Quotes in this blogpost are
from “Ecological
Computing,”

by John Seely Brown and Feng Zhao.

Confluences and their combined confluence

Looking back at the first decades of the third millennium, humans will see them as the era of the Great Transition, an unexpected result brought to us by a confluence of many confluences.

Writing about an omni-present, planet-scale sensor network that will dwarf the Internet by many orders of magnitude, and its implications for biological and computing
ecologies, John Seely Brown mentioned:

“The transformational force underlying this change is the confluence of recent rapid technological advances such as micro-electro-mechanical system (MEMS) sensors and actuators, wireless and mobile networking, and low-power embedded microprocessors… When the sensor grid becomes ubiquitous it becomes like an enormous digital retina
stretched over the surface of the planet.”

The idea of a planet-scale sensor network evokes an orbital view of not only the confluence of technological developments that make it possible but also, the other confluences that such network contributes to and mingles with. For example, the confluence of shifts from authority to authenticity as driver of social organization, from
scarcity to wide availability of knowledge, and from groupware to massively
distributed social media that link up mega-millions of minds.

“Let’s add intelligent browsers to this vast sensing system that lets scientists, government regulators, or environmental advocates use the internet to ask questions never before imaginable.”

When we’ll use such browsers for navigating on the ocean of data obtained from networked indicators of social well-being, collective moods, diseases in the global social body, and challenges to collective intelligence and wisdom, then we’ll have made a
decisive step towards the bulk of humankind joining in a self-aware meta-being.

Against methodological reductionism

“To build a system of this size, computer scientists and engineers will have to borrow ideas from biology and ecology, and figure out how large-scale complex systems adapt, repair and self organize. An open, two-way interaction between environmental scientists
and computer scientists is likely to have far-reaching implications for both
the computational and biological worlds for many decades to come.”

Besides technology, what can also be affected by learning from living systems is the world of social institutions and systems. I would not call “learning” the efforts to
mechanistically copy the principles of how the frequently referenced social
animals (beehive, ant colony, flock of birds, termites, school of fish, etc.)
behave. The mistake of the authors who propagate that reductionist view is in
the intent to draw guidance for or explain the higher-order complexity of human
society with the lower-order complexity of animal group behavior. The crucial
factor that they disregard is the values, worldviews, conscious choices, and
developmental stages in the individuation of people, which make instinct-based
collective behavior in human society impractical.

Where does the increasing popularity of the analogies with the social animals come from? It would be hard to not see the connection with escaping from the real but testing complexity of the social world, and at the same time, a large number of people moving from Orange to Green value system, in Spiral Dynamics terms, which can’t perceive
developmental stages above itself.

If we are to learn from the natural world, we need to go deeper than the behavioral level and look for inspiration in the basic evolutionary processes at play in biological
ecosystems and explore how they may be relevant in social and knowledge ecosystems.
That’s one of the intentions of my current research on “Designing bio-inspired
knowledge and technical ecosystems to augment collective intelligence.”

Control in ecosystems

“[H]ow such a distributed system can be controlled and by
whom are not simply answered by listing technical capabilities especially given
the self organizing nature of this kind of system.”

When we’ll have a technology focused on enabling harmonization across vast networks of communities, organization, and social systems, then its control needs to be massively distributed. If not, then Big Brother and the Borg would have too much fun
together.

The only way to make control in ecosystems, biological or social, sustainable is by giving up control as “power over” and, as a consequence, gaining control in the cybernetic
sense: the requisite variety needed by the self-regulation of a system can be
kept operational only by setting feedback loops on all of its vital functions.

Nature is good at that kind of control but as I mentioned earlier, human societies have some extra challenges in that domain. Over the millennia, we practiced control from above and it served as well until this point, when knowledge and trust-based
relationship became the key productive forces. Neither of them can be
effectively controlled from above. Observing how emergent forms of cybernetic
control works is the main task of co-sensing the future as it emerges through
zillions of independent and interdependent initiatives.