What we Need to Learn from NaturePosted: July 17, 2011
Julian Vincent, Professor of Biomimetics at the University of Bath, and a team of researchers wrote a paper in 2005 titled; “Biomimetics: its practice and theory”. It’s one of the earlier papers that really began to put biomimetics into context from a critical and pragmatic engineering sense. I have been dying to put this in a post, but there are so many different ways of approaching the content that I’ve been running around in circles. So let me get to the punch line and work my way back from there.
Here are two superb diagrams – cue sesame street music – can you spot the differences?:
Noticed the big differences?
Information vs Energy
None of this is new to any of us, we know humans in general “heat, beat and treat” our way out of our problems. But these diagrams actually begin to quantify and lay out the argument from a very tangible perspective. It also offers the opportunity to define what “structure” and “information” means in this context and how it can be applied to design thinking.
Tim McGee may be able to help out with more of these definitions, but in essence “information” from a materials perspective is the shape and structure within the material that creates the final property. John Warner, Janine Benyus and now Julian Vincent and Team are all highlighting the paradoxical complexity within natural materials, that there is a diverse array of combinations within a few simple, common chemicals.
In the paper this is described in the context of the exoskeleton of an organism, where the conflicting needs of stiffness and flexibility are required around the hinge joints, and openings for sensory cuticles.
Translated into cuticular structure, the hinge areas have different amounts and orientation of chitin (the fibrous component), and the matrix proteins are chemically different from the stiff areas and so more hydrated and softer; the geometry of the hinge can be linear (for an intersegmental membrane) or circular (for a hair socket).
What does this mean to designers?
The green chemistry and materials development conversations that have been brewing on this blog are a little bit “opaque” from an industrial design perspective. Designers for the most part have to wait for the materials to be developed, and must be content to play a role in creating a demand to fuel investment around materials research if possible. There is room to expand our thinking; having spent time with architects and urban planners recently, I recognize that this conversation of information rather than energy could occur at many levels.
The information, i.e. how a space is laid out, or how a city is planned, influences the energy, the material, the efficiencies, etc. I think there is a lot we understand intuitively, but the rigour that comes along with comparative research done by Julian Vincent could add some weight. The digital world loves this space, but the hardware dialogue has not caught up with the software dialogue, which is an opportunity for design education to explore.
One great way of looking at this is the difference between “designing” properties into a scenario that encourages emergent solutions rather than prescribed reactions. In essence, designing without an obsession for control at every level, which is what natural systems do:
…one of the basic features of living systems is the appearance of autonomy or independence of action, with a degree of unexpectedness directly related to the complexity of the living system. This gives living systems great adaptability and versatility, but at the expense of the predictability of the system’s behaviour by an external observer. In general, we do not accept unpredictability in technical systems; indeed, we avoid it.
The above quote is a little wordy and cumbersome, but beautiful. Kevin Kelly actually discusses this a lot in his book “Out of Control” (which is available for free as pdf here). From the opening chapter here is the generic recipe for distributed control:
- Do simple things first.
- Learn to do them flawlessly.
- Add new layers of activity over the results of the simple tasks.
- Don’t change the simple things.
- Make the new layer work as flawlessly as the simple.
- Repeat, ad infinitum.
Turns out the above is the process for designing intelligence in robots. The traditional approach of telling a robot how to deal with everything at once lead to hunks of incredible technology getting stuck on any kind of inconsistancy between the information it expected to see and the reality in front of its sensors. Apparently there are great stories of robotic cars stopping 5 metres in a race staring at the shadow of a tree it can’t compute.
I don’t have a lot of case studies at this level, but it does sound a lot like a description of good urban planning as opposed to the top down, everything at once model. I would love to know if anyone has case studies that explore this on multiple levels. I love the metaphor of information rather than energy and am curious to know where it exists beyond robotics. I’m sure it exists throughout the built landscape, I simply haven’t tuned my lens to see that yet.
Where did these graphs come from (and I want more)?
For anyone interested in diving deeper into this area of discussion, the paper I’ve got most of this information from is the tip of the ice burg. Here is some context to help people jump in.
The top graph, which analyzes human approaches to solving problems comes from analysis of patents. TRIZ is the problem solving tool that has emerged from this research that is popular amongst engineers. For those of you who don’t know, a solid definition of TRIZ is lifted from the Wikipedia site below:
“a problem-solving, analysis and forecasting tool derived from the study of patterns of invention in the global patent literature”
It’s an incredibly structured and rigorous approach to collecting, defining and proposing tangible methodologies for problem solving. The results of which are a series of provocative ways of approaching problems (called inventive principles), and there is a lot of content on line if you wish to explore this rabbit hole. What Julian Vincent and his team did in their research, was cross reference the functional solutions of nature (which became the second diagram) to the functional solutions of engineering. There is a complete section outlining the methodology, and I unfortunately do not have access to some of the references behind the content, which may not be a bad thing right now (it’s Saturday night, and I’m not sure why I don’t have a beer in my hand).
One of the results is an excellent list of possible methods of problem solving that include biological models to help frame the process. It is available for download here and is a list begging for a designer’s eye to visually translate and unlock it’s juicy goodness.
What we need to learn from nature?
As I’m digging into the scenarios of sustainability that might emerge from biomimicry thinking, this question of what to learn from nature keeps rattling around in my brain. The diagrams at the very beginning of this post are my recurring reference points for entering into this space, but I think the concepts are much deeper than I am currently able to process.
What will information, structure and space mean to designers in different contexts? How can these inventive principles be framed in such a way that they open up creative thinking for a diverse audience?
Look forward to discovering some of these answers…