“The smartest person in the room is the room itself: the network that joins the people and ideas in the room, and connects to those outside of it” (Weinberger, 2011).
PLEASE NOTE: This is a draft of a bit of the Open Scientist Handbook. There are references/links to other parts of this work-in-progress that do not link here in this blog. Sorry. But you can also see what the Handbook will be offering soon.
Goldman and Gabriel (2005) penned to phrase: “Innovation happens elsewhere” to capture the value of open-source software communities. In the academy, it doesn’t matter if you are at Oxford or in Oxnard, almost everything you need to know to make the next step in your research is also being considered at this moment: somewhere else.
In the academy, this “beyond” is a global intellectual commons now becoming abundant with open data and accessible — and reproducible (Crick, et al, 2015) — research results. Using online peer production methods (Benkler, 2016), the academy can optimize the value of this commons for innovation, knowledge, and growth. “[P]eer production practices [are] highly adept at learning and experimentation, innovation, and adaptation in rapidly changing, persistently uncertain and complex environments” (ibid).
The only competition your academy organization has is within itself. As other institutions — including new virtual science organizations — work to continuously improve on their work, your team needs to focus on leveraging the learning engine of double-loop governance to get better than your yesterday. In the infinite game of science, winning means accelerating your team’s learning and sharing capacity through what (Hagel and Brown, 2011) call a “creation net” for open innovation. Standing still is not an option when the research world is exploding somewhere else. This “explosion of creativity is taking over more and more of our world. Everyone involved in it is at the same time a producer and a consumer, a worker and a manager…. Progress in most academic disciplines now seems to move at the speed of ‘instantaneous,’ with discoveries building atop one another at a dizzying pace” (Ito and Howe, 2016).
Creation networks: open science’s network effect
A creation network is enabled by a certain quality of learning within social interactions, a greater quantity of information flows (and/or a greater attention to these), an availability of interpersonal trust (based on demonstrated skills and commitment), and an environment of reflexive involvement: all benefits of belonging to a community-led double-loop governance. “[I]nstitutions will need to become much more selective in their efforts to protect existing stocks of knowledge and much more adept in using their stocks of knowledge to contribute more actively in creation nets and to plug into promising flows of knowledge” (Hagel and Brown, 2008). Data-intensive science (Hey, et al, 2009) in a whitewater world of global research demands a nimble governance for its teams, labs, networks, societies, universities, and agencies.
Know enough to know enough
When your academy organization looks to innovate — or when your personal research is looking to find the right question to ask — in a world where multiple/large data/information inputs, and international science discoveries are coming on line, how can you stay ahead of this emergent complexity? One way to look at this problem is through Ashby’s principle/law of requisite variety, coming from cybernetic management. Ashby’s law notes that unless the control system has at least the variety of the environment it controls, it will fail; which actually means that some part of the environment will be controlled elsewhere.
You need to join the science elsewhere. Elsewhere is where other science teams are now playing the infinite game in collaboration, asking the questions that their networked teamwork generates. Elsewhere there are flows of information being shared across the planet. That is a great reason for new creation networks in the academy: for open science sharing across the academy.
Elsewhere is where innovation happens; because unless you can corral the inherent variety of the problem you face, it will be too complex for your team to innovate a response. If you are not engaged with the open-science elsewhere that is opening up today, your team will suffer. You can either go out and hire a bigger team (good luck talking your chancellor or the NSF into that), or you can borrow enough requisite variety just long enough to bring your own team up to speed by starting up or hooking into an online creation network. You can join the sharing economy, play the infinite game, and get better at it every day. Or you can rest on your (bullshit) reputation and keep on thinking the world will come to you.
When members are given license to form working teams across organizations, they also expand the extent of where their research adjacent possible is found; creative interactions and new knowledge become predictable outcomes. The larger the room, the smarter it gets. Find the room to nurture your research.
When the adjacent possible is a globally available
The “adjacent possible” is a notion that comes from biological theories of coherent change. It describes how the surrounding environment tucked between stasis and chaos provides a resource of available change. The adjacent possible enables, and almost guarantees, certain changes (while ruling out others) out of potentially infinite play of innovation.
“Biospheres, on average, may enter their adjacent possible as rapidly as they can sustain; so too may econospheres. … [T]he hoped-for fourth law of thermodynamics for such self-constructing systems will be that they tend to maximize their dimensionality, the number of types of events that can happen next” (Kauffman, 2000). Every new piece of information, each new proto-fact, expands the horizon of the infinite game of science (See: Learning to play the infinite game). The more scientists that add this new fact to their knowledge, the larger their mutual adjacent possible becomes.
Steven Johnson [Retrieved 1/15/2020], uses the metaphor of “liquid” to describe the optimal network environment to enable innovation (Johnson, 2011). “Solid” networks are too stiff to pivot toward “the adjacent possible” where new ideas sprout. “Gas” networks are too chaotic. “In a solid, the opposite happens: the patterns have stability, but they are incapable of change. But a liquid network creates a more promising environment for the system to explore the adjacent possible.” (Ibid).
More specifically, liquid networks — and the academy organizations that create these — enable individual researchers and teams to explore the adjacent possible; “When the first market towns emerged in Italy, they didn’t magically create some higher-level group consciousness. They simply widened the pool of minds that could come up with and share good ideas. This is not the wisdom of the crowd, but the wisdom of someone in the crowd. It’s not that the network itself is smart; it’s that the individuals get smarter because they’re connected to the network.” (Ibid). The room makes everyone smarter; these new everyones make the room smarter. You need to find/build that room. When you do, you use demand sharing to pull the information and knowledge you need right now to move ahead in your research (See: Demand sharing and the power of pull).
The liquid network is another way of talking about network diversity, the optimal mix of strong ties, weak ties, and strangers in direct communication (See: Ruef, 2002) that is a key predictor for innovation in the global elsewhere your research can call home. How do you get this home? The most reliable starting place is to build a culture of organizational learning into your organization. Double-loop governance is a durable platform on which to develop liquid networks across the academy, or in your lab or your department, and at your learned society.
Benkler, Yochai. “Peer Production and Cooperation.” Handbook on the Economics of the Internet 91 (2016).
Crick, Tom, Benjamin A Hall, and Samin Ishtiaq. “Reproducibility in Research: Systems, Infrastructure, Culture.” ArXiv Preprint ArXiv:1503.02388, 2015.
Goldman, Ron, and Richard P. Gabriel. Innovation Happens Elsewhere: Open Source as Business Strategy. Morgan Kaufmann, 2005.
Hagel, John, and John Seely Brown. “Creation Nets: Harnessing The Potential Of Open Innovation.” Journal of Service Science 1, no. 2 (2008): 27–40.
Hey, Anthony J. G., ed. The Fourth Paradigm: Data-Intensive Scientific Discovery. Redmond, Washington: Microsoft Research, 2009.
Ito, Joi, and Jeff Howe. Whiplash: How to Survive Our Faster Future. Grand Central Publishing, 2016.
Kauffman, Stuart. At Home in the Universe The Search for the Laws of Self-Organization and Complexity. Cary: Oxford University Press, USA, 2014.
Ruef, Martin. “Strong Ties, Weak Ties, and Islands: Structural and Cultural Predictors of Organizational Innovation.” Industrial and Corporate Change 11, no. 3 (2002): 427–449.
Weinberger, David. Too Big to Know: Rethinking Knowledge Now That the Facts Aren’t the Facts, Experts Are Everywhere, and the Smartest Person in the Room Is the Room. Basic Books, 2011.