Wednesday, March 01, 2006
Generative Methodology Glass Bead Games
On the limits of the OWL standard ŕ 
Reading material 
Reading material 
Reading material 
Summary of the discussion up to this point ŕ 
On Formal verses Natural systems
Related discussion on ontologyMapping thread ŕ 
On Erick’s comment on the same subject ŕ 
Communication from Judith Rosen
The way to verify anything is by testing the predictions of our models against the natural world: by testing our inferential entailments against the causal entailments we encoded from, to see if we have our inferences correct.
The problem in science is that even when a contradictory situation develops, the "tacit presuppositions" are almost never questioned if they have already "proved themselves" in the past.
What my father's work demonstrates is that
1) all models of complex systems cannot be anything other than partial encodings of the actual entailment of the system;
2) therefore, any model of a complex system will be able to predict accurately under some circumstances and will be unreliable beyond those circumstances;
3) most of contemporary science is based on models of complex systems which are mistaken for-- tacitly presumed to be--simple (albeit complicated) systems.
His conclusion is that the problem is in the tacit presumption of simplicity (and the confusion of complicatedness with complexity). There are myriad ways to work around the findings of numbers 1 and 2 and he suggested a great number of them.
But we must never forget that we are working from models and measurements and that all of our a' priori knowledge will depend entirely on the "if" part of the proposition. In other words, our models are context-dependent, just like everything else in this universe. If we just apply those models, regardless of context, there will be some contexts in which the models are incorrectly encoded. Does that make sense?
The entire body of work (of Robert Rosen) represents: an expanded theory on which to base scientific knowledge.
He felt exactly the same way you do: that empirical science is a necessary part of the entire enterprise but that it must be guided by an underlying theory that allows us to gain actual knowledge about the natural world. Otherwise, we will think we know something when we actually don't and when we make decisions based on what we think we know, and implement them in the natural world.... we generate "side effects". You see?
What I have been attempting to do in these discussions is show you the difference between our <culture’s> current "theory on which science is based" and the expanded version my father developed, along with the reasons why the current one is inadequate, in and of itself.
My hope is that you will be able to avoid the kinds of problems that arise when the tacit presuppositions of others before you are accepted as "truth" and plugged into your own work. That's all. I suspect that this is why Paul Prueitt contacted me in the first place and asked me to participate, here.
The thing is, you are developing new science. John Sowa is also developing new science. These are frontiers. However, all the foundations of contemporary science have incorporated certain "tacit presuppositions" which are causing a great deal of trouble in human applications of science currently, and those same encodings will cause you trouble, down the line, as well.
We could say that this is an entailment pattern, generated by the applications of science based on too simplistic a model.
My father's prediction of what will happen in any new applications, such as new science in information technologies, only holds as long as the model used is the one currently accepted: e.g.. that all systems in the universe are "like machines".
I restate the Rosen view that the main difference between simple systems and complex systems is the impact of relations within system organization.
My father’s work suggests the necessary theoretical everyday reasoning machinery by which science can be done.
There is no need to discard what we have, there is only the requirement to discard the machine metaphor as a tacit presupposition, and to proceed by expanding "what is scientific" to include relational matters.
In information technologies, his main contention is that what makes information "information" (as opposed to "data") is the relational aspect.