Autonomy comes from ancient Greek: auto-nomos — auto, "self" + nomos, "law" — "ruled by one's own laws." In political theory this term is associated with the right of self-determination and self-governance. More recently, in engineering, this term anticipates the objective to engineer self-regulating, autonomous systems.

What defines autonomy, and how it is achieved, can be endlessly debated. This page focuses on one small segment of this large topic, the definition of "collaborative autonomy." Although an apparent contradiction in terms, collaborative autonomy can also be viewed as the balance of opposed forces (illustrated by tensegrity structures) — the foundation for all living sytems.

Collaborative autonomy is a principle posited by Zann Gill as supplying the critical link between the A-PR Hypothesis and collaborative intelligence. Gill proposes the A-PR Hypothesis, that two key capacities of life, Autonomy and Pattern Recognition, together make evolution toward increased functionality possible:

• autonomy of individuals as unique participants in their ecosystems,
• individuals’ differing capacities for pattern recognition (together A-PR).

Whereas we tend to assume individuality is the foundation for competition, collaboration implies suppressing individuality to an agreed consensus. A paradigm shift is needed to understand that exactly the opposite is true.

Leading evolutionary theorists have noted the central role that individuality plays in driving evolutionary advance toward increased functional effectiveness. Differences between individuals, and collaboration among individuals, are prerequisites for evolution. Sir Charles Sherrington's classic statement: “Life is always and has always been individual. That is related to its mode of generation. There is here no question of a ‘universal’ because any attempt at definition of life must start out with the concept of the ‘individual,’ otherwise it would not be life." Harvard evolutionary biologist Ernst Mayr (1904 – 2005) noted that competition among individuals would be irrelevant if individuals were typologically identical. Individual differences are a prerequisite for evolution. Contemporary biologist Leo Buss at Yale University formulated his hypothesis that individuality evolves through the collaboration of components into a higher, more complex individual. Coordinated collaboration precedes and enables individual specialization.

Evolutionary biology has underscored the importance of the individual for evolution, as highlighted by Louis Menand: "Once our attention is redirected to the individual, we need another way of making generalizations. We are no longer interested in the conformity of an individual to an ideal type; we are now interested in the relation of an individual to the other individuals with which it interacts. To generalize about groups of interacting individuals, we need to drop the language of types and essences, which is prescriptive (telling us what finches should be), and adopt the language of statistics and probability, which is predictive (telling us what the average finch, under specified conditions, is likely to do). Relations will be more important than categories; functions, which are variable, will be more important than purposes; transitions will be more important than boundaries; sequences will be more important than hierarchies."

As in evolution, individual differences are critical, so also Innovation (or Knowledge) Networks link participants, maintaining their uniqueness and collaborative autonomy such that knowledge can evolve as networks grow, with potential for emergent, unpredictable patterns and innovative outcomes manifesting collaborative intelligence.Similarly, individuals create the collaborative ecosystems for their own survival and development.

Translating evolutionary principles to social systems and next generation social networks: Collaborative autonomy is the principle underpinning collaborative intelligence through which individual contributors maintain their roles and priorities as they apply their unique skills and leadership autonomy in a problem-solving process. Individuals are not homogenized, as in consensus-driven processes, nor equalized through quantitative data processing, as in collective intelligence. Consensus is not required. Problem resolution is achieved through systematic convergence toward coherent results. Collaborative intelligence relies on the principle of collaborative autonomy to overcome “the consensus barrier” to succeed where other methods have failed.

Future Directions. Automated and Autonomous Sensor Networks are of interest as a very basic instance of a non-living collaborative intelligence network. Better understanding of collaborative intelligence is needed to drive innovation in a range of areas, including:

• Automated sensing activities
• Autonomous/Intelligent sensor networks to support social networks
• Autonomous/Intelligent social sensor networks
• Competing sensors or sensor networks
• Collaborating sensors and sensor networks
• Context awareness in sensor networks
• Data management in sensor networks
• Data and resource sharing in sensor networks
• Decision support systems for sensor networks
• Heterogeneous sensor networks
• Intelligent sensors and sensor networks
• Multi-service sensor networks
• New architectures and protocols for sensor networks
• New sensor network applications
• Quality of service in sensor networks
• Resource management in sensor networks
• Self-organization and self-adaptation in sensor networks
• Semantic-based management of sensor networks
• Sensor networks for autonomous/intelligent management of spatial resources
• Sensor networks for autonomous/intelligent risk management
• Sensor network control
• Sensor network maintenance
• Sensor networks for sustainable development
• Sensor networks on the web
• Sustainable sensor networks
• Virtual environment for supporting sensor networks

Autonomy is a concept that has its foundations in the life sciences, but significant implications in engineering and in the socio-political domain, e.g in books such as Autonomy: applications and implications by Markku Suksi.

Autonomy. Individual contributors collaborate, maintaining their diversity of roles and priorities as they apply unique skills in a problem-solving process. Individuals are not homogenized, as in consensus-driven processes, nor equalized through quantitative data processing, as in collective intelligence.

Pattern Recognition. Unique capacities for pattern recognition and interpretation characterize living systems and enable them to choose appropriate (or not) actions in context, driving evolution toward increased functional effectiveness. The A-PR capacity occurs in iterative learning, self-improving systems and is the most powerful way that artificial systems can mimic life, which discriminates on its own behalf, bootstrapping itself toward improved functionality by its choices.

References
Buss, Leo. 1997. The Evolution of Individuality. Princeton University Press.
Gill, Zann S (1986) The Paradox of Prediction. Daedalus: Journal of the American Academy of Arts and Sciences 115(3): 17-49.
Gill, Zann (2012) User-Driven Collaborative Intelligence: Social Networks as Crowdsourcing Ecosystems. ACM CHI (Computer Human Interaction). May 5 – 10, 2012. Austin Texas.
Mayr, Ernst. Toward a New Philosophy of Biology: Observations of an Evolutionist. Cambridge, MA: The Belknap Press. 1988. p. 224 – 225.
Menand, Louis. 2001. The Metaphysical Club New York: Farrar, Straus and Giroux. 123–124.
Sherrington, Sir Charles. 1951[1937-8]. Man on His Nature. Gifford Lectures. Cambridge University Press. 7.
Snelson, Kenneth. Discovery of Tensegrity Structures. See Heartney, Eleanor. Kenneth Snelson: Forces Made Visible.
Suksi, Markku. 1998. Autonomy: applications and implications. Kluwer Law International.
Tensegrity Structures in Engineering.

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