Conceptual Modeling

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Revision as of 22:52, 15 August 2019 by Andy Culbertson (talk | contribs) (v0.2.0: Reduced to a bullet point, summary format. Added rationales. Replaced my existing, haphazard method with the outline of a more principled one.)
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Version 0.2.0, 8/15/2019

Introduction

What is conceptual modeling?

  • The process of describing a situation in a way that enables someone to fulfill a particular purpose.
  • It's not the concept modeling of architecture (making a physical model of a design), and it's broader than the conceptual modeling of software design.
  • Regardless of its specific procedure, logically it divides into two types of activities:
    • Analysis: separating the subject matter into its components.
    • Synthesis: reassembling the parts by identifying their relationships.

What is this article?

  • A summary of my current thinking on a general approach to conceptual modeling.

Rationale

Why am I developing a modeling approach?

  • Modeling is a key tool in my life, and I want a more effective way to do it. Developing a method will help me use it more often and with a more thorough and consistent procedure.
  • Modeling should get more widespread use, and work on a general approach can serve as a foundation for its adoption.
  • It's relatively easy to find tools of analysis and modeling in specific domains, but it's hard to find work that ties it all together.
    • Applying a single method to multiple domains means that each domain's conceptual modeling will involve less duplicated work than if they developed their techniques independently.

Why am I writing this article?

  • A summary will help me direct my work and organize the presentation of my findings. The summary is a research agenda.

Why do we do conceptual modeling?

  • To clarify thinking.
  • To learn information or a skill.
  • To create other content.
  • To build tools.
  • To manage activity.
  • To reach agreement.
  • To make decisions.
  • To solve problems.
  • In the case of this essay, our purpose is to have a general modeling procedure to apply.

Why should we improve it?

  • Better models help us solve more problems and solve them better.
  • Faster modeling helps us create these better models sooner.

Interdisciplinarity

What is it?

  • The collaboration among multiple academic or professional disciplines to pursue the interdisciplinary field's goals.

Why should a conceptual modeling method use it?

  • Different disciplines have methods and frameworks and viewpoints particular to themselves that can be generalized so that other disciplines can use them.

How can we use it?

  • Investigate the potential contributions of various disciplines to modeling.
  • Collaborate with those disciplines, seeking input and dialoguing for new insights.
  • Model the modeling of various disciplines, and generalize the results.

Overview: How does conceptual modeling work?

  • The modeler follows a process that involves querying internal and external sources in terms of particular conceptual frameworks, evaluating the findings, and encoding the resulting model to present it to the model's stakeholders.

Status

  • As a summary, this article doesn't give all the practical advice we'd want.
  • As a summary, the article doesn't address every question, objection, or competing model.
  • As a work in progress, much of it is likely to change in the relatively near future.
  • As a work in progress, it has uneven coverage and gaps that future work will hopefully fill.
  • As a model of modeling, it's subject to evaluation.
  • Since I'm an outsider to most of these disciplines, my initial sources are introductory or popular-level treatments.
  • Since the article is primarily meant for my own use, it might not make complete sense to other people.

Process

Workflow

What is it?

  • The large-scale procedure that moves the model from conception to completion.

Why do we need it?

  • A consistent procedure enables more reliable planning and better results.

How is it done?

  • An agile methodology is a good starting point.
  • Initialize the project.
  • Gather requirements.
  • Gather personnel and other resources.
  • Plan the work.
  • Research conceptual frameworks and domain knowledge.
  • Conduct modeling sessions.
    • Modify existing frameworks, and create new ones as needed.
  • Test the results.
  • Iterate over this procedure to improve the model.
  • Present the final product.
  • Close the project.

Mental processes

What is a mental process in conceptual modeling?

  • A set of activities carried out in the mind to pursue a goal, in this case to become aware of the possible components and relationships of a model and to reason about them.

Why should we know about them?

  • Modeling can't happen without mental processes.
  • The quality of mental processes vary and can be improved with knowledge and practice.

Querying

What is it?
  • Consciously directing the subconscious to deliver answers to explicit or implicit questions.
Why do we need it?
  • It's the way we articulate information we already know but don't have in our immediate awareness.
  • Having no procedure means the information becomes conscious haphazardly rather than when we need it.
How does it work?
  • The mind recalls information based on cues.
  • To ask questions you need a conceptual framework, even a simple and fragmentary one. Your subconscious is matching its perceptions and contents to patterns.
  • Make guesses about answers, and evaluate them based on the feelings and thoughts your subconscious reports back with.
  • The mind can index information in many ways, so be varied in your cues.
  • Externalize the information you surface to serve as further cues without burdening your working memory.

Reasoning

What is it?
  • The application of logic to information to draw inferences and evaluate claims.
Why do we need it?
  • Reality apparently works in a consistent and logical fashion, and models that aren't regulated by logic have a higher chance of clashing with reality, thus failing or causing harm.
How is it done?
  • Learn appropriate conceptual frameworks for logic: categorical, propositional, first-order, bayesian, informal fallacies, cognitive biases, etc.
  • Carry out the mental processes of modeling, applying the logical frameworks to the information and following the conclusions the applications indicate.

Productivity

What is it?

  • Practices engaged in to maximize the amount of work accomplished.

Why do we need it?

  • Using time wisely is responsible.
  • Many projects will be under a time crunch.
  • Maximizing work gives you a competitive advantage, against the problems you're solving, if nothing else.

How is it done?

  • Follow general productivity practices.
  • Follow nonlinear modeling.
  • Apply as many frameworks as applicable.
  • Build on previous work.

Sources

  • Agile development
  • Design thinking
  • Engineering

Framework

Construction

What is it?

  • Creating a new conceptual framework to use in creating a model.

Why do we need it?

  • Models seem to be instantiations of more general frameworks.
  • Our minds need expectations to serve as cues for surfacing more information and patterns to recognize.
  • Models need many different shapes and features to account for all the situations we need to understand to solve all the kinds of problems we encounter.
  • New frameworks can be created based on preexisting components.

How is it done?

  • The most manipulable models are based on a framework of parts and relationships.
  • Adapt existing frameworks.
    • Components can be assembled from simpler particles and differentiated from more general categories.
    • Components can be decomposed and then reshaped.
  • Abstract from existing models.

Sources

What is it?

  • Frameworks lie on a spectrum of generality, and different fields tend to generate frameworks in different places along the spectrum.
  • These are mainly sources on the more general end of the spectrum, plus some closer to the specific end.

Why do we need it?

  • The more general fields give us fundamental frameworks and components that can be applied to a broad range of modeling situations.
  • The more specific fields give us models that can be applied directly to situations in those fields and to other fields by analogy.

How is it done?

  • Explore likely fundamental fields:
    • Linguistics
    • Mathematics
    • Software engineering
    • Knowledge representation
    • Knowledge organization
    • Data visualization
  • Explore more specific fields. This list is a small sample:
    • Physics
    • Systems theory
    • Intelligence analysis
    • Business analysis
    • Social science research

Presentation

What is it?

  • The representation and framing of the model in relation to its purpose for the project's stakeholders.

Why do we need it?

  • The models we build are abstractions, but to communicate them and work with them effectively, we have to give them concrete representations.
  • Fulfilling the project's purpose will usually require communicating more than a bare statement of the model. The presenter will need to frame it, which may include introducing and applying the model.

How is it done?

  • Identify the key communication factors.
    • Purpose
    • Audience
    • Context
    • Format
  • Compose the presentation.
    • Transform the model into the needed format.
    • Frame the model according to the project's purpose.
  • Conduct the presentation.
  • Evaluate the presentation.

Examples

From this site:

From other authors who seem to take a similar approach:

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.

Sterman, John. Business Dynamics: Systems Thinking and Modeling for a Complex World. Boston: Irwin/McGraw-Hill, 2000.