheapness and their shrinking size. Much of the hype of RFID chips has blown over in recent years and we’ve moved on to shinier things, but these are fundamentally transforming what it means to be connected, what it means to be smart. We no longer need a battery, a processor, memory, and a wi-fi connection. You can slap a 3 cent sticker on a milk carton and it’s on the network. Greenfield also talks about IPv6. This is the internet protocol that replaces IPv4. Greenfield writes that IPv6 has enough addresses that every grain of sand can have its own IP address – over and over again. IPv6 wants to transform every thing and every piece of every thing – into a node.

In each of these examples, we see design that works with its environment to fulfill designed in motivations and designed in desires. For those of us that work in building websites, this kind of emergent organic behavior seems new, like some trick that our manager put on our plate and said, “your problem now.” This combination of environment and structural composition that leads to these behaviors, however, is all around us. And it has been all around us for quite a while.

In Beyond the Brain, Louise Barrett shows how the behavior of ants is and example of just such a combination. She describes how ants find and follow the shortest route to food by laying down and following pheromone trails. As an ant is out foraging, it leaves a pheromone trail. When it finds food, it immediately returns back to the colony, again leaving a pheromone trail. But because the inbound ant is moving faster than all the other ants, the pheromone trial that it leaves has less time to evaporate, so it’s a stronger trail. Other ants are more likely to follow a stronger pheromone trail – they’re more likely to follow this trail. Of course, we know how this story ends up: it’s that pretty soon the whole colony is in your potato salad.

We tend to infer from this that in order to pull off such complex navigation and coordination, that ants must have equally complicated navigational and coordination abilities. This complexity of behavior, though, isn’t the complexity of the ant. It’s the interaction of the ant and the terrain. In reality, the ant is using a set of very simple rules. It is the interaction of these rules with the environment that equals complex behavior. The insight here is that there’s no necessary correlation in complexity of behavior and the complexity of the mechanism producing it.

Lucas, Ballay, & McManus identify four simple principles that lead to this kind of complex behavior. These make up what they call “Beautiful Complexity.” The first of these elements is hierarchy. This is the idea that parts are assembled into components that become larger wholes that are fundamentally different in kind than any one of the individual parts. The second component they identify is modularity. This is the interchangeability of capabilities and needs that often leads to the interchangeability of parts (though it’s not simply the standardization of parts, or things that are stamped out in identical models).

The third element they identify is redundancy. This is the idea that extra information is encoded into an object that protects meaning when part of the expression is vague or missing. Here again we we see examples of this all over in the environment. Our DNA is probably one of the most striking examples for us. Not only repeated in every one of the 37 trillion cells in our bodies, but also hugely redundant in each individual strand. Generativity is the last element that they identify. This is where the other three elements work together to create novel forms.

So. So far I’ve talked about:

The work we do to create meaning as IAs.
The challenges and opportunities in emerging, organic networks.
What it means to be designed for desire.
And how simple rules executed in their environment can lead to complex behavior.
Now, with that in mind, I’d like to look at an example of how these principles can be applied to build structures that want to be understood in context.

The Guardian recently completed a responsive redesign of their website. Built responsively on a single codebase, it works across phone, tablet, desktop. We can see in the desktop example here that their global nav spans the entire content column. You’ve got everything that the Guardian is about, all its labels, right up there where you can see them. In one glance you can take in what this space is and what you can do there.

In a recent write-up, the designers of the Guardian described the importance they discovered of page level navigation. They write “we saw from analyzing user data, that once a user gets to a certain point in their journey, they stop using the navigation, and instead navigate through content.”

Now, they’ve done an excellent job of maintaining the integrity of their page level design across all of these form factors. Perhaps as a result of this insight, however, they’ve also built in a kind of … awkward navigation structure into their mobile version. We can see that, on mobile they’re actually using that same full menu, but since it doesn’t fit on the mobile screen, they’ve put it behind a little window that you scroll horizontally, to see what those different labels are.

Now the content is still there, but the ideal that you can look at this site and see at a glance everything that it’s about and how those pieces fit together is lost. To be fair, they also have a dropdown menu where you can see all of those items, but of course this then covers up the screen and you lose that page level navigation they were trying to maintain.

What I see here is a mapping of a rigid rendering of a design taxonomy into a space in which, frankly, it doesn’t fit. The taxonomy as I’ve reverse engineered it here doesn’t want to be understood across contexts as a relationship of terms and concepts. What it wants to do is impose its fixed structure wherever it appears. In this case what we see is more about the structure of this iteration than about the relationship of these terms in context – or about making that relationship intelligible to users.

With this as a starting point, I’d like to show an example of what a design taxonomy that wants to be, that desires to be understood in context might look like.e And we’ll use the Guardian as a test case. This is a process I call “articulated taxonomy.” And it consists of three steps:
We define a vision, we create a base taxonomy, and then we articulate that taxonomy in context.

I’ll go through each one of these. Our visions is, simply put, a simple statement that informs and guides our information design. This is where we provide a central point of cohesion, and this is what influences the choice see make about structure, categorization, and labeling.

The Guardian is an organization that values news that is the result of vigorous debate within the paper and it considers itself an organ of the middle class. A visions statement for the Guardian might read something like “To inform and empower the UK middle class.” In contrast, the local newspaper in the neighborhood where I grew up would have a very different set of goals. So they would have a different vision. Theirs might read something like “connecting and enriching the community.”

In both of these cases a vision statement – even one a simple as these – helps focus the information design efforts on he concepts being conveyed, and helps ensure that the taxonomy that we create from those concepts emerges in a way that is consistent with its environment.

Our vision defined, our next step is to create our taxonomy. In this case, I’m referring explicitly to a design taxonomy, as opposed to a retrieval taxonomy – though, of course, the two are closely interrelated in any given project. A taxonomy in this context is simply a method of arrangement designed to create a particular kind of understanding. This is where we create new meaning with structure, and this is the model that we use to communicate our vision.

For the Guardian, we saw a basic sense of their taxonomy as I derived it from page layout and navigation. We also saw how once a structure like this is in place, it tends to become immutable. It’s a monolithic block. The taxonomy is done. Don’t touch it.

If we take it apart a little bit, though, and we examine the models that make it up, we can actually see that there is a simpler set of modular relationships working together generatively to create something new. This top level of relationships, for instance, is based on categories: world, sports , culture, money, environment. We see relationships based on location: countries, economic regions, environmental regions. We have relationships based on time; this would be the up-to-datedness of sports scores, the newness of an article, the life stage of a reader. Relationships based on personality; these are particularly relevant for the opinion and advice columns. And then, finally, we have relationships based on hierarchy, and this is just a simple magnitude scale of popularity.

What we see by taking this apart a little bit are the component pieces of the models used to assemble this whole. And what we find are five basic classifications. Five basic ways that are embedded in the way that we think as embodied, linguistic thinkers. And I call these “basic” not arbitrarily or because I want them to be basic, but because we live in bodies that have to be in a location at a time. We don’t have a choice around this; we understand it with our beings. When we use language to communicate with people, we attribute personality. And we project personality. And then we make all sorts of judgements because of that. We do the same thing when we interact with objects. If an object has what we call “personality,” we treat it differently that something that is seen as dumb, or without personality.

These five categories, of course, aren’t unique to the guardian. Richard Saul Wurman, again in his Hats article, says that there are only five ways that we classify things in the world: location, alphabet, time, category, and hierarchy. I’ve chosen a couple of different categories for this example, but in most cases, in these kinds of exercises, you find only about a half dozen different ways that things are being organized. Everything else is a combination of those base relationships. It’s a composite model. As designers, we choose which elements to highlight and which to hide on a case by case basis. This ensures that our vision, the new meaning we create in context, is communicated in context.

It also means that any given composite structure is only one of several possibilities. And we can see this by taking this model apart a little bit more. If we take the first Guardian article from each of these top level categories, we can see the other combinations implicit in the component parts. These are the articles that appeared on the landing page of each one of these categories on the Guardian website recently.

Keeping in mind the visions we’ve defined, “To inform and empower the UK middle class,” if we reorganize these exclusively by location, we find a different set of classifications and labels. We might get: the Americas, the UK, Europe. And we’ll even get an outlier here. Now, as IAs and as taxonomists, this makes us sweat a little bit. The miscellaneous category, it feels like losing. In this model, though, remember that this is just one organ that’s going to work among others. It’s a simple structure that works in a larger system. We don’t expect it to be sufficient on its own. In fact it would be kind of weird if it were – like if my liver was moonlighting as a bartender while I was asleep. (Though that could explain some things ….)

Of course, location isn’t the only way we can structure this content. If we look at each of these articles by time, we get a different set of classifications: developing stories, stories that are breaking, stories that are looking ahead. We can also organize by personality. Here we might get artists, athletes, leaders, pundits. Again, we’ll have one that’s kind of an outlier. Still not worried about it. Finally, we can organize all of these by hierarchy, according to how popular they are. And here we get that same magnitude scale of popularity.

In any one of these simple classification schemes, we can organize most of our content in a way that reflects our vision. When we take each of these individual schemas as a collective whole, then, we get an information model that can act as a point of articulation between the vision that we started out with, and the contextually appropriate interface or a particular design implementation.

This brings us to our third step, articulation. In the sense I mean it here, articulation is best summed up as the expression of a concept in context. This is where we define the breakpoints of an information model, to again borrow from responsive web design. This is where we create coherency across contexts.

To continue with our example from the Guardian, on a large screen with lots of display space, we might see the Guardian’s categories in full. And that would give us a navigation model much like we’ve already seen on the Guardian desktop and mobile site. On a small device with limited display capability, however, we might see a smaller selection of categories, pulled from different levels of the base taxonomy. This would give us a different set of top level categories, articulated from the same model.

Since both of these are derived from and follow the same base set of classifications, they allow us to communicate vision in the same way. More importantly, they allow us as designers to flex the model responsively to accommodate contextual changes. They allow our vision to emerge in a given context by working with its environment instead of fighting against it.

There two advantages here. One is that we avoid continuing to map the constraints of one technology onto the technologies that follow. In this case, we see the mapping of the constraints of a cursor/pointer interface onto a mobile device. We more often see it go the other direction, where you have huge desktop site and all of your nav is hidden behind a hamburger menu. Evidently now, as Luke Wroblewski recently tweeted, we also have kabob menus, and bento box menus, and … I think there’s one other mystery meat that’s escaping me at the moment – ah, right, “the meatball menu,” thank you.

So, this mapping of constraints is rampant. But there’s also a second advantage: and that’s that we avoid reinventing our information model on a case-by-case basis – which, as I hope, given our discussion on network growth, we can all see is becoming increasingly impractical.

Now, if you’re like most people I talk to about this approach and technique, this is where you likely have an objection. You may be saying, “Okay, that’s great – but those nav patterns are different. The menus are different.” And you’re right; they are. They’re not consistent. And as designers, this makes us incredibly anxious. Because it makes our clients anxious.

As embodied, linguistic sense-makers, however, consistency is much less important to us than coherency, which depends on context. Coherency is how we make sense of much of the rest of the world. In Digital Ground Malcolm McCullough writes that “navigation consists of making decisions at landmarks, even if the resulting picture is less of a map than a recombined collage.” An articulated, recombined approach to information design allows us to frame our information spaces in the same way that we frame our understanding of the world in language every day. This, in turn, allows us to communicate our vision across contexts, and even as new contexts continue to emerge.

What I’ve shown you here is one speculative example with the Guardian. As you look around at the information spaces we work in, with these ideas of beautiful complexity in mind, you can actually see this articulated, recombined approach, increasingly cropping up in production level environments.

The BBC, for instance, has recently completed a mobile redesign of their site. This is the desktop site we see here. We have a main content column with featured articles, we have top level nav across the top, we have a sidebar heading with a “watch” label, with video thumbnails beneath it, and then “most watched” and “most read” in a tabbed module down below that.

As we go to the mobile site, we can actually see that this “most read” and “most watched” element has become the primary way you toggle between information in the main content area. There is also a dropdown menu that gives you that global nav, but if you want to get to these thumbnails you see under “watch” on the desktop screen, you actually have to navigate several levels down. Likewise, the “most read” and “most watched” tabs become “most read” and “top stories” on mobile. In fact, top stories is implicit in the desktop site because we just see those right there in front of us.

The BBC site remains consistent in terms of color content, and typography. They’ve kept their brand consistent across these two particular touchpoints. But it is inconsistent in terms of wayfinding. The path to content remains clear, however, because it remains coherent – even when the labels and categories aren’t consistent across these contexts. In fact, I would argue that it is more coherent because they’re not consistent: they’re articulated in a contextually appropriate way for each of these devices.

In this example, and in the test case for the Guardian that I showed earlier, we see elements of beautiful complexity. We see hierarchy, modularity, and redundancy, all working together in a generative way. This set of principles, in combination with the work we already do as IAs, gives us one way to design more effectively for emergent, organic networks. And this is a challenge. As a culture, we love the myth of the master builder. We love the myth of the architect. We’re fascinated with the idea that someone is in control – even if it’s a villain. At the end of the Matrix trilogy, the architect reveals to Neo that he designed the Matrix in its entirety according to his own, as it were, diabolical plan. Up until now, we have been able to be the masters of our limited models, the architects of our walled gardens and hidden valleys, of our seemingly isolated islands, cut off from everything.

The connected ecologies of fluid information and ubiquitous computing, however, are increasingly becoming far more complex. In the closing chapters of Trillions, Lucas, Ballay, and McManus write that “the designer that aspires to build on the scale of ecology must abandon the conceit of absolute control of her medium and seek instead the abstract understanding of the scientist.” In the examples I’ve shown, I’ve argued that one way we can do this is to pay close attention to the design of the simplest and most fundamental components of what we build – the ones that make sense to us at base levels as linguistic and embodied sense makers – and then to craft models that allow the new information we create with structure to emerge organically in context. The principles of beautiful complexity offer us one set of signpost to show us how to do this.

Ultimately, however, what all of this points to is the power of letting go of absolute control – and that this is necessary when it comes to designing for complex systems. In order to help us envision this goal, let me suggest a replacement – or, at least a companion – for the architect: the walking stick.

The walking stick fascinates me because, to put it bluntly, it’s not all wired up. It’s legs aren’t’ connected in any kind of neurological way. Each one operates on its own and just, more or less, gets along with the others. It does this by following a set of simple rules and working with its environment instead of against it.

To lovers of absolute control like many of us, as a design strategy, this probably sounds insane. It is, however, nature’s way of dealing with complexity in the face of limited resources. In Beyond the Brain, Louise Barrette writes that “it is both cheaper and more efficient for the walking stick to exploit the structure of its environment and its … effects on joint angles than to build a more complex neural network.” This is what she and others call loose coupling, or soft assembly.

This idea of loose coupling is at the core of desiring ecologies. It represents a fundamental shift in our approach to design when it comes to ecological systems. This, then, represents one way that we can let go of control, yet still provide direction to systems that are quickly rising to a level of complexity far beyond even the most astute architect.

Lucas, Ballay, and McManus finish the quote I cited a moment ago by noting that “we cannot specify an ecology, but nor are we powerless to affect its evolution.” The flexible strategies for negotiating complex systems that I’ve shown you today are a step in this direction. They’re not the last step, but they are one that gets us closer to effective information design in the emerging information ecologies that are presently reshaping the world around us.

Thank you.

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