Bacteria and other unicellular organisms have been shown to be autonomous and social beings manifesting cognition through association, remembering, forgetting, learning, signaling and responding to each other's signals to execute collective action. Researchers describe bacteria as "information processors" able both to send and receive signals, interpret and respond to signals by altering themselves and others, emitting signals in a self-regulated manner. Contrary to the premise that bacterial responses are entirely automated and predictable, bacteria exhibit rich behavior and have internal degrees of freedom. Professor Bonnie Bassler speaks here on Quorum-Sensing in Bacteria.

LEFT. Example of bacterial pattern formation in response to Septrin antibiotic. Colonies of Paenibacillus dedritiformis bacteria secrete attractor signals that cause their members to come together into large vortices, which increase the colony’s capacity to dilute the antibiotic with the lubricating fluid secreted by individual microbes.

Each bacterium has its own autonomy as a biotic system, and freedom within constraints to select its response to the biochemical messages it receives, including self-alteration, self-plasticity, and capacity for decision making to alter its behavior See Ben Jacob, E., Aharonov, Y. and Shapira, Y. 2005. Bacteria harnessing complexity. Biofilms 1. 239-263.

Image:
Eshel Ben-Jacob.

An internet search of the key words “molecular pattern recognition” turns up thousands of entries on this topic. To self-organize, life must have a discriminating algorithm that tells it what its own behalf is. Bacteria swim upstream in a glucose gradient. One need not confer consciousness on single-celled organisms to acknowledge that this behavior manifests a rudimentary capacity for autonomy and pattern recognition, capacity to sense and respond to their environment. The A-PR cycle spirals from bacteria to humans as increasingly sophisticated pattern recognition is achieved.

There are many competing definitions of complexity. Ecosystems are multi-agent, complex systems where each autonomous agent is a pattern recognizer, interpreting signals in context. Overemphasis on EVO, survival of fittest algorithms, neglects the DEVO dynamic of self-organization in evolution. Zann Gill posits the A-PR Hypothesis (autonomy and pattern recognition), that life continually self-organizes, adapting toward increased functional complexity. Nature's processes manifest directionality without a goal state. Living systems operate as innovation networks, harnessing principles of collaborative intelligence: quorum-sensing in bacteria; ants as more than collective intelligence systems of pre-programmed robots, the theory of facilitated variation operating in our cells via weak linkage.

Practical Applications. From micro level cellular mechanisms to macro level population genetics, evolution manifests a coordinated, multi-level Dyadic Model for innovation that next generation social networks could apply in decision support systems for eco-sustainability. Exponential acceleration of technology innovation extends the acceleration of biological evolution, posing challenges and opportunities. Competing hypotheses about the origin and evolution of life show nature’s adaptive mechanisms, evolvability, and the algorithmic implications of evo-devo debates, and also suggest a new collaborative computing paradigm, which can increase the effectiveness of human teams as “evolving, multi-agent ecosystems,” harnessing evolutionary principles to increase our collaborative intelligence.

Our current, conventional problem-solving model, based primarily upon “analysis of the facts,” is inappropriate for many of the problems we face today, which require a systematic (design) approach to synthesis and ways to acknowledge the role that human perception of the facts, and capacity for pattern recognition, plays in problem-solving.

The distant (or not so distant) future vision of a system to support collaborative intelligence will be inspired by life-like systems with autonomy and capacity for pattern recognition (the A-PR Hypothesis), enabling them to navigate. Life-like capacity for pattern recognition and choice may exhibit what Babbage alluded to as a violation, an unpredictability to which the system must be able to adapt, and intelligence to respond to random unpredictable elements, accepting and adapting usable random variations. Babbage, the computer visionary, may have seen beyond the possibilities of today’s algorithmic computing to an era of intelligent computing with the unpredictabilities of life. If so, he anticipated that collaborative intelligence will require the capacity to accept and respond to unpredictability.

Innovation (or Knowledge) Networks link participants, while maintaining their uniqueness and collaborative autonomy such that knowledge can evolve as networks grow, with potential for emergent, unpredictable patterns and innovative outcomes.

Problem mapping a priori, in contrast to information visualisation after-the-fact, generates visual frameworks, or “empty constructs” to structure the process of knowledge-gathering. Problem maps can evolve into navigable user interfaces. These open frameworks (partial patterns) tap the pattern recognition capabilities of users, serving as vehicles to order incoming information in process, and for use by participants during the problem-solving process. A classic example of a problem map is Dmitri Mendeleev’s Periodic Table of Elements, which prompted chemists to look for elements that appeared logically likely to exist, based upon the pattern of the Table.

Situation architecture addresses the key factor of contextualization. Meaning is interpreted in context and may differ, not only with different interpreters, but in different contexts.

Synergy is whole system behavior that cannot be predicted from the behavior of its parts.
Synergetics is the dynamics through which synergies are produced; both synergy and synergetics were key principles in the work of Buckminster Fuller.

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