Okay, pardon this passionate outburst but I want to reaffirm something — something I’ve banged on about ad nauseam for the past couple of years: the absolutely indispensable influences generative capacity and collaboration will play in our future.
An article I devoured earlier this morning confirms why these two concepts will likely provide the standard on which public and private entities alike will rise and fall within the 21st century knowledge economy.
Oh, and pardon the unwieldy term “generative capacity.” I simply can’t come up with anything that better describes what will likely be one of the two principal preoccupations for the foreseeable future. I owe Steven Johnson for this term.
Simply put, the massive sharing and social collaboration that has accompanied networking has enabled all forms of thinking, formal and informal alike, to be generated at vastly accelerated volumes.
Much like the 15th century Gutenberg Press, networking is changing all facets of how we develop and share knowledge. Even science, the principal source of refined, formal knowledge, is proving to be no exception.
A couple of years ago, Cambridge University Tim Gowers engineered a remarkable demonstration of the significance of generative capacity to scientific inquiry when he used his personal blog to solicit the help of people around the world in solving a highly complicated mathematical problem.
His effort, cleverly dubbed the Polymath Project, proceeded on the relatively straightforward premise that online tools can be used to enlist disparate brains into a temporary but greatly enhanced cognitive intelligence.
Within weeks Gowers’s problem was solved as mathematicians from sundry perspectives and with varying levels of expertise weighed in with insights.
Granted, not all of Gowers’s collaborative efforts have met with similar success, but his efforts have been successful enough to lead a number of observers to conclude that this networked approach to problem solving represents the future of science.
As the title of an Oct. 29 Wall Street Journal article aptly observed, “The new Einsteins Will Be Scientists Who Share” — or, in other words, collaborate.
In fact, that rather clever title underscores how these two factors, generative capacity and collaboration, will be inextricably linked in the future. Borrowing the lyrics from that beloved Sinatra classic, “Love and Marriage,” what unfolds over the next few decades will only underscore that “you can’t have one without the other.”
Collaboration is the critical guarantee of generativeness (again, excuse my digression from standard English). They work hand in hand. Optimal generative capacity can only be ensured within open, fluid networks, which are secured only through optimal levels of collaboration. One of the principal preoccupation of all knowledge providers in the future will be building fluid learning environments — platforms as I prefer to call them — that strive to secure the highest levels of collaboration and generative capacity.
For what it’s worth, I’m personally convinced that science will prove no exception. Yes, there is resistance. Proprietorship has been a defining characteristic of science for the last three centuries. It will take years to divest scientists of the increasingly antiquated notion that writing for professionally refereed journal articles is more valuable to the future of human progress than open sharing of knowledge within extended networks.
Even so, the advent of a new, open and networked scientific model that ensures the fullest measure of generative capacity by securing optimal levels of collaboration is inevitable. As the WSJ article stresses, the immense potential of “discoveries not yet dreamt of” is simply too valuable to ignore.
Generative capacity lies at the heart of this immense potential, and as growing number of scientists will learn, it will only be secured through maximum levels of collaboration.