There's a long tradition of ethnography whereby anthropologists document and analyze work practices. Collaborative intelligence starts from a different perspective, focusing on innovation and on the cross-disciplinary problem to be solved, structuring the framework for problem-solving.

Gerd Gigerenzer, Director of the Center for Adaptive Behavior and Cognition at the Max Planck Institute for Human Development in Berlin, has developed a method he calls ecological rationality, a platform for collaborative intelligence. He describes how we need capacity to journey into unknown territory, a land of rationality that is different from the one we know.

Inspired by AI pioneer Herb Simon's concept of "satisficing," Gigerenzer proposes that we need

• heuristic principles for guiding search;
• heuristic principles for stopping search; and
• heuristic principles for decision-making.

Gigerenzer studies behavioral intuition and is one of the researchers responsible for the science behind Malcolm Gladwell's bestseller Blink. Gladwell showed how snap decisions often yield better results than careful analysis. Gigerenzer examines why intuition is such a powerful decision-making tool. Drawing on a decade of research, Gigerenzer demonstrates that gut feelings are the result of unconscious mental processes that apply rules of thumb that we've derived from our environment and experiences. The value of these rules lies precisely in their difference from rational analysis — they take into account only the most useful bits of information rather than attempting to evaluate all possible factors. By examining various decisions we make, Gigerenzer shows how gut feelings not only lead to good practical decisions, but also underlie the moral choices that make our society function. [from publisher description]

Just as the work of Gladwell sits on top of the middleware research platform of Gigerenzer, Gigerenzer's research sits on top the the foundational research of neuroscientists such as Antonio Demasio, whose book Descartes' Error brought the importance of emotion and intuition for so-called wise "rational" decision-making to public attention. Through studies of brain-damaged patients, who had lost emotional capacity, Demasio concluded that there is a strong connection between the ability to make wise decisions and capacity to feel emotion, which fuels the quick blink intuitive reaction that Gladwell described. Review by biologist Paul Grobstein, who has focused on the mind-body (brain) problem.

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Practical Applications.

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.

References
Gigerenzer, G. (1980). Messung und Modellbildung in der Psychologie. München ; Basel, E. Reinhardt.

Gigerenzer, G. (1989). The Empire of chance : how probability changed science and everyday life. Cambridge England ; New York, Cambridge University Press.

Gigerenzer, G. (2000). Adaptive thinking : rationality in the real world. New York, Oxford University Press.

Gigerenzer, G. (2002). Calculated risks : how to know when numbers deceive you. New York, Simon & Schuster.

Gigerenzer, G. (2007). Gut feelings : the intelligence of the unconscious. New York, Viking.

Gigerenzer, G. (2008). Rationality for mortals : how people cope with uncertainty. Oxford ; New York, Oxford University Press.

Gigerenzer, G. (2010). Rationality for mortals : how people cope with uncertainty. New York ; Oxford, Oxford University Press.

Gigerenzer, G. and C. Engel (2006). Heuristics and the law. Cambridge, MA, MIT Press in cooperation with Dahlem University Press.

Gigerenzer, G. and J. A. M. Gray (2011). Better doctors, better patients, better decisions : envisioning health care 2020. Cambridge, MA, MIT Press.

Gigerenzer, G., R. Hertwig, et al. (2010). Heuristics : the foundations of adaptive behavior. New York, NY, Oxford University Press.

Gigerenzer, G. and D. J. Murray (1987). Cognition as intuitive statistics. Hillsdale, N.J., L. Erlbaum Associates.

Gigerenzer, G. and R. Selten (2001). Bounded rationality : the adaptive toolbox. Cambridge, Mass., MIT Press.

Gigerenzer, G., P. M. Todd, et al. (1999). Simple heuristics that make us smart. New York, Oxford University Press.

Kurz-Milcke, E. and G. Gigerenzer (2004). Experts in science and society. New York, Kluwer Academic/Plenum Publishers.

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Image Credit. Andrew Wunsche
Left. Random Boolean Networks

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