## Semiotic Theory Of Information • 6

2014 Oct 14

Through the 1970s I gradually recovered from my early traumas with Fortran, and with the aid of more symbol-friendly programming languages like Lisp and Pascal began to play around again with implementing simple forms of graphical calculi inspired by Peirce and Spencer-Brown in the form of programs that carried out the requisite transformations in automatic or guided interactive fashions.

Experiments like these, moving from scribblings on paper to algorithms and data structures in electronic media, brought about a transformation in my perspective on symbolic logic and other semiotic processes.  The shift was very gradual over the decade that followed, but I think it began with thinking of computer memory as very like a sheet of paper, a tabula rasa, or a Sheet of Assertion as Peirce dubbed the unmarked state in his systems of logical graphs.

Thinking this way naturally brings out the system-theoretic aspects of semiosis in general and logic in particular.  One begins with sign relations as subsets of cartesian products ${O \times S \times I},$ where ${O, S, I}$ are sets of objects, signs, and interpretant signs, respectively, and over time one begins to see dynamic systems in place of those sets.  Then one day in the mid 1980s I distinctly remember flashing on the fact that the graph-theoretic data structures I and my programs were manipulating in memory were actually diagrams in Peirce’s sense.

With that pre-ramble, here is a bit of background from (Awbrey & Awbrey, 1990) that describes our system-theoretic approach to agents with a capacity for learning and reasoning.

### The State Space Approach to Intelligent Systems

The common definition of a system as a list of variables is useful but not absolute.  It characterizes the system only as measured from a particular frame of observation.  The property of a system that we really care about is its state space, or a representation showing the possible states of a system.  When considering systems that exhibit complex sets of properties, such as being able to transform information and to act with intelligent purpose, it becomes more difficult to specify in advance the exact nature of the state space, or even whether a space exists that satisfies a given set of specifications.  Therefore we do not consider the state space of intelligent systems to be given absolutely, as the subset of some predefined space (like ${\mathbb{R}^{n}}$), but defined provisionally, subject to a list of constraints and subject to revision.

When considering intelligent behavior, we are most interested in the state of information that an interpreter system has about an object system, and this information has its expression in a system of signs.  The characterizations sign, object, interpreter are not so much exclusive categories of entities as roles that systems may play within the so-called sign relation.  Although interested in the nature and relations of these systems in themselves, whatever we can say about them takes place within the domain of signs.  As we use the words, sign is the general category, while symbol is a particular type of sign.  From the pragmatic point of view, almost all the actual work of computation is involved with transformations between expressions in the symbolic domain.

To be continued …

### 8 Responses to Semiotic Theory Of Information • 6

1. Poor Richard says:

For once I think I follow most of this. I like the term “pre-ramble” too. I can see a list of variables as a particular perspective of a system, and a set of states as another, and a combination of the two as another. My default abstract model of a system is a recursive network of associations. Associations are composed of nodes and links, both of which can have attributes with values. Triples, as in RDF and other data modeling approaches, seem very useful as a representational tool for data objects and associations. How do such triples fit, if at all, in the kind of semiotics, signs, and symbols that you concentrate on?

• Jon Awbrey says:

The first-generation systems analysts who taught my first courses in Systems & Simulation were rather fond of their no-nonsense definition of a system as a “List Of Variables” (LOV). Of course they knew that a system-in-itself was a thing-in-reality that was not exactly unknowable but knowable only via the various representations that we toss in its direction to see which ones will stick.

Indeed, the thing that distinguishes the object system is the fact that it has so many representations. Collecting, comparing, contrasting, coordinating, and critically choosing among these representations is the business of inquiry on the semiotic plane of signs and interpretant signs in observational-experimental interaction with “what goes there” in the object domain.

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