Complete

A model is an informative, intentional, well-defined, clear, complete, and correct representation of the set of things and relations between those things defined within an axiomatic system.

A model enables a community of stakeholders trying to achieve a specific goal or objective or a range of goals/objectives to agree on important details related to capturing meaning or representing a shared understanding of and knowledge in some system of interest.

In her book An Introduction to Ontology Engineering (PDF page 23), C. Maria Keet, PhD, provides discussion about what constitutes a good and perhaps a not-so-good ontology.  There are three categories of errors she discusses and these three ideas are applicable to models:

  • Syntax errors: If the expression of the model is of some specific machine-readable language, having a syntax error is similar to computer code not being able to compile.
  • Logic errors: If the expression of the model has an error in logic then the model does not reflect what is being modeled and the  model will not work as would be expected.
  • Precision and coverage errors: If the expression of a model precisely reflect what is being modeled with a high enough level of precision and a high enough level of coverage of the area of interest being modeled then the model might not be adequate enough to meet the needs of the stakeholder of a system that has been modeled. For example, when you neglect to represent a rule and therefore something that should not be permissible appears in the model to be permissible because you neglected to include that rule; then you might not get the result you expect from the model.
Completeness of a model is determined by the level of precision (high or low) and the level of coverage (high or low) relative to the specific goal or objective or range of goals/objective that a community of stakeholders has agreed to  with respect to some system of interest.

Borrowing from the ideas of Keet in her book, here is the notion of completeness shown graphically. 
Imagine that this gray cloud represents the scope of some area of interest:

Now, imagine that the darker gray circle represents the set of aspects within that area of interest that are important to achieving some stated goal(s) or objective(s) of your system. Saying this another way; a system does not ever represent 100% of the things in the real world, just the things, relations, and rules that are important to achieving the goals/objectives of the system.


Further imagine that the hot pink circle represents the actual things represented within your model.  Notice how in the graphic below the hot pink set is smaller than the dark gray set which means that the actual things represented is LESS THAN the important things in the system of interest.  That means that the  system is NOT COMPLETE. (i.e. important things are missing)  There is a missing thing, a missing representation of a relation between two things, a missing rule or assertion or constraint or restriction.  This lack of completeness can result in problems with the operation of the system.

An incomplete representation can result from sloppiness or neglect or a mistake on the part of the person representing the system.  But an incomplete representation can also result from an inability of the language used to represent the system in machine readable form such that a software application can understand the system.

What you want to achieve, a complete representation of the important things within the area of interest, looks like the graph below. You don't see the dark gray circle because the hot pink circle which represents the set of things actually represented is the same size as the important things within the area of interest.  Actually represented = Important = Complete.
Building systems that are effective is really that straight forward. It is really about common sense and properly employing the elements of logic. A system needs to be complete and precisely reflect the area of interest that is important to achieving the goals and objectives of your system of interest.

Additional Information:

Comments

Post a Comment

Popular posts from this blog

Overview

Image
Imagine it.  Freedom from drudgery. Freedom from the toil of tedious, repetitive, mundane, monotonous tasks. This overview is an attempt to take the work that I have done to understand XBRL-based reports and back into a framework for explaining those reports to accountants and software engineers building software for accountants.  I am not referring to this as model-driven reporting .   The sources of the information provided below are my " lab notebook " from December 2007 to October 2022; my Digital Financial Reporting blog from October 2022 until January 2025; Mastering XBRL-based Digital Financial Reporting ; and finally my Seattle Method  documentation. What I have tried to do is take the seemingly hundreds of incomplete, far too technical, typically nonstandard explanations that I have come across; take the good/best ideas from each, make improvements to those explanations, and resynthesize the information into a form that is useful to me and perhaps usef...
Read more

Knowledge Representation Approach

Image
Knowledge representation is simply organizing knowledge related to some area of knowledge into some physical form.  A knowledge representation provides an explanation of or description of the nature and structure of something. There are a number of different approaches that a knowledge representation might take, each approach having a different level of expressivity and reasoning capability.  These different knowledge representation approaches form a spectrum.  The following is a graphic which shows the expressivity and reasoning capability of different knowledge representation approaches: The following is a brief description of each of the different knowledge representation approaches which will help you understand the relative expressivity and reasoning capability of each approach: Name authority : A name authority provides what amounts to a dictionary or list which amounts to a flat inventory of terms with no relations expressed between the terms. This can be thought...
Read more

Area of Knowledge

Image
An area of knowledge (a.k.a. area of interest) is a highly organized socially constructed aggregation of shared knowledge (a.k.a. corpus) for a distinct subject matter.  Subject matter experts (SMEs) create, use, and maintain an area of knowledge. An area of knowledge has a specialized insider vocabulary, underlying assumptions (axioms, theorems, constraints, assertions, restrictions), and persistent open questions that have not necessarily been resolved (i.e. flexibility is necessary).  You can think about an area of knowledge as being characterized in a spectrum with two extremes: Kind area of knowledge: clear rules, lots of patterns, lots of rules, repetitive patterns, and unchanging tasks. Wicked area of knowledge: obscure data, few or no rules, constant change, and abstract ideas. Stakeholders of a system need to be in agreement as to an undisputed core knowledge of an area of knowledge. Sensemaking is the process of determining the knowledge, or deeper meaning or signifi...
Read more