Is Fluidity on the Horizon?
Written with Miroslaw Manicki
Whether considering infectious transmissions or climate cycles, knowledge and authority come together. If recent experience serves, they collide. We need to not only listen to the experts, we need to empower them in their areas of respective knowledge. Indeed, this can be done. If it isn’t, we are due for ongoing disappointments.
The world cloud above outlining a stream winding its way between pine trees is an interesting snapshot of what is on the minds of leading thinkers in business, marketing, and economics. What I see here is a people wanting to break free. They want to be proactive and to progress, but they are aware of health limitations in the form of Covid-19 and the pandemic, which are obviously on their minds. These are constraints to them.
Indeed, they represent the unknown. It also reflects the rough, jagged edges of knowledge gain, as the smartest among us are obviously learning in full public view. This opens the door for pernicious snipping at the knowledge combine. This represents little more than howling at the moon, but it does take its toll socially and politically.
In 1993, when I came up with the ideas of dual control and fluidity, no one really cared about such matters. I encountered a computerized model based on trees that potentially eliminated the barrier between people with knowledge, but without expertise in computerizing that knowledge. Do people care now? There is evidence that this is so.
This question of knowledge is not a simplistic matter. There are different kinds of knowledge. There are different kinds of experts. Knowledge itself can take different forms and serve different uses. Underlying it all are the questions of method and methodology, leading to understanding the two critical questions where something must me known: (1) Is the proposition valid — does it demonstrate internal and external validity? This means that does it apply in one situation first, then, whether it applies to other situations? (2) Is the proposition reliable? This involves data, how it is measure and treated — involving both numbers and symbols.
This isn’t what scientists and other knowledge workers talk to lay people about, it is what they talk to each other about — and communicate via documents, as can be seen in the following table — the Tacit, Explicit, Expressive Model. Currently, science functions in the four white categories. They talk among themselves — exercising their tacit knowledge. They document these in explicit forms — the mountains of documents, pages, that describe these conditions in static ways. Even scientists cannot reliably jump into such conversations or understanding related documentation. The concepts will be unknown to them; there will be mountains of studies and findings that will take considerable time to come to understand. Often this requires specialized and highly complex physics, mathematics, and chemistry interactions. There will be methods and models that are complex and nuanced that will require time to begin to understand.
Very few scientists have the capacity to “program” such knowledge in active, process-oriented forms, which makes all the difference. For one thing, typically programming languages and tools based on “if-then” structures do not support such functions effectively. As a result, much of the richness and nuance of the original science is lost in the explanation. This skews the balance among knowledge workers. They are not all created equal and they suffer from the lack of ability to directly convert their knowledge from tacit and explicit forms to expressive forms as outlined in the table.
One huge problem is that the management of scientific knowledge is too social. This is to say that social interactions in tacit and explicit realms can serve to pollute the process of sorting, integrating, and applying scientific knowledge in societal contexts. For example, it is only natural for scientists to describe what they know using metaphors. This is when they say (using tacit knowledge shared by non-scientists) “[this situation] is like [that situation]”. An example might be that “a virus spreads like a seed in the wind”. Well, that might be helpful to some degree, but that fact is that viruses do not spread like seeds in the wind. There are enormous differences in scale alone. As a result, misunderstanding and misapplication is inevitable.
If a person takes what he or she thinks that they know from a metaphor of this kind based on non-scientific knowledge, meaning that they do not have a profound and nuanced understanding of the validity and reliability of current knowledge, they are virtually sure to get it wrong. Getting caught on the vortex of simplistic, but wrong and misleading metaphors is responsible for confusion in public, also among scientists, practitioners, policy advisors, and administrators.
There are fundamentally three kinds of scientists in this process. It is important and useful to understand these and how they interact with one another. I have written three short booklets with some collaborators in these areas — describing specialists, generalists, and journeyman scientists who direct their attention at new areas of science to fill a void.
Specialists are what most people think of when they think of scientists. These are the white coat people, mostly, who have laboratories of one kind or another. This needn’t be a wet lab or a chemistry table. There are many forms of study. They make a deal with society. They focus their efforts wholeheartedly on singular subject areas which may or may not pan out. Society takes care of them.
As these people are committed to such singular issues, they live in the literatures in question. They come know each other if not in tacit terms, certainly in explicit terms — and those that came before in those fields and in the underlying science in question. They must make lifetime commitments to gain traction in profound and complex areas of study. When given the chance, they will almost always oversell their science and its implications. This isn’t a criticism, it is a matter of human nature — particularly when they have to battle for financial and other forms of support for their work.
Generalists are scientists that can understand and prioritize the work of specialist teams and communities. If specialists deal with trees, generalists manage forests (a metaphor, to be sure, with appropriate cautions). There are not so many generalists as we need. They need to be supported and encouraged. Prominent specialists, for example, question the need fo and the skills of generalists, who they might consider to be ‘sell-outs’ to the degree they doffed the lab coasts. Generalists would benefit greatly from fluidity, from the ability to organize and integrate knowledge-based processes using technology.
There are many pseudo-generalists with systems knowledge, but little understanding of or commitment to the actual science. They can thus very easily whitewash over legitimate knowledge and science with ‘sciency’ content that is misleading and wrong. This is a really big problem an a major reason that the scientific community lacks credibility. With a technology barrier that generalists cannot overcome given their commitment to doing science, not programming computers, society suffers even when knowledge to resolve problems exists. It does little good when the end goal is a document on a hard drive or a dusty shelf.
We use a sports metaphor — that of a utility or journeyman athlete that adapts his or her role to meet the needs of the team. Admittedly, that metaphor isn’t perfect, as indicated before, but it is better than most. This is a common path for young scientists. Indeed, such breadth is established as an important aspect of scientific preparation.
This kind of scientist is critical to success. Some personalities favor this kind of work, migrating from new area to new area as a career choice. Others burrow into the subject in question to become specialists, often carving out the discipline in the process. Others become generalists, particularly as they have broadened their understanding when working in different fields, on different kinds of problems.
Key to these functions is the question of gatekeepers. These are scientists that have the last word — that speak to the public, that create products, and that employ marketing and distribution strategies. Ideally, these are the generalists. Society depends on these. An example of an important generalist is the famous Anthony Fauci. He, among other generalists, has specialist credentials. It is in his generalist capacity that he is known to the public.
It is insufficient to take in the metaphors of such people. They should be seen as the tip of the spear. In some cases, where fluidity exists, they may literally be judges of last resort, merging processes in a policy framework with authorities and policy leaders.
What of a supposed generalist that leverages his or her facility with metaphors and persuasion to mislead policymakers and the public? It doesn’t matter if he or she has degrees and credentials, if not steeped in the gnarly issues of validity and reliability in the particular issue at hand, misunderstanding and misdirection are inevitable.
One thing of interest in the word cloud above is the prominence of the word data. There is also some prominence with regard to technology. As to knowledge — nothing of that kind surfaces. Without success in the management of knowledge to match authority and will, achieving the other conditions as they foresee them is little more than a pipe dream. This involves attention to the needs of specialists, generalists, and journeyman scientists — particularly with respect to computerizing the processes that represent validity and reliability of their know-how. Their knowledge must flow when and where we need it.