Conceptual Modeling

Version 0.1.0, 6/20/2018

The purpose of this article is to develop a method of conceptual modeling based on the techniques I've practiced over the years. The purpose of doing that is to help myself use the method more often and with a more thorough and consistent procedure and to come up with ways to teach it to anyone who's interested.

The article is a work in progress, as is the method.

Definition

Conceptual modeling is a process of clarifying the structure and dynamics of a system of concepts that describe a situation. Procedurally the process takes different forms, but logically it divides into two types of activities--separating the subject matter into its components in analysis, and reassembling the parts by identifying their relationships in synthesis.

Goals

What are the goals of modeling in this method? There are two stages to think about in a modeling journey: the product of the modeling itself and the purposes that product can be used for.

The product I intend from a modeling project is a model of the subject I'm observing. The representation of the model can take various forms, such as a diagram, an essay, or a computer program. The model itself is an abstraction, though it will always have to take some form, if only as a set of ideas in the mind.

Another metaphor I use for this product is a map. This article, for example, is a representation of a map of my modeling method. This is appropriate for my journey metaphor.

The second stage of travel is the use you'll put the model to. To name some key examples, the model could be a basis for learning information or a skill, for creating other content, or for making a decision or solving a problem. In this case our purpose is to have a general modeling procedure to apply.

Why are we interested in knowing the goals of modeling? First, the goal determines what aspects of the material we're modeling are important. So it's key to defining the model and also to directing our attention and maximizing our use of time. In this case, these goals will define our model of modeling.

Second, knowing the point of what we're doing is a great motivator. Sometimes it can make the difference between persevering and giving up.

Subjects of study

Any modeling project will have a subject that's being modeled, which I'll call the subject of study. It helps to survey what subjects these can be. You can model just about anything. But you might not think of something as a thing that can be modeled. And when thinking through a general modeling procedure, your ideas about the procedure might be too limited if you don't keep in mind the range of possible subjects.

So what kinds of modelable objects are there? They can be classified a number of ways that might affect the ways you model them. I'll list a few categories here that could be grouped and contrasted in various ways and an example or two of each: static (images) vs. dynamic (videos, procedures); concrete (concrete) vs. abstract (equations); individual (employees, birds) vs. collective (businesses, ecosystems); discrete (social networks) vs. continuous (temperatures); visual (dances), auditory (room acoustics), tactile (paper textures), textual (scientific articles), etc.; objective (nerve impulses) vs. subjective (intuitions, sensations); causal (geological processes) vs. purposeful (engines). Each subject can be placed into more than one category.

Additionally, you'll be modeling particular dimensions of the object depending on your purpose. For example, a film could be analyzed for its dialog, cinematography, music, acting, directing, its influences, impact on society, and so on.

Representation

The models we build are abstractions, but to communicate them and work with them effectively, we have to give them concrete representations. These take the form of various media types. Some of the most common are written or spoken text, information visualizations such as maps and graphs, and computer file formats such as software and data files. There may be other effective representation formats in auditory, haptic, and other modes.

It's important to distinguish the representation from the model itself. It means you can switch to a different representation if it better communicates the model or fits the needs of the audience. Another representation might also make the model easier for you to explore or manipulate as you're developing it.

Procedure

Here I'll describe my current modeling process. One main purpose of writing this essay is to work out a more refined version of this process, but I'll start with what I do now. That way I don't misrepresent myself too badly and I don't lose as much potentially useful information about the method.

Subject matter

The process starts when I run across a subject I feel the need to understand. Usually it's because I want to use it to do something, or I just feel confused by it. Sometimes I feel like it's something I should understand, as if no self-respecting me would remain ignorant of it (film criticism, for example, though I haven't gotten around to studying that one).

Sometimes it's a subject that comes from sources outside myself (e.g., memory improvement, math); sometimes the source is me (e.g., this modeling method, my religious beliefs).

My output mostly takes the form of writing, and it has two broad stages: processing (one or more rounds of notes and journaling, for lack of a better term) and communicating (one or more essays).

Processing

Most of my processing output is in the form of paragraphs and headings. Sometimes it takes the form of outlines, with hierarchies of related points. Sometimes I make lists or tables, occasionally a flowchart, usually in the form of an outline. Once in a while I try making a diagram, but I quickly run into problems laying it out or knowing how to represent the kinds of information I want.

I tend to start the processing stage by writing some introductory remarks to capture the reasons I'm looking at this subject and what I want to get out of it. I do this in the hope that it'll focus and direct my analysis. I'm not sure how much it does.

Note-taking

I've found my mind doesn't work well in a vacuum, so to jump start my thinking I need material to respond to. This takes the form of things like notes on sources, memories of processes or experiences, and preliminary outlines based on my conception of the subject matter.

Note-taking mostly takes the form of prose statements pulling out whatever information from my source seems significant. Often I like to just quote the source, since that saves time in the moment. One of my long-term battles is taking in a source efficiently. Many things can bog this process down, such as having to paraphrase (though paraphrasing into a more useful form could be worth the drawbacks).

Journaling

Journaling takes the form of writing my thoughts on the subject in semi-organized fashion. The journaling step represents several passes through various parts of the material, capturing my assessments of its characteristics, parts, and inner workings.

I compose my writing mentally in chunks. I think for a while about what I want to say, usually points I want to make around one or two ideas, and then I write it somewhat carefully. I try to arrange the points in a logical order so that they flow into each other and communicate clearly, even though in this stage I'm only really writing for myself.

Sometimes I only do enough thinking beforehand to feel that I have a good starting point, and I do the rest of my thinking as I'm writing. One statement will spawn new thoughts to capture. This happens even if I think I'm starting with my whole set of thoughts on the idea.

If I have a thought on a point but no time to flesh it out, I write a brief reminder of it wherever I am in the journal, or sometimes in a logical spot among my other thoughts, on a separate line that starts with a hyphen so I can find it again.

My journaling is only loosely organized. I give it headings as I go so it's easier to find earlier thoughts again and reorganize them if it helps me think about them.

As I think through the subject matter, I'm generally looking for what its parts are, how it works, and how I can make use of it. To that end I find myself asking these kinds of questions, more or less consciously, of the pieces I find:

• Definition: What is this? How could I identify the answer (e.g., in choosing between interpretations of X)?
• Properties: What is this like? What are this item's properties?
• Contrasts: What are the contrasts within X? How is this different from other things?
• Naming: What keyword or name identifies this for me?
• Categorization: What kind of thing is this? Why this category? What kinds of things are here? What can I generalize from this? What's this about? What's the topic of this model?
• Whole-part: What are its parts? What’s an example of this? What are all the options? What if we group these and treat them as a new whole?
• Causality: How does this work? How did that happen? Why is this here? What's the bigger picture? What are the conditions (necessary and sufficient) for this? What would need to be true?
• Function: What is this doing? What does this do? How is this used? How do I do that?
• Sequence: What then?
• Implication: So what? What does this imply about X?
• Purpose: What's the point? What is the purpose of this model? What's instigating it?
• Counterfactuals: What about X? What if X? Why wouldn’t X happen? Why this one and not a different one?
• Patterns: How are these arranged? What predefined structure or grammar could help me organize and extend my observations? What rules seem to govern this? How can this type of item (node, relationship, pattern) be applied to other parts of the model?
• Introspection: What's unusual about this? What doesn't make sense about this? What’s funny (sad, angering, scary, surprising, interesting, confusing, etc.) about this? What's easy or hard to understand here?
• Logic: Does that question make sense here?
• Rationality: How do you know? Are these uses of X (and maybe overlapping terms) really the same? What's really going on?
• Priority: What's important here?
• Concerns: What are the concerns (values, worries) here?
• Next steps: What do I do next? What am I missing? Where do I start? What do I notice? What would be satisfying to know about? How would I answer this?
• Dimensions: What dimensions of this component should I notice? What other dimension of this topic should I look at? What new way should I think about this?

As I go, I try to develop a controlled vocabulary for the model. That is, I try to come up with a single term to use whenever I'm talking about a concept rather than using several synonyms throughout the discussion. This makes the writing more boring and repetitive but hopefully clearer. It at least helps me think about it more clearly.

I make haphazard progress through the processing stage. I'm guided by certain intuitions based on my semi-conscious concerns and my underlying ideas of how models are structured, but I find myself getting stuck more often than I'd like. My procedures for getting unstuck are very poorly defined and I think take longer than they should. I'm hoping that formalizing my method somewhat will solve this problem.

Communicating

The communicating stage is basically a revision of the journaling where I assemble my thoughts into something people can hopefully follow, even if it's not very exciting to read.

I usually start my writing with a general outline in mind to make sure I cover all the subject matter and do it in a logical order that's fairly easy to grasp. This outline usually gets revised as I write, because as I progress I find out the material actually falls more naturally into another organization. This is annoying but almost inevitable. It seems my thinking on a subject never stops, at least until I stop writing about it.

At the end of the writing, I read it a few times to revise and put on the finishing touches you normally need when writing for an audience--making sure sources are cited correctly, all the formatting looks right, and so on.

Examples

From this site:

• On Being an Agnostic Christian
• A Framework and Agenda for Memory Improvement
• Navigating the World of Comics
• Math Relearning/Fundamentals
• Math Relearning/Number Sense
• Math Relearning/Math Student Simulator/Introduction - A discussion of learning by programming.
• Book Weeding Criteria

From other authors who seem to take a similar approach:

• Visualization Analysis and Design by Tamara Munzner

Roadmap

Here, in general terms, are the improvements I have in mind for this essay.

• Articulate and expand my metamodel.
• Articulate the intuitions to follow.
• Formalize a procedure.
• Articulate a supporting model of the mind.
• Expand the method to cover group processes.
• Expand it to cover evaluation of claims.
• Articulate the essential and distinctive features of my approach.
• Expand my library of model patterns.
• Expand my library of indirect questions.
• Incorporate a programming approach.
• Develop arguments for studying modeling.

Potential sources

Alexander, Christopher, and Christopher Alexander. The Process of Creating Life: An Essay on the Art of Building and the Nature of the Universe. The Center for Environmental Structure Series, v. 10. Berkeley, CA: Center for Environmental Structure, 2002.

Bernard, H. Russell, Amber Wutich, and Gery Wayne Ryan. Analyzing Qualitative Data: Systematic Approaches. 2nd ed. Los Angeles: SAGE, 2017.

Britt, David W. A Conceptual Introduction to Modeling: Qualitative and Quantitative Perspectives. Mahwah, NJ: Lawrence Erlbaum Associates, 1997.

Checkland, Peter. Systems Thinking, Systems Practice: Includes a 30-Year Retrospective. Chichester; New York: John Wiley, 1999.

Cooper, Alan. About Face: The Essentials of Interaction Design. 4th ed. Indianapolis, IN: John Wiley and Sons, 2014.

Evans, Eric. Domain-Driven Design: Tackling Complexity in the Heart of Software. Boston: Addison-Wesley, 2004.

Flood, Robert L. Rethinking the Fifth Discipline: Learning within the Unknowable. London; New York: Routledge, 1999.

Halpin, T. A., and A. J. Morgan. Information Modeling and Relational Databases. 2nd ed. Morgan Kaufmann Series in Data Management Systems. Burlington, MA: Elsevier/Morgan Kaufman Publishers, 2008.

Herman, Amy. Visual Intelligence: Sharpen Your Perception, Change Your Life. Boston: Houghton Mifflin Harcourt, 2016.

Heuer, Richards J., and Randolph H. Pherson. Structured Analytic Techniques for Intelligence Analysis. 2nd ed. Washington, DC: CQ Press, 2015.

Horton, Susan R. Thinking through Writing. Baltimore: Johns Hopkins University Press, 1982.

Huddleston, Rodney D., and Geoffrey K. Pullum. A Student’s Introduction to English Grammar. Cambridge, UK; New York: Cambridge University Press, 2005.

Lesh, Richard A., and Helen M. Doerr, eds. Beyond Constructivism: Models and Modeling Perspectives on Mathematics Problem Solving, Learning, and Teaching. Mahwah, NJ: Lawrence Erlbaum Associates, 2003.

McDonald, Kent J. Beyond Requirements: Analysis with an Agile Mindset. New York: Addison-Wesley, 2016.

Michalko, Michael. Thinkertoys: A Handbook of Creative-Thinking Techniques. 2nd ed. Berkeley, CA: Ten Speed Press, 2006.

Munzner, Tamara. Visualization Analysis and Design. A.K. Peters Visualization Series. Boca Raton: CRC Press, Taylor & Francis Group, CRC Press is an imprint of the Taylor & Francis Group, an informa business, 2015.

Osborne, Grant R. The Hermeneutical Spiral: A Comprehensive Introduction to Biblical Interpretation. 2nd ed. Downers Grove, IL: InterVarsity Press, 2006.

Porter, Bruce, Vladimir Lifschitz, and Frank Van Harmelen, eds. Handbook of Knowledge Representation. 1st ed. Foundations of Artificial Intelligence. Amsterdam; Boston: Elsevier, 2008.

Rosenfeld, Louis, Peter Morville, and Jorge Arango. Information Architecture: For the Web and Beyond. 4th ed. Sebastopol, CA: O’Reilly Media, Inc, 2015.

Saeed, John I. Semantics. 4th ed. Introducing Linguistics 2. Chichester, West Sussex [England]; Malden, MA: Wiley Blackwell, 2016.