Engineering and design science methodologies (2016TEWMHB)
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Summary Engineering and design science methodologies (2016TEWMHB)
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Engineering and design science methodologies (2016TEWMHB)
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Universiteit Antwerpen (UA)
The summary provides a comprehensive understanding of the methodologies and tools used in engineering and design science, and how they can be applied in the field of management. Got me a 16/20.
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Engineering & Design Science
Table of Contents
The Purpose of Engineering...............................................................................................5
Engineering:............................................................................................................................. 5
Technology .............................................................................................................................. 6
Engineering.............................................................................................................................. 6
Technology and Economy ......................................................................................................... 7
Economy.................................................................................................................................. 7
Economy and Innovation.......................................................................................................... 7
Science, Models, and Paradigms .......................................................................................8
The Scientific Method .............................................................................................................. 8
The characteristics of models ................................................................................................... 8
Observation and modelling ...................................................................................................... 8
Modeling ................................................................................................................................. 9
Laws of nature ......................................................................................................................... 9
Philosophy of science ............................................................................................................... 9
Self-fulfilling models .............................................................................................................. 11
The Policy Pitfall: ................................................................................................................... 12
Thomas Kuhn: Structure of scientific revolutions .................................................................... 12
Past and Future...................................................................................................................... 13
Auctoritas dixit = the teacher said .......................................................................................... 13
The science of engineering ..............................................................................................13
Science and engineering ......................................................................................................... 14
The process of engineering ..................................................................................................... 14
The scientific method ............................................................................................................. 15
The essential purpose of science ............................................................................................ 16
The term “Design Science” ..................................................................................................... 16
The Science of Design ............................................................................................................. 16
Distinguishing Scientific methods ........................................................................................... 17
Design vs. Design Science ....................................................................................................... 17
Empirical method ? ................................................................................................................ 17
Designing the artificial (Technieken van ontwerp) ...........................................................18
, The functional - constructional gap ......................................................................................... 18
The functional – Construction mapping .................................................................................. 19
The inducive generalization .................................................................................................... 19
.............................................................................................................................................. 19
Abstraction: Modeling............................................................................................................ 19
Modeling: Lacunas ................................................................................................................. 20
Logic: Optimization ................................................................................................................ 21
Optimization: Lacunas ............................................................................................................ 21
Realization: Mapping ............................................................................................................. 22
Mapping: Lacunas .................................................................................................................. 22
Validation: Statistics .............................................................................................................. 23
Statistics: Lacunas .................................................................................................................. 23
Architecting the artificial (Architectuur van het ontwerp) ................................................24
Architecture: Hierarchy .......................................................................................................... 24
Modularity and Consolidation ................................................................................................ 25
Hierarchical architectures: Lacunas ......................................................................................... 26
Design and engineering .......................................................................................................... 27
Change Ripples: (constraints of artefacts) ............................................................................... 27
Is this inevitable? ................................................................................................................... 28
A new hope: .......................................................................................................................... 28
Are we there yet? .................................................................................................................. 28
Biomimetics: A Design Science Paradigm to build complex adaptive systems ...................28
Biomimetics: Definitions ........................................................................................................ 28
Examples: .............................................................................................................................. 28
Why biomimetic design? ........................................................................................................ 29
Biomimetic design guidelines ................................................................................................. 30
Artifact-task-environment ...................................................................................................... 30
The sciences of the artificial ................................................................................................... 31
Design Science ....................................................................................................................... 33
The engine of complexity ....................................................................................................... 33
Evolving adaptive/purposeful artifacts ................................................................................... 35
The NK model ........................................................................................................................ 35
Enhanced optimization on rugged landscapes ......................................................................... 37
Departmentalizing ................................................................................................................. 37
Evolvability ............................................................................................................................ 38
Modularity............................................................................................................................. 39
, The evolution of modularity ................................................................................................... 40
Design Science ....................................................................................................................... 43
Organized complexity ............................................................................................................ 43
Organized complexity = hierarchy? ......................................................................................... 43
Hierarchy = evolvable? ........................................................................................................... 44
Evolvable complex artifacts .................................................................................................... 44
Application of Normalized Systems to modular architectures ..........................................45
Artifact production as part of modular design ......................................................................... 45
On artifacts, Modularity, and Production ....................................................................................................46
Modules and product variants .....................................................................................................................46
Building the artifact variants ........................................................................................................................47
On the rationale to separate modules .........................................................................................................47
Changing the module variants......................................................................................................................48
On the Rationale to Encapsulate Modules............................................................................... 48
Modularity and building artifacts .................................................................................................................49
Modular design and production ...................................................................................................................49
Design directives grounded in combinatorics .......................................................................... 50
Modular design and production ...................................................................................................................50
Modular design guidelines ...........................................................................................................................50
On the Rationale: ..........................................................................................................................................51
Combinatorics of Modular Architectures .....................................................................................................51
Combinatorics of Modular Architectures .....................................................................................................54
On the impacts of modular Re-Use ..............................................................................................................55
Coupling and Cohesion Reinterpreted .........................................................................................................56
The integration of additional concerns ........................................................................................................56
Combinatorics of coupling between concerns .............................................................................................56
Cross-cutting concern integration architectures ...................................................................... 57
Design Directives for Modular Architectures ...............................................................................................57
Integrating Additional Concern Dimensions ................................................................................................57
Architectures for Integrating Concerns ........................................................................................................57
1. Embedded Dedicated Integration.......................................................................................................57
1B. Embedded Standardized integration .....................................................................................................58
Emergence of centralized concern modules ................................................................................................58
2. Relay to dedicated framework............................................................................................................58
2B. Relay to Standardized Framework .........................................................................................................58
2C. Relay to framework gateway .................................................................................................................59
2B – Relay to Standardized Framework ................................................................................... 59
Architectures for Integrating Concerns ........................................................................................................59
Guideline 1: Encapsulation ...........................................................................................................................59
Guideline 2: Interconnection ........................................................................................................................60
Guideline 3: Downpropagation ....................................................................................................................60
The Emergence of Elements – Software ......................................................................................................61
Hierarchy and Downpropagation – Housing ................................................................................................61
Integrated Elements and the Integration Design Matrix .......................................................... 62
Cross-Cutting Concern Integration Architectures ........................................................................................62
The Concept of Elements – NS Software ......................................................................................................62
Toward Elements – Hexagonal HiveHaus .....................................................................................................62
Toward Elements – Current Smart Phone ....................................................................................................63
Toward Elements – Representation Framework ..........................................................................................63
Definition:
Engineering is the application of scientific, economic, social, and practical knowledge in
order to design, build, maintain, and improve structures, machines, devices, systems,
materials and processes. It may encompass using insights to conceive, model and scale an
appropriate solution to a problem or objective. The discipline of engineering is extremely
broad, and encompasses a range of more specialized fields of engineering, each with a more
specific emphasis on particular areas of technology and types of application.
Three fundamental human interests:
Engineering
- The engineer, and more generally the designer, is concerned with how things ought
to be.
- Scientists study the world as it is. Engineers create the world that has never been.
- Our mission is to contribute to society. It is always about making things better.
- People think of me as an investor and a businessman. But I’m an engineer.
- Some men see things the way they are, and ask ‘Why?’
I dream of things that never were, and ask ‘Why not?’
Some quotes from famous engineers.
,Technology
The fundamental purpose of technology/engineering is to contribute to human prosperity
and well-being by inventing artifacts and techniques.
• to increase productivity through leverage effects:
- Fishing: manual -> spear -> fishing net
- Transport: walking -> chariot -> car
- Digging: manual -> shovel -> excavator
- Computing: paper -> calculator -> computer
- Communicating: courier -> letter -> message
• to support and improve life in general:
- Treatments and medicines to cure patients
- Information systems to consolidate knowledge
True realization of these effects requires:
1) Scalability of production:
a. No huge efforts
b. No scarce resources
c. No rare capabilities
d. Technical complexity of manufacturing
2) Sustainability of production:
a. No depletion of recourses
b. No production of poisonous substances
c. No jeopardizing of health
d. Technical complexity of maintenance
Engineering
3 fundamental human
interests
,Technology and Economy
• Introducing new technologies will lead to undesired transition effects
o Existing forms of work become superfluous
o Existing products become redundant
• Fundamentally, technologies increases wealth
o More returns for the same effort
o Less effort for the same returns
o Combination of both
• Society must compensate for the transition effects
o Redistribution wealth
o Conversion workers
Economy
Studies the production of goods and services, the creation of value and wealth, and their
distribution.
➔ Importance is attached to concepts such as efficiency, economies of scale, and
division of labour
• Joel Mokyr argues that the origins of modernity lie in the emergence of a belief in
the importance of progress, and that it was a turning point when intellectuals began
to see knowledge as cumulative. However, he also outlines three explanations for
why societies are resistant to new technologies.
• Yvan Van De Cloot argues that smarter organization of the economic fabric and the
innovations within it has ensured our current prosperity. The real source of
prosperity lies in scientific breakthroughs and innovations, which are based on
knowledge, research and development. Crucial, however, is the insight that a new
technology sometimes takes decades to realize its impact.
Economy and Innovation
People sometimes think technology just automatically gets better every year, but it doesn’t.
It only gets better if smart people work like crazy to make it better.
― Elon Musk
Before the financial crisis, people thought that we could have a stable thriving economy
based on financial services.
― Tim Minshall
The best minds of my generation are thinking about how to make people click ads. That
sucks.
― Jeff Hammerbacher
,Science, Models, and Paradigms
(On the basics of science and the scientific method, and the pitfalls of self-fulfilling models
and rusted paradigms)
Quote: “Trust is the antithesis of the scientific method”
The Scientific Method
1. Observe a phenomenon
2. Find patterns in observations
3. Develop fitting descriptions and/or equations: these will be called models or
hypotheses
4. Conduct experiments to verify to what extent the models are able to predict future
observations
5. If the model/hypothesis predicts multiple observations successfully, it will become a
law or scientific theory
The characteristics of models
▪ Are a description, a simplification of reality
▪ Fundamental laws describe, do not explain reality
▪ Appeal preferably to intuition
▪ As simple as possible, i.e., Ockham’s razor
▪ Need to remain stable with respect to new data
▪ Are in general valid within certain boundaries
▪ Should be able to predict future observations, both through extrapolation and
interpolation
Observation and modelling
,Modeling
Laws of nature
➔ Are fundamental models
➔ They describe and do not explain
➔ Very thoroughly tested
➔ Can be (partially) falsified, never fully verified
➔ Are only valid within certain boundaries
➔ Newton’s Gravitation Law → General Relativity Theory
➔ Can be superseded by ‘better’ models that
➔ provide more accurate predictions
➔ remain valid in a broader range or scope
➔ Are essentially differential equations
Examples:
- Gravitation, Ohm’s law, mass conversation, …
Philosophy of science
With a scientific explanation we already know the phenomenon being explained has
occurred, whereas with a scientific prediction we don't.
<->
Empirical verification = derived from or relating to experiment and observation rather than
theory. Based on practical experience rather than scientific proof.
The problem of induction is a philosophical problem that arises when trying to justify the
claim that a certain pattern or regularity observed in the past will continue to hold in the
future. The problem is that there is no logical or mathematical proof that can guarantee that
a pattern or regularity observed in the past will continue to hold in the future.
One way to approach the problem of induction is to accept that we cannot be certain about
the future, but that we can still make reasonable predictions based on past observations.
This approach is known as Hume's fork, named after the philosopher David Hume, who first
proposed it. Hume argued that while we cannot be certain about the future, we can still
make reasonable predictions based on past observations. However, this approach raises the
question of what makes a prediction reasonable, and how we can decide which predictions
are more likely to be true.
, Another approach to the problem of induction is to try to find a way to logically or
mathematically prove that certain patterns or regularities will continue to hold in the future.
This approach has been pursued by many philosophers and scientists, but so far no
satisfactory solution has been found. The problem of induction remains one of the most
debated and enigmatic problems in philosophy and science.
Ockham’s razor: also known as the principle of parsimony, is a philosophical principle that
states that, when presented with multiple explanations for a phenomenon, one should
choose the explanation that makes the fewest number of assumptions. The principle is
named after William of Ockham, a 14th-century English logician and Franciscan friar, who is
credited with being one of the first to formulate the principle.
Ockham's razor is often used as a heuristic (a mental shortcut) in scientific and philosophical
reasoning to help guide the search for the most likely explanation for a phenomenon. The
principle is based on the idea that, all other things being equal, simpler explanations are
more likely to be true than more complex ones. This is because simpler explanations are
easier to understand, test, and communicate, and because they are less vulnerable to being
falsified by new evidence.
Ockham's razor is not a hard and fast rule, and it is not always possible or desirable to
choose the simplest explanation for a phenomenon. In some cases, more complex
explanations may be necessary to adequately explain a phenomenon, especially when the
phenomenon is itself complex or when there is a large amount of data to be explained.
However, Ockham's razor is still a useful tool for guiding inquiry and decision-making in
many contexts.
Paradigms: Thomas Kuhn was a philosopher of science who is best known for his concept of
the "paradigm shift." In his book "The Structure of Scientific Revolutions," Kuhn argued that
the development of scientific knowledge does not follow a linear, cumulative process, but
rather occurs through periodic revolutions, or "paradigm shifts."
According to Kuhn, a paradigm is a shared set of assumptions, concepts, values, and
methods that defines a particular scientific field and that provides the framework for normal
scientific research. Paradigms are more than just theories or models; they are
comprehensive frameworks that shape how scientists view the world and how they go
about investigating it.
Kuhn argued that, within a given paradigm, scientists work to solve problems and to test
and confirm the assumptions and theories of the paradigm. However, over time, anomalies
and inconsistencies may arise that cannot be explained within the existing paradigm. When
these anomalies accumulate to a sufficient degree, they may trigger a paradigm shift, in
which a new paradigm emerges to replace the old one.
Kuhn's concept of the paradigm shift has had a significant impact on the way that many
people think about the nature of scientific progress and the role of theories in science.
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