It’s been a long time since I’ve blogged about the Complex Adaptive System Composition and Design Environment or CASCADE project run by John Paschkewitz. For a reminder, read these:
• Complex adaptive system design (part 1), Azimuth, 2 October 2016.
• Complex adaptive system design (part 2), Azimuth, 18 October 2016.
A lot has happened since then, and I want to explain it.
I’m working with Metron Scientific Solutions to develop new techniques for designing complex networks.
The particular problem we began cutting our teeth on is a search and rescue mission where a bunch of boats, planes and drones have to locate and save people who fall overboard during a boat race in the Caribbean Sea. Subsequently the Metron team expanded the scope to other search and rescue tasks. But the real goal is to develop very generally applicable new ideas on designing and ‘tasking’ networks of mobile agents—that is, designing these networks and telling the agents what to do.
We’re using the mathematics of ‘operads’, in part because Spivak’s work on operads has drawn a lot of attention and raised a lot of hopes:
An operad is a bunch of operations for sticking together smaller things to create bigger ones—I’ll explain this in detail later, but that’s the core idea. Spivak described some specific operads called ‘operads of wiring diagrams’ and illustrated some of their potential applications. But when we got going on our project, we wound up using a different class of operads, which I’ll call ‘network operads’.
Here’s our dream, which we’re still trying to make into a reality:
Network operads should make it easy to build a big network from smaller ones and have every agent know what to do. You should be able to ‘slap together’ a network, throwing in more agents and more links between them, and automatically have it do something reasonable. This should be more flexible than an approach where you need to know ahead of time exactly how many agents you have, and how they’re connected, before you can tell them what to do.
You don’t want a network to malfunction horribly because you forgot to hook it up correctly. You want to focus your attention on optimizing the network, not getting it to work at all. And you want everything to work so smoothly that it’s easy for the network to adapt to changing conditions.
To achieve this we’re using network operads, which are certain special ‘typed operads’. So before getting into the details of our approach, I should say a bit about typed operads. And I think that will be enough for today’s post: I don’t want to overwhelm you with too much information at once.
In general, a ‘typed operad’ describes ways of sticking together things of various types to get new things of various types. An ‘algebra’ of the operad gives a particular specification of these things and the results of sticking them together. For now I’ll skip the full definition of a typed operad and only highlight the most important features. A typed operad has:
• a set of types.
• collections of operations where . Here are the types of the inputs, while is the type of the output.
• ways to compose operations. Given an operation
we can compose them to get
These must obey some rules.
But if you haven’t seen operads before, you’re probably reeling in horror—so I need to rush in and save you by showing you the all-important pictures that help explain what’s going on!
First of all, you should visualize an operation as a little gizmo like this:
It has inputs at top and one output at bottom. Each input, and the output, has a ‘type’ taken from the set So, for example, if you operation takes two real numbers, adds them and spits out the closest integer, both input types would be ‘real’, while the output type would be ‘integer’.
The main thing we do with operations is compose them. Given an an operation we can compose it with operations
by feeding their outputs into the inputs of like this:
The result is an operation we call
Note that the input types of have to match the output types of the for this to work! This is the whole point of types: they forbid us from composing operations in ways that don’t make sense.
This avoids certain stupid mistakes. For example, you can take the square root of a positive number, but you may not want to take the square root of a negative number, and you definitely don’t want to take the square root of a hamburger. While you can land a plane on an airstrip, you probably don’t want to land a plane on a person.
The operations in an operad are quite abstract: they aren’t really operating on anything. To render them concrete, we need another idea: operads have ‘algebras’.
An algebra of the operad specifies a set of things of each type such that the operations of act on these sets. A bit more precisely, an algebra consists of:
• for each type a set of things of type
• an action of on that is, a collection of maps
obeying some rules.
In other words, an algebra turns each operation into a function that eats things of types and spits out a thing of type
When we get to designing systems with operads, the fact that the same operad can have many algebras will be useful. Our operad will have operations describing abstractly how to hook up networks to form larger networks. An algebra will give a specific implementation of these operations. We can use one algebra that’s fairly fine-grained and detailed about what the operations actually do, and another that’s less detailed. There will then be a map between from the first algebra to the second, called an ‘algebra homomorphism’, that forgets some fine-grained details.
There’s a lot more to say—all this is just the mathematical equivalent of clearing my throat before a speech—but I’ll stop here for now.
And as I do—since it also takes me time to stop talking—I should make it clear yet again that I haven’t even given the full definition of typed operads and their algebras! Besides the laws I didn’t write down, there’s other stuff I omitted. Most notably, there’s a way to permute the inputs of an operation in an operad, and operads have identity operations, one for each type.
To see the full definition of an ‘untyped’ operad, which is really an operad with just one type, go here:
• Wikipedia, Operad theory.
They just call it an ‘operad’. Note that they first explain ‘non-symmetric operads’, where you can’t permute the inputs of operations, and then explain operads, where you can.
If you’re mathematically sophisticated, you can easily guess the laws obeyed by a typed operad just by looking at this article and inserting the missing types. You can also see the laws written down in Spivak’s paper, but with some different terminology: he calls types ‘objects’, he calls operations ‘morphisms’, and he calls typed operads ‘symmetric colored operads’—or once he gets going, just ‘operads’.
You can also see the definition of a typed operad in Section 2.1 here:
• Donald Yau, Operads of wiring diagrams.
What I would call a typed operad with as its set of types, he calls an ‘-colored operad’.
I guess it’s already evident, but I’ll warn you that the terminology in this subject varies quite a lot from author to author: for example, a certain community calls typed operads ‘symmetric multicategories’. This is annoying at first but once you understand the subject it’s as ignorable as the fact that mathematicians have many different accents. The main thing to remember is that operads come in four main flavors, since they can either be typed or untyped, and they can either let you permute inputs or not. I’ll always be working with typed operads where you can permute inputs.
Finally, I’ll say that while the definition of operad looks lengthy and cumbersome at first, it becomes lean and elegant if you use more category theory.
Next time I’ll give you an example of an operad: the simplest ‘network
Some posts in this series:
• Part 1. CASCADE: the Complex Adaptive System Composition and Design Environment.
• Part 2. Metron’s software for system design.
• Part 3. Operads: the basic idea.
• Part 4. Network operads: an easy example.
• Part 5. Algebras of network operads: some easy examples.
• Part 6. Network models.
• Part 7. Step-by-step compositional design and tasking using commitment networks.
• Part 8. Compositional tasking using category-valued network models.
• Part 9 – Network models from Petri nets with catalysts.