This review of a Knol article has evaporated together with Knol itself
but is adapted here below as a self-standing article.
We can see that the world is complex, fast moving, with many soft and
Thus, what research, facts, knowledge and interpretations can we
call "scientific" ?
Without being too restrictive and therefore missing many aspects ?
What is science?
Science is about knowledge, of course,
But what kind of knowledge?
Just bodies of knowledge that became widely accepted?
Is it enough to consider current paradigms as definitive certainties ?
Science is also about formal interpretations (theories, laws)
of facts that gives hints (if not full certainty) about what can happen
in similar cases.And to find those facts and bring interpretations
=> OK then,
* what kind of formalized knowledge,
* what serious research of facts
* and what consistent interpretations
can be called scientific?
Here come the human factor.
To what extent personal experiences (and creations), clearly
formalized (assumptions), bring enough evidences to be called research,
if not science?
It might be less feasible in some areas in which there are :
* more complexity and change than in others,
* more "soft" (= human) aspects.
Or on the contrary more feasible?
Can a perfect knowledge of the elements of a system, and even
of how they interact, be enough to explain a dynamic "emergent" system ?
Or are not only the components but also the properties of the
system itself what explains the evolutions it underwent, such as
self-organization and emergence?
More generally, uncertainty is one of the famous (and
...certain ?) "laws" of the universe.
Here therefore hot debates surge, in which we have to choose between
an open and a reductive position
I will give my two cents by saying that we need an open mind and should
cover the whole range of scientific areas, and more practically that two
types of formalized knowledge have to be accepted as science.
The two types of sciences
Hard to make categories within something as multifaced as science
But as a rough approach we might say that what we define as formalized
knowledge, coming from consistent research and interpretation, covers
two main "families:
* HARD sciences, applied to the physical universe.
This is an area of stable and predictive models (at least in the form
of probabilities), in which the theorized phenomena repeat identically under precise conditions. Many of those models use mathematical or at
least logical laws.
In this area Aristotle's logic is supposed to apply.
With (binary and debatable) postulates such as:
"one cause, one effect",
"things can only be true or false"...
But even here, hard logic can be restrictive (see fuzzy logic), as things are
not fully objective, clear-cut, stable and predictive. For example:
Many theories and even common paradigms
are still theories, meaning not fully proven.
About the facts themselves, they are still grey zones
between white and black, between their existence and their
The "laws" work only under some strict conditions,
and even so, various interferences can thwart them.
Bifurcations and chaos are all over the place (hello, butterfly!).
And we are still far from finding the famous "string theory"
that would cover all universal phenomena.
* SOFT sciences, related to human beings and human societies
Here it is harder to find permanent laws / stable equilibrium
models / objective probabilities. But,
* Although there is more uncertainty, and although Aristotle' binary logic
is in check (with many more grey / ambiguous zones of half-truths / half falsehoods, if only because human / social nature is even more
complex and evoluting than the physical nature ,
* Observation stays feasible and useful, subjective approaches
are helpful and they cannot be demonized as "unscientific".
All the more if their applications show their unefulness
What would be unscientific would be to condemn research and the quest
for (flexible) explanation theories on those subjects under the pretense
that they are ambiguous and/or evolving.
The full extent of science
...and its vulnerability
I propose that the broadest definition of science, as given above, get used
in educational articles.
But we must recognize that all knowledge and sciences, even the hardest
ones have weaknesses, as new paradigms can put to test some old ones.
Knowledge in general has to be learned but often also delearned.
As for the relation between science and innovation, a specific article
shows them, as applied science is a powerful source of innovative initiatives,
but not the only one.