Eye of the Storm Leadership
150 Ideas, Stories, Quotes, and Excercises On The
Art and Politics of Managing Human Conflicts
by Peter Adler, Ph.D.
THE BOOK & VIDEO > 10. Joint Fact Finding
 

X. Joint Fact Finding

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Starting Point / Termites and Submarines

Hurricane Isabel 3-D Structure. Credit: Vincent J. Realmuto, NASA/JPL.
Pierre-Paul Grasse is an entomologist who coined the term “stigmergy.” It means “incite to work.” With a keen interest in ants and termites, Grasse wondered how social insects managed to build sophisticated architectural structures filled with specialized tunnels, passageways, ventilation systems, and food chambers. He observed that when three or four termites were put together in a contained chamber, they wandered around aimlessly. When more were added and some unknown critical mass was achieved, they began to build. Once they reached sufficient numbers, termites picked up each other’s fecal pellets, stacked them in neat columns, and constructed perfect arches to form the foundations of a new mound.

Stigmergy is not very well understood. Somehow information is exchanged, patterns are recognized, and action ensues. It is the same when people are faced with big problems. An actual or impending crisis seems to mobilize. It goads people to get organized, provokes them to find innovative ways to work together, and spurs them to generate new and usable information. Information, both objective and subjective, helps people understand the mess. A base of salient facts points toward possibly acceptable solutions.

Image:Nest of Formosan subterranean termites.jpgFormosan Subterranean Termites. Credit: Wikimedia Commons.
John Craven is a veteran of World War II, a lawyer and law professor, a marine engineer, and a former Chief Scientist of the U.S. Navy’s Special Projects Office. He is also an “inciter to work” and has also been an “incitee.” In the aftermath of the loss of the nuclear attack submarine Scorpion in 1968, Craven was charged in no uncertain terms by Admiral Hyman Rickover with finding it. The Scorpion had disappeared in the North Atlantic on its way back to Norfolk after a tour in the Azores. This was the Cold War era. The Navy needed to know if it had malfunctioned or had been sunk by the Russians and whether its sophisticated weapons and sonar were still intact.

The Navy knew the ship’s location within a twenty-mile radius. Two listening posts had recorded a series of explosions, but the data did not actually pinpoint where the sub had gone missing. A careful examination of the explosion patterns led Craven to believe the Scorpion had actually been traveling east, not west as was assumed. If true, the wrong area was being searched. Using a version of Bayesian analysis, Craven assembled a team of experts, pooled all available hypotheses, and asked each expert to make his best guess in the form of a wager with the winner getting a bottle of fine scotch.

Then he built a probability grid. The grid was a merger of two kinds of guesses: the probability of the sub being at a certain location and the probability that the sub would actually be found at a specific depth and topography. Five months after its disappearance and a week after the search had turned to the area predicted by Craven, the remains of the Scorpion sub were located 220 yards from the Craven group’s best amalgamated guess.[i]

Image:HMCS VICTORIA.jpgInternal Structure of the Submarine HMCS Victoria. Credit: Wikimedia Commons.
The technical analysis that Craven and his team did had its roots in the work of Thomas Bayes, an eighteenth century Presbyterian minister and mathematician who was keenly interested in probabilities. Bayes developed an early theory of inference that could potentially help make predictions of future events. Bayes proposed his theory in 1763 and then published it two years later in An Essay Towards Solving a Problem in the Doctrine of Chances.

A Bayesian approach to problem solving encourages you to combine new data with existing knowledge, talent, and expertise. In technical terms, it creates statistical inferences in which probabilities are interpreted not as frequencies or proportions, but as degrees of belief. It relies on and combines theory, data, hunches, intuition, and probability. Think of it as “systematic intuition.”

Bayes’ rule would allow us, if we chose to do it, to gather together a small group of people with interest in, passion for, and knowledge about termites, pool our information, improve the definition of the problem we are working on, and predict with a high degree of accuracy just how many termites are needed to build a well-functioning termite colony. Or, we could use this as part of a well designed process to zero in on viable solutions to tough social issues like reducing green house gasses in the atmosphere, improving health care, crafting better food labels, and improving agricultural production in places where people are hungry.

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91    Information[ii]

“I want everyone to be smart. As smart as they can be. A world of ignorant people is dangerous to live in.”  Carson Kanin

Image:Circle-information.svgInternational Symbol for Information. Credit: Wikimedia Commons.

In the perennially politicized world of education policy, everyone puts the blame for declining test scores and poor graduation rates on someone else. Colleges blame high schools, high schools blame middle schools, middle schools blame primary schools, primary schools blame parents, and parents blame politicians. Conflict is laced with hard choices that challenge the values held in the heart and the choices framed by the mind. To work effectively with polarized political choices, the problem must be imbued with new information. Information is a central lever of change. By itself, data never takes you to an acceptable conclusion, but without good information, no conclusion is really acceptable over the long run. The well-organized information exchange, the creative fact-finding procedure, or the jointly-commissioned study creates a pathway to wiser decisions. Done wrong, prepare yourself for expert wars, competing methodologies, fights over modeling, and findings that cannot be verified. Done right, you will see the birth of potential solutions.

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92    Native Intelligence

“Information is light. Information, in itself, about anything, is light.”  Tom Stoppard

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