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Traditional Systems Engineering. (Is REALLY Model Based Systems Engineering). Kenneth A. Lloyd, Jr. Objectives of this Presentation. Provide background context & research for SE? Raise awareness of models in SE practice. SE Conops are models. SE Requirements are models.

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traditional systems engineering

Traditional Systems Engineering

(Is REALLY Model Based Systems Engineering)

Kenneth A. Lloyd, Jr.

objectives of this presentation
Objectives of this Presentation
  • Provide background context & research for SE?
  • Raise awareness of models in SE practice.
    • SE Conops are models.
    • SE Requirements are models.
    • SE Validation and Verification are models.
    • All SE Documents map to models.
  • Show Concepts are the foundation of models.
  • Are Systems Models?
    • No, but you can model systems.
  • Have some fun …

“Scientists [and engineers] come to their particular problem with

an accepted body of knowledge behind them, and on which they expect

to draw, without questioning the validity of each and every method,

assumption, or set of facts that they use. If we all tried to work

everything out from first principles, or even insisted on

understanding every piece of the puzzle in equal detail, none of use

would ever get anywhere. So to some degree we have to accept that

whatever has been acknowledged by the relevant community has been

done carefully and correctly, and can be relied on … But the

process is far from perfect, and once in a while we are surprised to

discover that a piece of knowledge we had long taken for granted is

questionable or even wrong.”

  • Duncan J. Watts, from
  • “Six Degrees: The Science of a
  • Connected Age” [p. 132]
  • Experts notice features and meaningful patterns of information that are not noticed by novices.
  • Experts have acquired a great deal of content knowledge that is organized in ways that reflect a deep understanding of their subject matter.
  • Experts’ knowledge cannot be reduced to sets of isolated facts or propositions but, instead, reflects contexts of applicability: that is, the knowledge is “conditionalized” on a set of circumstances.
  • Experts are able to flexibly retrieve important aspects of their knowledge with little attentional effort.
  • Though experts know their disciplines thoroughly, this does not guarantee that they are able to teach others.
  • Experts have varying levels of flexibility in their approach to new situations.

John D. Bransford, Ann L. Brown, and Rodney R. Cocking (eds.), How Experts Differ from Novices

4 major concerns of se
4 Major Concerns of SE
  • Enterprise aspects
  • Technical aspects
  • Project aspects
  • Agreement aspects


Our focus

models steve lehar
Models – Steve Lehar




Concepts do not need, nor do they have

the same “topology” as reality.

They have maps.

Courtesy: Steve Lehar, PhD – Cognitive Science, Boston University

models roger penrose
Models – Roger Penrose




Roger Penrose – Road to Reality

the chasm the problem domain
The Chasm – The Problem Domain

Courtesy: Steve Lehar, PhD – Cognitive Science, Boston University

minimal model system
Minimal Model System

A Hypothesis or



Real world


Idealized at equilibrium

A statement of what you believe you know, and what you don’t

in mathematical language
In Mathematical Language

F is called a functor

carnegie mellon models
Carnegie-Mellon Models

Carnegie-Mellon University

A System of Model Interaction

Related to Phenomenon






His Brain

For His



Aussi, ceci n’est pas un modéle

INCOSE Handbook 3.1 p. 2.4

conceptual hierarchy
Conceptual Hierarchy

Higher Abstraction

maps to models
Maps to Models

Petri nets

UML, SysML and

Textual Documents

Each level has elements of self-similarity – but not equality

technical aspects
Technical Aspects


  • Requirements definition,
  • Requirements analysis,
  • Architectural design,
  • Implementation,
  • Integration,
  • Verification,
  • Transition,
  • Validation,
  • Operation,
  • Maintenance, and
  • Disposal




Overview (excerpt)

“Effective management and stewardship of the nuclear weapons stockpile

into the future requires the ability to accurately assess the behavior of the

weapons in order to ensure robust and reliable performance while maintaining

the testing moratorium. These accurate assessments drive the requirements

for predictive capability in weapons science, including a fine-scale numerical

resolution and advanced models for physics and material behavior.” – pg. 5





models steve lehar redux
Models – Steve Lehar Redux


What does

this requirement



Phenomenon as





Requirements do not need, nor do they have

the same “topology” as reality.

They have maps to models.

Courtesy: Steve Lehar, PhD – Cognitive Science, Boston University

mapping information to models
Mapping Information to Models

Agents, spiders and crawlers … Oh, my!

contact info
Contact Info

Kenneth A. Lloyd, Jr.

Director, Systems Science

Watt Systems Technologies Inc.

Albuquerque, NM 87114 USA