A couple of weeks ago I had the pleasure of sitting in on an Organicities design studio at EPFL, focused on “the digital generation of architecture using biological paradigms.” It was an exploration of the potential these paradigms might hold for generating effective public, corporate, and community spaces in urban environments.
Compared to studying our own behaviour, it seems easier for us to study biological systems when the physical scale is such that, with current technology, we can make observations which statistically span many members of those systems, allowing us to compare and correlate individual behaviours with global ones. In these systems, we have often observed coherence of purpose at varying scales we either do not (yet?) posses, or have not formally been able to observe in human systems.
There are broad open questions about the lessons we can take from these structural observations. Have we not yet adopted them because the optimal structure for collections of humans lies elsewhere? Because we have not yet achieved the necessary biology, skills or technology? Would mimicking and learning from them be a step forward or backward in terms of some common reference for progress?
As and while we tackle those questions, evolutionary algorithms remain a rich source of generative structures. Seeing the students’ projects in the Organicities studio planted a seed in my brain. Could we apply these algorithms as design principles for growing organizational structures?
Imagine an algorithm that adheres to patterns of growth or branching consistent with large scale desirable behaviours, such as collaboration, and relatively flat, egalitarian hierarchies. Such algorithms could be parametrized by organizational properties like size, physical distribution, or production lines. They could then be used to derive institutional roles and collaborative structures predicted to incentivize these positive emergent behaviours.
Even more interesting, what if we could use such a system to guide the evolution of so-called ‘learning organizations’? Simulations of the dynamics of these structures might make it clear where a proposed change is feasible versus destructive, based on its structural ability to evolve in such a way. It might also help leaders understand the phases of growth of their organization, and when it is or is not time for a large change, and if so, in what direction.
Granted, the students at EPFL were designing physical architecture, not human systems. But we have a long (and reasonably successful) history of modeling human systems as graph networks, and these have an intuitive mapping onto organization positions, job descriptions and group responsibilities.
Whether or not approaches such as these are fruitful, or only direct future research, it’s certainly exciting to think about the ways that modeling and statistical sciences can inform a more rigourous analysis space of possibilities. It’s also nice that it supports explorations and associations where formalizations have yet to catch up; namely, in the self-similar, recurring patterns of nature.
One Comment
Your comment gave me a flash of insight about a favourite movie of mine, Antonia’s Line. It’s an obvious ‘algorythm’—behaviours/attitudes travel down through generations. In this case, love and acceptance.
Your article made me see it slightly differently and also ask how we can be more aware of how and when we influence those around us. Is there a critical mass of influence, i.e. (dare I say it?) some kind of ‘tipping point’?
Also, I saw a show at the Textile Museum about using biological growth mechanisms to create architectural structures. It was more speculative that implemented, but some were actual. I still have the brochures; if you’re interested, I could mail them to you. They’re small. But I would like them back at some point.
V.