Understanding the bigger picture, emergent properties & complex systems
As we’ve discussed in previous posts, a key part of this process is to be representative. However, that doesn’t mean gathering a huge number of samples. There are many forms of sicentific sampling which depend on what you’re trying to achieve.
When it comes to complex systems modelling (to inform accurate decision-making), the way we do this is all-important. Here we are going to illustrate the method with reference to a painting by Italian Renaissance artist Giuseppe Arcimboldo.
Lack of any framework to understand the context of the information gathered
An important aspect of being representative is about knowing where the pieces fit into the puzzle. For example, when we collect data poorly (e.g. by focusing on problems and not values) we end up with a big a sample of information but no way to know where most of this fits into the picture.
Many environmental decisions are made by understanding problems but with no idea where they sit in the context of any ecological, social or economic needs. This is akin to having a few jigsaw pieces and nowhere to put them.
We resolve this by creating a standardised framework. If we follow the method of focusing on values and assigning those to components of a model such as ecosystem services and features, the community data will be put in the right place.
Ignoring important factors
The other common mistake in environmental management is to limit focus to a small number of parameters. Usually these are the more tangible components that can be evaluated remotely or monetarily, without talking to local communities.
In this instance, we may know where the information sits within the overall socio-ecological picture but it’s only a fraction of the information we need to make sense of the overall picture. Therefore, it can be grossly misleading.
In this methodology we know where the information goes but we’ve only reall sampled the parts that we already knew about. All this achieves is more precision in those places. We have a bowl of fruit and some flowers but it still doesn’t give us the overall picture.
Systematic / Random Sampling Flaws
Two other common methods of trying to achieve representation is to gather information systematically or randomly. The problem with this approach is it can require enormous amounts of information to understand the picture. The left hand image (below) is systematic data; the right is random. A picture is beginning to emerge but it’s still unclear. We’ve also lost the precision on some of the things we know about e.g. the flowers. We also can’t be sure what we’ve missed either.
Random and systematic sampling have their place in statistics but don’t really work for complex systems modelling. Mostly because we are dealing with huge numbers of variables. There is no possible way we can talk to enough people or have enough workshops to create the picture this way. It’s not a feasible approach but neither is it necessary.
The Power of Emergent Properties
We’ve built a model that links Actions > Threats/Pressures > Ecosystem Features > Services > Values. The reason we’ve done this, is because we know that those are the emergent properties we need, to understand how the system behaves and to make good decisions.
While we don’t yet understand the bigger picture, our knowledge of these properties beforehand enables our community to precisely target a representative set of emergent properties in a systematic way.
For instance, let’s say you’re doing a jigsaw without any reference to the box lid. You don’t know if it shows a landscape, person, animals, historic building or fantasy. You will start by looking for the corners and edges as you know where they go. You’re already using emergent properties to place those pieces correctly.
If you’re then told that the image is a painting of a face, everything changes. You immediately start looking for the eyes, nose, ears and mouth pieces. Once again, you are building a model based on emergent properties. Initially you don’t know what the picture is exactly but very soon, you do … and you didn’t didn’t even need to complete most of the jigsaw before the answer revealed itself to you.
When we enable a process where communities gather information in accordance with emergent properties that are fit-for-purpose, we don’t need all the pieces. Not only does the picture reveal itself but we can more precisely tell what it is – in this case, a self-portrait of the artist as fruit!
The way we collect and position the information is critical
If the process of revealing a face is designed to choose a pair of glasses, for example, the shape of the nose and distance between the eyes might be critical to that decision. We don’t need to know the width of a person’s neck or hairstyle to do that. Imagine how much time and money would be wasted if your optician had to systematically sample your whole face.
Similarly, charged with deciding where to most efficiently spend public funds to maintain economic resilience, one needs to know which ecosystem features are most aligned with community values and ecological resilience. The only way to do this efficiently and systematically is:
to gather systematic data based on emergent properties; and
to ensure those emergent properties are in a structured framework that enables decisions to be made.
The beauty of this is it simplifies the method and makes it wholly achievable for community-led conservation.