I agree with Kathy's statement that "Formal models enable engineering to
take place" 100%. The need to find their way into conceptual models,
documentation, etc. If this is not the case, we will run into very serious
problems with many of our systems. The reason for this assumption is that
our current approaches for system engineering, software engineering, etc.
is still shaped byt the picture of a group of human experts working on a
set of requirements and delivering a system or a programmed solution.
SYstem of systems and service-oriented architecture and related ideas of
reconfigurable components that contribute to more than one system and that
can be composed to satisfy current user needs require that this components
are either self-aware or have place-holder agent that know the abilities of
the component. These self-aware systems or representing agents, however,
are systems that can only follow logic. There is no intuition, no best
guess, etc. These systems need formal models of what the components can
do, who they understand incoming data and how they represent outgoing data.
Without formal models, we will get stuck in all our distributed solutions.
Nice little essay on this and related topics: http://
All the best
Andreas Tolk, Ph.D.
Associate Professor (01)
ontolog-forum-bounces@xxxxxxxxxxxxxxxx wrote on 08/03/2007 10:28:59 AM: (02)
> >...people begin with a compelling interest
> >in some phenomenon, and they keep their eyes on prizing out some
> >explanation...people will naturally turn to formal and
> >mathematical models of the theoretical constructs of
> >interest....they will have been talking about sets and functions
> >and relations (oh my), or categories and morphisms and functors
> >(OMG)...none of this very modern model of mediate reprsentation is
> >to pose a terminal distraction from the intitial phenomenon of interest.
> Formal models enable engineering to take place. They allow people to
> communicate precisely about a phenomenon in which they have a
> compelling interest. They enable other people to understand exactly
> what is being said about the phenomenon of interest. They enable
> automated reasoning engines to process descriptions of the phenomenon
> of interest to derive logical consequences of the descriptions that
> were not immediately obvious to those who formulated the
> descriptions. Sometimes this identifies weaknesses in the
> descriptions that can be repaired to yield improved descriptions.
> Other times, the consequences of the descriptions elicit great
> surprise and consternation, but when checked against the world, they
> turn out to be correct! Historically, this has been one of the most
> powerful features of the scienfific method. One constructs a precise
> formal theory of a phenomenon. The formal theory explains empirical
> observations that have been obtained, but also entails some
> highly-counter-intuitive consequeces that -- amazingly -- turn out
> upon investigation to be correct!
> There are people who care more about formal models per se than they
> do about the phenomena being modeled. Such people tend to become
> pure mathematicians or logicians.
> There are people who care about the phenomenon and dislike
> mathematics and logic. Because they find mathematics difficult and
> unpleasant, they tend to balk at the intellectual effort required to
> apply math and logic to the phenomena in which they have an intrinsic
> interest. However, when they encounter a truly gifted teacher who
> can explain the relevance, they usually do come to understand why
> formal models are necessary and important.
> There also are people who have an affinity both for the formal models
> and for some particular phenomenon to which formal models can be
> applied. Such people are the ones who make the breakthrough advances.
> None of this is intended to disparage in any way other modes of
> inquiry and understanding, such as analogy, informal case studies,
> experiential learning, and the like. Formal models are not the ONLY
> way to understand a phenomenon. They do not replace other ways of
> thinking about a phenomenon. Nevertheless, they can be extremely
> useful. And they are essential for engineering and information
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