Breaking Down the "Documents and Policies" Project - Competency Questions

Our previous post defined a project for which a set of ontologies is needed ... "What access and handling policies are in effect for a document?" So, let's just jump into it!

The first step is always to understand the full scope of work and yet to be able to focus your development activities. Define what is needed both initially (to establish your work and ontologies) and ultimately (at the end of the project). Determine how to develop the ontologies, in increments, to reach the "ultimate" solution. Each increment should improve or expand your design, taking care to never go too far in one step (one development cycle). This is really an agile approach and translates to developing, testing, iterating until things are correct, and then expanding. Assume that your initial solutions will need to be improved and reworked as your development activities progress. Don't be afraid to find and correct design errors. But ... Your development should always be driven by detailed use cases and (corresponding) competency questions.

Competency questions were discussed in an earlier post, "General, Reusable Metadata Ontology - V0.2". (They are the questions that your ontology should be able to answer.) Let's assume that you and your customer define the following top-level questions:

• What documents are in my repositories?
• What documents are protected or affected by policies?
• What documents are not protected or affected by policies? (I.E., what are the holes?)
• What policies are defined?
• What are the types of those policies (e.g., access or handling/digital rights)?
• What the details of a specific policy?
• Who was the author of a specific policy?
• List all documents that are protected by multiple access control policies. And, list the policies by document.
• List all documents that are affected by multiple handling/digital rights policies. And, list the policies by document.

These questions should lead you to ask other questions, trying to determine the boundaries of the complete problem. Remember that it is unlikely that the customers' needs will be addressed in a single set of development activities. (And, work will hopefully expand with your successes!) Often, a customer has deeper (or maybe) different questions that they have not yet begun to define. Asking questions and working with your customer can begin to tease this apart. Even if the customer does not want to go further at this time, it is valuable to understand where and how the ontologies may need to be expanded. Always take care to leave room to expand your ontologies to address new use cases and semantics.

This brings us back to "General Systems Thinking". It is important to understand a system, its parts and its boundaries.

Here are some follow-on questions (and their answers) that the competency questions could generate:

• Q: Given that you have document repositories, how are the documents identified and tagged?
• A: A subset of the Dublin Core information is collected for each document: Author, modified-by, title, creation date, date last modified, keywords, proprietary/non-proprietary flag, and description.
• Q: How are the documents related to policies?
• A: Policies apply to documents based on a combination of their metadata.
• Q: Will we ever care about parts of documents, or do we only care about the documents as a whole?
• A: We may ultimately want to apply policies to parts of documents, or subset a document based on its contents and provide access to its parts. But, this is a future enhancement.
• Q: Do policies change over time (for example, becoming obsolete)?
• A: Yes, we will have to worry about policy evolution and track that.
• Q: What policy repositories do you have?
• A: Policies are defined in code and in some specific content management systems. The goal is to collect the details related to all the documents and all the policies in order to guarantee consistency and remove/reduce conflicts.
• Q: Given the last 2 competency questions, and your goal of removing/reducing conflicts, would you ultimately like the system to find inconsistencies and conflicts? How about making recommendations to correct these?
• A: Yes! (We will need to dig into this further at a later time in order to define conflicts and remediation schemes.)

Well, we now know more about the ontologies that we will be creating. Initially, we are concerned with document identification/location/metadata and related access and digital rights policies. We can then move onto the provenance and evolution of documents and policies, and understanding conflicts and their remediation.

So, the next step is to flesh out the details for documents and policies. We will begin to do that in the next post.

Andrea

Design Considerations from Weinberg's "Introduction to General Systems Thinking"

For those of you bored with this topic, you will be happy to know that I am on my last post. For those of you that find this interesting, thanks for sticking with me. :-) (Also, I will suggest that you get a copy of the book and find your own insights and wisdom in it. I certainly did not cover all the details!)

Let me jump into design considerations that Weinberg highlights. These are questions that I ask myself when modeling a system/designing an ontology:

• What notions are within and what are external to our systems? In other words, what are the boundaries of our system?
• How might we partition or decompose our system? When we decompose, how do the parts interact?
• In talking about "interactions", we start to talk about behavior... What is the behavior of the system and its parts? What are the system's and parts' possible states and how are they constructed or achieved?
• What is constant (survival and stable) about the system? What changes? Are there observable causes and effects?
• White box or black box techniques can be used to define and understand behavior. If we use black box, then we are observing and suffer the inadequacies of our observations (as pointed out in my second post on this subject). If we use white box, then we may miss behaviors that are inherent and not of our own construction. A combination of both techniques can provide good insights.
• In understanding behavior, you have to assume that you have incomplete knowledge. This is Weinberg's Diachronic Principle: "If a line of behavior crosses itself, then either: the system is not state determined or we are viewing a projection - an incomplete view."
• And, another, similar principle is the Synchronic Principle: "If two systems occur in the same position in the state space, at the same time, then the space is under dimensioned, that is, the view is incomplete."
• What are the properties or qualities of our systems? How do they change as the system evolves? Are there invariant properties? In particular:
• What properties are used for identity?
• What is "typical" about the system?
• What is "exceptional" (but important) about the system?
• What properties are used to define and control the system?
• What are we missing? (Sometimes we miss important properties or behaviors because we look at things using our "standard" approaches, notations and tools.) Can we look at a system or problem differently in order to gain new insights?
• Weinberg highlights the need to change our methods and approaches when new problems present themselves (especially if our current ways of thinking are not working). He captures his insights in his Used Car Law, and asks "[W]hy do we continue pumping gas into certain antique ways of looking at the world, why do we sometimes expend mammoth efforts to repair them, and why do we sometimes trade them in?"
• Another insight is Weinberg's Principle of Indeterminability: "We cannot with certainty attribute observed constraint either to system or environment." In other words, does our system have only small things in it because that is true or because we only have a net that lets in small things?

Hopefully, some of these questions will help when modeling or observing new systems. Let me know if you have other insights from Systems Thinking or just have other questions that you ask yourself when trying to understand a system or address a new problem.

Andrea

Part 2 on What I Learned from "General Systems Thinking"

Welcome back. I want to continue my overview of Gerald Weinberg's "Introduction to General Systems Thinking". I thought that I could shorten the discussion and fit the rest of the book into one more post. But, the more that I re-read the book, the more that I found to talk about! So, I am going to continue my ramblings and discuss Chapters 3 and 5, building on my previous post.

Chapter 3 is all about "System and Illusion" (that is its title!). It starts by asserting that a "system is a way of looking at the world". Obviously, how we look at something influences what we see. And, after we have looked at something, we can try to write it down/produce a model that describes the thing (describes that piece of the world).

We have lots of ways of looking at the world, and lots of models that reflect our different perspectives. But, these models won't all agree. It is important to remember that even if models don't disagree, each one may be totally valid (since it is defined from a unique perspective). Wow ... how do we do anything with that?

Well, we start by acknowledging that the problem exists and not get into religious wars about why "my model is better than your model" (or substitute the word, "ontology" for "model"). We need to understand the systems/models/ontologies and look at the perspectives behind why and how they were designed. When I have done this in the past, I saw things that I missed because I lacked certain perspectives. I also saw things that I did not need to worry about (at a particular point in time), but felt that I might need later. And, if I did need them later, how would I incorporate them? The last thing that I wanted was to paint myself into a corner.

So, what do we need to consider? Systems in the world of "general systems thinking" are sets of things. The entities in the sets vary by perspective. For an example, consider a candy bar. In a system that is concerned with eating, the candy bar may be broken into chunks of certain size and weight. And, you may be worried about the size of your chunk. But, in a system that is concerned with the chemical components of the candy bar, the size of the pieces doesn't matter at all. The constituent molecules and their combination are everything.

Hall and Fagen in their paper, "Definition of Systems" (1968) state that:

A system is a set of objects together with relationships between the objects and between their attributes.

That sounds a lot like an ontology. But, in defining the objects and their relationships, be careful. Don't be limited by how you write your model. Often, we get tangled up in our notations and limit our thinking to what we can express. For example, we say "I can express xyz with XML, or abc with OWL, or mno with UML. So, that is all that I can do." Indeed, we will always be limited by our notation, but our thinking should not be. This is one of the general cautions or principles that Weinberg highlights:

The Principle of Indifference: Laws should not depend on a particular choice of notation.

Unfortunately, Weinberg later turns around (in Chapter 5) and states that reality is quite different than what we want or what should happen (remember that there will always be exceptions to our laws and principles :-). Reality is never fully perceived, and our abstractions may be flawed since "the limited mental powers of the observers influence the observations that they make". That leads us to another principle:

The Principle of Difference: Laws should not depend on a particular set of symbols, but they usually do.

We get involved in our own symbols and notation. In fact, we may be agreeing on perspective and modeling the same system with the same components as someone else. But, we won't realize it, because we can't see past our notation.

So, let's take a step back and try to look at a system from many perspectives, and understand the semantics independent of the notation. We need to look at the system broadly enough to understand its scope, and then try to "get a minimal view" ("one that lumps together [information that is] ... unnecessarily discriminated"). But, when we simplify (or minimalize), we need to feel that our model is "satisfying", that it conforms to what we know of the world, its past behaviors and our past experiences. Weinberg calls this his "Axiom of Experience".

Buckminster Fuller put it more eloquently, I think ...

When I am working on a problem, I never think about beauty. I think only how to solve the problem. But when I have finished, if the solution is not beautiful, I know it is wrong.

Andrea

General Systems Thinking, a (classic) book by Gerald Weinberg

Early in my career, I was told to read the book, "An Introduction to General Systems Thinking", by Gerald Weinberg. It was supposed to teach me how to model. So, I dutifully read the book and ended up learning a great deal. Mostly, I learned how to look at problems, solutions and models.

For everyone that does not have time to read the book, or just wants a quick overview .... I would like to summarize the main concepts (what Weinberg calls "laws" and "principles") - especially the ones that have stuck with me through the years. So, here goes.

Let's start by observing the obvious. Systems are complex, and all of our science and technology fall down when faced with the fullness of that complexity. So, we try to simplify. We try to find the least amount of information (smallest number of assumptions or details) that help us define, predict or understand a "system". This works well when we have lots of individuals (or parts) in a system (leading to the "law of large numbers" - that statistical predictions work well when calculated over large populations) or when we have a very small number of individuals (when we can observe them all). We have problems, however, in the world of medium numbers.

Weinberg's Law of Medium Numbers: For medium number systems, we can expect that large fluctuations, irregularities, and discrepancy with any theory will occur more or less frequently ... Translated into our daily experience ... the Law of Medium Numbers becomes Murphy's Law ... Anything that can happen, will happen.

Many systems are typically made up of a medium number of individuals/parts. This means that we can't just average across the behavior of the parts, or look at all the parts independently. Taking the latter approach, we would have to consider all the parts and all their interactions within the system. But, we quickly find an exponential number of things that we have to study! So, we apply reasoning and simplifying assumptions.

Weinberg builds on the work of Kenneth Boulding ("General Systems as a Point of View") and highlights the need to discover "general laws". Typically, we find these laws by generalizing from specific, different disciplines. He examines the roles of analogy, categorization and the concepts of generalization and induction. The key is to find similarities in the individual "laws" of specific disciplines and extrapolate to the general laws. Then we can apply these general laws in new situations to understand them and draw conclusions. Weinberg says:

To be a successful generalist, one must study the art of ignoring data and of seeing only the "mere outlines" of things.

In defining his general laws, Weinberg makes a point of discussing that laws evolve as new information is discovered. But, he also observes that the laws themselves are usually the last things to change (the "Law of the Conservation of Laws" :-). Instead, conditions are added to the laws to account for negative cases. So, a law becomes "x=y unless ..., or if ...". Like the rules of parlay (Code of the Order of the Brethren) from Pirates of the Caribbean, "the code is more what you'd call 'guidelines' than actual rules". Weinberg's laws are meant to be memorable, generally true and 'guidelines'. They can be understood as bits of insight and "stimulants" to thought (i.e., not really "laws").

What are a few of Weinberg's general "laws"?

• Law of Happy Peculiarities - Any general law must have at least two specific applications
• Law of Unhappy Peculiarities - Any general law is bound to have at least two exceptions (or, if you never say anything wrong, then you never say anything)
• Composition Law - The whole is more than the sum of its parts
• Decomposition Law - The part is more than a fraction of the whole

I love Weinberg's "laws" and sense of humor. I noticed that one of my development "rules of thumb" simply follows from Weinberg's Law of Happy Peculiarities ... "You must always have at least 2 use cases. A use case of one always has a solution."

So with that observation, we come to the end of Chapter 2 in Weinberg's book. There are more great discussion points and principles that I want to cover. But, I will leave them to my next post. In the meantime, I will close with some thoughts from the mathematician, Mark Kac (which are mostly also at the end of Chapter 2):

Models are for the most part caricatures of reality, but if they are good, then, like good caricatures, they portray, though perhaps in distorted manner, some of the features of the real world ... The main role of models is not so much to explain and to predict - though ultimately these are the main features of science - as to polarize thinking and to pose sharp questions ... They should not, however, be allowed to multiply indiscriminately without real necessity or real purpose.

Kac implies that there are three activities that involve models - "improving thought processes" (as described in the quote above), "studying special systems" (understanding and translating to the specific from the general) and "creating new laws and refining old" (systems research!).

Amen!

Andrea