Matrix of Design, Why Some Computer Scientists See What Other Scientists Can’t
Posted by pwl on March 29, 2014
The unseen world of science that some computer scientists have an advantage seeing the objective reality of Nature through the Matrix of Design.
“Two important characteristics of maps should be noticed. A map is not the territory it represents, but, if correct, it has a similar structure to the territory, which accounts for its usefulness.” – Alfred Korzybski
It must be noted that Information Science is at the very heart of the objective reality of Nature, the fabric of spacetime itself distinguishes information into discrete quantum packets of particles and components of energy at the smallest level of Plank time and length (Plank Spacetime). Existence would not exist without distinction of information, this from that, that from this, here from there, there from here, now from then, etc… in a (seemingly) never ending continuum and volume.
In a very real way information science is more fundamental than even physics. Without information existence would not exist. Is it even possible to have existence without information? Only in a singularity of the pre-big bang instant when there is nothing, I’d assert.
“There is a considerable difference between a mathematician’s view of the world and a computer scientist’s. To a mathematician all structures are static: they have always been and will always be; the only time dependence is that we just have not discovered them all yet. The computer scientist is concerned with (and fascinated by) the continuous creation, combination, separation and destruction of structures: time is of the essence.
In the hands of a mathematician, the Peano axioms create the integers without reference to time, but if a computer scientist uses them to implement integer addition, he finds they describe a very slow process, which is why he will be looking for a more efficient approach.
In this respect the computer scientist has more in common with the physicist and the chemist; like them, he cannot do without a solid basis in several branches of applied mathematics, but, like them, he is willing (and often virtually obliged)
to take on faith[“to hold as contingent knowledge”] certain theorems handed to him by the mathematician.
Without the rigor of mathematics all science would collapse, but not all inhabitants of a building need to know all the spars and girders that keep it upright.
Factoring out certain detailed knowledge to specialists reduces the intellectual complexity of a task, which is one of the things computer science is about.” – Parsing Techniques, 2008, Grune, Jacobs, page 2.
I disagree about their usage of “take on faith” as that isn’t a word (or phrase) that should be used in science and isn’t accurate anyhow. Trust is a not that much better.
What I would say is I as a computer scientist and a scientist in general take the “claims” or “assertions” by others, mathematicians, physicists, chemists, engineers, other computer scientists, etc… as “contingent knowledge that I hold as possibly true” yet that hasn’t been validated or refuted but is merely claimed by the other(s) and is open to verification or refutation should the need arise, and you know sometimes it does arise.
Often the assertions prove to be accurate or accurate enough for the purposes of the software being created, other times I or colleges have falsified claims of other computer scientists or mathematicians or even of others in other science, engineering and technology fields.
That is the beauty of computers, they have a harsh reality that lets one discover the empirical facts about how things work and don’t work. It’s one of the main attractions to software for me, it’s malleable yet it presents a requirement that you get whatever it is that you’re modeling (business, engineering, finance, data, calculations, math, physics, chemistry, …) correct and accurate enough for that purpose. Sometimes you have to dig and during those times you can find the mistakes of others or you can improve upon their “assertions of claims”.
There is also a way of modeling that some computer scientists do that can provide insights into other fields in ways that those in those fields can’t see.
The Matrix of Design isn’t a skill that many computer scientists have. It takes more than coding skill or math skills. It takes the ability to construct a model, see it, see it and it’s parts move in space and time, to wonder how connections impact it, how we the observers impact it, how the data it represents interacts in the very real world of the binary wonder of actual computers.
The Modeling of Design constructing a Matrix of Design is not a skill taught in science, though a few scientists also end up being computer scientists.
It’s difficult to teach. Part intuition. Part communication. Part imagination. Part coding. Part reality construction. Part playing as if one is one of those creatures from mythology, the alleged gods. Being the creator of a system of a Matrix Designed. Not forgotten are debugging skills to root out the flaws in the notions of ideas, in the execution of modeled realities, in the specifications of theories claimed, expertise few other gain in their careers.
The Matrix of Design enables vision that distinguishes the Matrix of Information of the Virtual World as well as the harsh objective reality of Nature and her ways of wonder like no others.
This flexibility gives us the capability to view multiple representations of the reality being modeled, whether that is a simple model or a complex model of the real world.
We are also very aware of the limitations of models. Scientific hypotheses are models of one sort or another, most often with classical math such as in physics that is theorizing a math model of an aspect of Nature (e=mc^2). With the rise of A New Kind Of Science by Stephen Wolfram evidence based Cellular Automata that can represent realities that classical math can’t have become possible. There are many other ways to represent objective reality other than these two mentioned.
This awareness of the limitations of the various kinds of models enables us to see flaws in scientific hypotheses (at times) as well. Certainly we find many flaws in the models constructed by scientists who lack the computing Matrix of Design skills distinguished here.
The Matrix Of Design producing Organic Physical Realities.
Some computer scientists are incredible at holding multi-view points and on possible theories and possible designs. That is one distinguishing capability. Much like Einstein’s ability to visualize physics computer scientists that have this ability excel at the Matrix Of Design. We switch between possible hypotheses for the Matrix of Design on a dime often entertaining many at the same time.
It is our ability to hold things as contingent knowledge, as a contingent model, that enables us to hold scientific hypotheses as contingent and to have “emotional separation” from them which is crucial in science otherwise it becomes less scientific. The ability to test one’s model aka hypothesis against the objective reality of the computer teaches us to be connected to the scientific method in daily professional practice. If we don’t get it right we know because the harsh objective reality of the computer informs us. This carries over to other fields when we work with subject matter experts or domain specialists, or when we study and learn those fields ourselves.
Another capability is the importance of communication and being able to learn quickly and ask questions to elicit detailed knowledge about the subject matter being represented and modeled. This is excellent training for science as at the core of progress in science is using the scientific method to discover the facts of the objective reality of Nature and to communicate this to others using the scientific method as the verification or falsification of knowledge.