What do a city, a forest, and your business ecosystem have in common? It turns out, a lot. All three are examples of complex adaptive systems.
Earlier this week I spoke at a conference hosted by the Royal Flemish Society of Engineers on the topic of complexity. The keynote speaker was Prof. Geoffrey West, former President of the Santa Fe Institute that pioneered the study of complexity science using a combination of economic theory and biology/physics (the founders were an economist and a physicist – both Nobel Laureates). The end goal of complexity research is to develop new integrated conceptual frameworks for understanding the interdependence between various complex adaptive systems that define our world, including cities, financial systems, and the environment.
West’s research suggests we may be able to use the same rubric to study both cities, and forests, and maybe even economies. Complex adaptive systems share certain characteristics. Among other things, they: have many nodes, are interconnected, are adaptive and resilient, have many participants that create bottom-up disruptive change, result in emergent phenomena, and are often subject to unintended consequences. Sounds a lot like the type of emerging business ecosystems we talk about here on the Wikinomics blog. Collaboration between large groups of disperse and diverse individuals is extremely complex; when you add in financial systems, various incentives, supply chains, and a global information network, it becomes even more so.
West also talks about different types of networks—often layered on top of each other—as a characteristic of complex adaptive systems. The better we can understand networks and their interdependence, the better equipped we will be to understand complexity. He believes that underlying all complex systems are simple rules or patterns. For example, if you look at the metabolic rate, size, and lifespan of various organisms, you can determine that every biological organism grows in the same fundamental way. Here West asks some compelling questions: Are cities and companies just very large organisms satisfying the laws of biology? If so, why do all companies eventually die, while almost all cities survive? To this end, I think there’s probably great value in studying the evolution and “biology” of collaborative networks, informal networks within enterprises, business ecosystems, information flow and knowledge networks, and the multitude of other networks that collectively define Wikinomics-enabled business practices.
So, what are the best types of structures to deal with complexity? If we base our answer on how the Santa Fe Institute is structured, we find that the solution to complexity requires a multidisciplinary approach that involves participants that can bring different perspectives and diverse expertise. It also necessitates an open, distributed, and collaborative approach, a willingness to take risks, and a relatively small executive team that is able to meet face-to-face in order to build consensus and drive decision making at the highest level. This sounds remarkably similar to what we prescribe for next generation enterprises that want to thrive in today’s dynamic business ecosystems.
Another interesting thought at the conference came from Prof. Francis Heylighen who spoke of the Internet as a global brain that may act to combat complexity at a macro level by reinforcing strong signals between parties and building “synapses.” Tied to the he global brain theory is his theory of human stigmergy—a mechanism of spontaneous, indirect coordination between agents or actions (think of the way ants and other insects develop collective intelligence that enables coordinated and fairly complicated activities).
All of this is very much related to the research we’re conducting here at nGenera regarding what we call the highly-instrumented enterprise where actions are increasingly digitized, sensors and software track and analyze new sources of data, and create new understanding of complex systems and emergent phenomena. Some examples of these types of tools in a Wikinomics context might include reality mining tools that track the behaviours of individuals; automated sentiment analysis of text, voice, and even video; platforms that generate data and offer venues for consensus-building; and enterprise monitoring tools that map the informal networks and measure productivity within organizations. In fact, I’m sure there are many more connections to be made here, and look forward to thinking more about this one and hearing thoughts from our Wikinomics readers.