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HOW TO GET A PH.D.:
Methods and Practical Hints I-II (2011(2011-2012)
Aarne Mämmelä, 20.10.2011
http://www.infotech.oulu.fi/to_phd
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Research methods: systems approach
Aarne Mämmelä
The systems approach, which is also called the holistic approach, is a
complementary approach to the conventional analytical approach
described in an earlier session. It is not meant to replace the
analytical approach. The systems approach started to develop only in
the 1950’s, including systems analysis and systems engineering. It is
not yet at all complete due to challenges met. A system is a set of
parts so related as to form a whole with a certain purpose. Most manmade systems are hierarchical and modular with a minimum number
of interfaces between modules. The fundamental system resources
include material, energy, and control information. (Continued)
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Research methods: systems approach
Aarne Mämmelä
(Continued) The aim of a designer is to use those resources as
efficiently as possible. It is known that optimization of separate parts
does not necessarily result in an optimal system. The performance is
usually measured by functionality, reliability, convenience, and price.
Systems are difficult to analyze because of the many interacting parts
with many nonlinear feedback loops. On each hierarchy level there
are some emergent properties that come from the interactions and
they cannot be easily explained. Some deterministic systems are even
chaotic unless they are designed carefully. The design of systems is
not completely sequential but highly iterative. Some examples of
fundamental problems will be briefly described. A chronology of
scientific and engineering discoveries is briefly presented to
demonstrate the importance of the history in improving of our
awareness of our work.
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Summary of my lectures
A doctor must be able to discover new scientific knowledge
independently (lecture 1)
Existing knowledge in the literature is best found through
bibliographies, citations, and databases (lecture 2)
All documents are written using a hierarchical top-down
approach (lecture 3)
The actual research is a learning process where the opposite
bottom-up approach (analytical approach) is used (lecture 4)
Also top-down approach (systems approach) can be useful to
foster creativity and support learning (lecture 5)
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Scientific Method
Note: the term ”analytical approach” refers to the breaking up part of the
scientific method, the term ”bottom-up approach” to the combining part of
the scientífic method
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Newton’s Method of Analysis and Synthesis
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Axiomatic System
Some Philosophy: Plato’s Allegory of the Cave
http://faculty.washington.edu/smcohen/320/cave.htm
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Research methods: systems approach
Introduction
Limitations of analysis
Science and technology
Definition of a system
Reductionism and holism
Measurement of performance
Basic resources
Analysis, simulations and experiments
Order and hierarchy
Fundamental limits and open problems
History and roadmaps
Researcher and organization
Conclusions
References
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Introduction
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Introduction
Methodological Approaches and Research Methods
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Introduction
Systems approach (top down approach, holistic
approach) is complementary to the analytical
approach.
Whole is more than a sum of its parts. Interactions
between parts create emergent phenomena.
Everything depends on everything.
This is especially true for nonlinear systems which
cannot be separated into independent parts.
Integrating studies are necessarily crude.
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Analytical and Systems Approach
In the analytical approach the relationships between parts are assumed to be
weak and simple
In the systems approach relationships are taken into account (emergence)
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Balloon in a Car
What happens to the helium balloon?
Limitations of analysis
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Limitations of Analysis
I. Newton 1687
L. E. Boltzmann
1884
K. F. Sundman 1912
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Limitations of Analysis [Bertalanffy99],
[Checkland99], [Bohm00]
World is fundamentally stochastic (as in quantum mechanics), but for
engineering purposes it looks deterministic and causal although
sometimes chaotic if studied carefully enough [Nagel79], [GellMann94].
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Levels of Awareness
[Skyttner05], [Arbnor97], [Checkland99]
Man-made systems work on the lowest syntactic level (relationships
between symbols)
Engineers have not been able to tell to a computer the relationship of
symbols with reality (= meaning) in the semantic level.
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Science and technology
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Technology: Natural Science and
Engineering
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Sciences, Practices, and Technology
[Wilson99], [Schwanitz99]
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Definition of a system
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What a System is [Hall62]
System is a set or arrangement of things so related or
connected as to form a unity or organic whole (for example,
the solar system)
a set of parts with relationships between the parts and
between their properties
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Set--member and partSet
part-whole relations [Honderich05]
A whole consists of parts, properties, relations, and events.
A process is a series of changes with some unity or unifying principle
(time is the dimension of change).
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Classification of Systems
Systems are divided into two classes [Pagels88]
Hierarchical systems (serial organization, most engineering systems)
Network systems (parallel organization, there is no top and bottom,
much redundancy, for example brain)
Our analytical tools are not valid in complex systems [Bertalanffy98],
[Pagels88]
Properties of a system depends on the parts and their relations
(“everything depends on everything”, for example three-body problem in
physics, two-body problem corresponds to a feedback system)
Complex systems may have a chaotic behavior
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General Properties of Systems [Burrell79]
System is identified by a boundary between the system and its environment
System is essentially a process in nature
Basic model includes input, throughput, output, and feedback
Operation can be understood in terms of satisfaction of system needs
System is composed of subsystems
Subsystems have their own boundaries and have mutual interdependence
Operation can be observed in terms of the behavior of its elements
Critical activities are those which involve boundary transactions internally or
externally
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Reductionism and holism
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Reductionism and Holism
[Checkland99], [Honderich05]
reductionism: theory that every complex phenomenon can be explained
by analyzing the simplest, most basic physical mechanisms that are in
operation during the phenomenon (scientific approach)
holism: theory that whole entities have an existence other than as the
mere sum of their parts (systems approach)
emergence: occurrence of properties at higher levels of organization
which are not predictable from properties found at lower levels
[Nagel79], for example transparency of water, temperature of a gas,
life, consciousness
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Example: System Theories in Communications [Wolfram02],
[Checkland99], [Bertalanffy98], [White76], [Nau08]
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Measurement of performance
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Performance [Bock01], [amc03], [VIM]
Performance metric (criterion) is a function (for example meansquare error) of a quantity whose output is performance
Performance is the numerical value of the performance metric, to be
compared with the performance requirement
Performance requirement is desired performance value that
describes some user need
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Trueness, Precision, and Accuracy, [amc03],
[VIM]
Trueness (small
systematic error)
Precision
(small random
error)
Accuracy
(small total
error)
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Uncertainty [amc03], [VIM]
Each statistical measurement result should be presented with three
numbers: 1) average, 2) confidence interval, and 3) confidence level
[Kreyszig83], these numbers represent the uncertainty of the measurement
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Accuracy and uncertainty [VIM]
Accuracy “closeness of agreement between a measured quantity value and a true
quantity value of a measurand”
Trueness “closeness of agreement between the average of an infinite number of
replicate measured quantity values and a reference quantity value”
Precision “closeness of agreement between indications or measured quantity values
obtained by replicate measurements on the same or similar objects under specified
conditions”
Uncertainty “non-negative parameter characterizing the dispersion of the quantity
values being attributed to a measurand, based on the information used”
Reference quantity value: “quantity value used as a basis for comparison with
values of quantities of the same kind”, “a reference quantity value can be a true
quantity value of a measurand, in which case it is unknown, or a conventional
quantity value, in which case it is known”
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Basic resources
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Conversion of materials, energy, and information
(signals) [Pahl07], [Asimov74]
Three basic flows in any system include materials, energy, and
information flow
Systems are roughly divided into three groups (apparatus, machine,
device) depending on the main flow
Engine is a machine that takes energy from some material (burning
or flowing) and turns it into work in the form of rotating motion
Motor a machine that takes energy from electricity and turns it into
work in the form of rotating motion
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A Basic Resource: Energy
Origin of energy: nuclear energy (sun, ground heat)
Energy conservation law: energy is constant in the
universe
Entropy law: available useful energy is reduced all the
time (it will never increase)
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A Basic Resource: Material
Origin of materials: nature
Conservation of mass: mass is constant in the universe
(actually energy and mass are conserved)
Entropy law: materials can be partially reused
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A Basic Resource: Information
Origin of information: human brain?
Information is an immaterial resource
Information flow refers to essentially the control
information [Checkland99]
Information flow is using energy (what about bottle
post)
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Performance, Complexity, and Resources
High performance and low complexity
are closely related problems but
somewhat conflicting aims
High performance (priorities,
throughput, reliability, delay, etc.)
implies efficient use of resources
Low complexity (number of parts and
their relations) implies efficient use of
resources
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Resources are Converted into Properties (1)
[Checkland99], [Honderich05]
Performance is the manner in which something reacts or fulfils its
intended purpose, measures the efficiency in using the basic resources
Complexity refers to the number of parts and their relations, measured by
the amount of used resources
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Resources are Converted into Properties (2)
[Checkland99], [Christensen03], [Honderich05]
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Analysis, simulations, and
experiments with prototypes
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Analysis, Simulations, and Experiments (1)
Analysis
Simulation
Prototype
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Analysis, Simulations, and Experiments (2)
1. Mathematical analysis (theoretical model)
creates best scientific papers
simple, mathematically tractable problem, must be often linear and random
variables must be Gaussian (numerical results needed)
2. Simulations (numerical analysis of the theoretical model)
complicated systems can be developed rapidly, but slow to simulate
basic idea: lower level blocks are simplified and idealized (hierarchy)
key problem: realistic models for the environment (e.g. channel)
3. Prototyping (empirical research)
more convincing than “pure” simulations, not so flexible, slow and expensive
to develop complicated systems
environment (channel) simulators still needed (approximations!), field tests
expensive, repeatability/reproducibility?
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Building a Prototype
Build the prototype from complete components
as high integration as possible
Build a focused prototype instead of comprehensive prototype [Ulrich95]
demonstrate the novel properties
Build a scaled prototype [Honderich05]
macroscopic or microscopic (miniature) model
example: use 100 Hz instead of 1000 Hz (time scaled down by ten, spatial
dimensions scaled up by ten)
Build a virtual prototype
simulation models are time-scaled models
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Order and hierarchy
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Order: Sequential Process [Bohm92]
Different forms of order include 1) timeless order (e.g. crystal structure), 2)
sequential order, and 3) generative order
Sequential process is characterized by a regular sequence of events
[Bohm92], [Honderich05]
In project design this kind of process is called waterfall model where the
project is divided into rather independent phases [Calvez93], [Leppälä03]
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Order: Generative (Iterative) Process [Bohm92]
Iterative or generative process is an overall process from which the manifest
form of things emerges creatively, internal interrelations are included,
especially iterations
In project design this kind of process is called spiral model: the whole
process is repeated and each time the result improves [Calvez93], [Leppälä03]
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V Model (V Cycle) [Calvez93]
Different Hierarchies [Calvez93], [Simon96]
Description Levels
Nested Hierarchy
Formal Hierarchy
(layered pattern,
successive layers)
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Fundamental limits
and open problems
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What is Guiding Our Work
Systems
engineering
History &
roadmaps
Fundamental
limits
System models,
relationships,
complexity analysis
Reviews of literature
Physical limits,
optimal systems,
performance analysis
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Fundamental Limits (about 18501850-1950)
conservation of mass-energy (Lavoisier, Joule, Einstein)
entropy law (Carnot, Clausius)
absolute zero (Kelvin) – lowest temperature
upper velocity limit (Einstein) – velocity of light
uncertainty principle (Heisenberg)
incompleteness theorem (Goedel) – axiomatic systems
cannot be made complete (there are theorems that cannot
be proven)
speed of transmission of information (Nyquist, Shannon)
Refs. [Lundheim02], scienceworld.wolfram.com
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Some Fundamental Engineering Problems
Sun
Information
Energy
Energy
Nature
Materials
Energy
Factory
Information
Products/
Services
Waste
Waste
People
People
Problems: Distribution of information, energy, materials, products, and
services, transportation of people, waste management, etc.
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Some Open Problems in Engineering
General theory of systems (philosophy of technology, hierarchy
theory) [Checkland99]
Semantic information theory (Shannon’s statistical
information theory does not cover the meaning of the
information, only the amount of information) [Checkland99]
Network information theory (statistical information theory
covers only isolated links) [Cover91]
Frame of reference problem (how a machine could decide the
frame of reference or context) [Honderich05]
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Why are Some Design Problems
Difficult to Solve? [Michalewicz04]
no single performance metric that describes the quality of any proposed
solution is available, but a set of performance metrics that should be
weighted
the number of solutions in the search space is so large as to forbid an
exhaustive search for the best answer and the iterative methods (by
trial and error) are too slow or unreliable to find the optimum solution
the possible solutions are so heavily constrained that constructing even
one feasible answer is difficult (reduction is used to simplify the problem
and this adds an additional constraint)
the performance metric is noisy or varies with time (need an entire series
of solutions)
our models may be too simplified so that any result is essentially useless
some psychological barrier prevents us from discovering a solution
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History and roadmaps
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Example: History of Telecommunications
[Haykin01], [Proakis01]
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Cultural History [Boyer91]
Legends (see previous page)
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Roadmap and Vision (for details of roadmaps, see
[Kostoff01])
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Tele-Immersion
Tele(graphics.cs.brown.edu/research/telei/home.html)
We define tele-immersion as that sense of shared presence with distant
individuals and their environments that feels substantially as if they were
in one's own local space.
This kind of tele-immersion differs significantly from conventional video
teleconferencing in that the user's view of the remote environment changes
dynamically as he moves his head.
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Researcher and organization
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Researcher and Organization
ROLE OF ORGANIZATION
ROLE OF RESEARCHER
1. History and state of the art
2. Vision and roadmap
3. Fundamental principles and
problems
4. Research problems and projects
Problem
Intial data
collection
(literature review)
Tentative solution
(hypothesis)
5. Marketing, recruiting, investing
6. Project plans
7. Research culture and education
Analysis/
simulations/
experiments
System model
(prototype)
Theory/paper
(new knowledge)
8. Integration of results
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Structure of a Typical Research Project
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Researcher’s Work
Positive: Intellectual pleasure [Feynman99], thrill, new
knowledge, prestige [Hicks99], spirit of the scientific community:
special research culture, freedom to think, suspect and criticize
authorities, impersonal judgments of discoveries, integrity (=
honesty) [Jain97], unique communication network [Jain97].
Negative: Jobs only in certain places, jobs may be temporary, work
includes raising of funds, quality of work not straightforward to
measure (citations have many weak points), sometimes rather
narrow topics, guidance may be difficult to find, not all people
understand the value of criticism but it is rejected
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Conclusions
Conclusions (1)
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Conclusions (3)
use bottom-up (inductive) approach in research, which is
essentially a learning process
use top-down (deductive) approach in technical
documents (reviews, monographs), this will make the
presentation compact and easy to follow for experts (use
IMRAD structure)
use bottom-up approach in teaching (tutorials, textbooks), and
integrate results by using the top-down approach
remember that a doctoral thesis is not a textbook (writing a
textbook is a large challenge), write the thesis for experts in
the field
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Summary of my lectures so far
General hints
use bibliographies to improve your efficiency in
literature reviews (start from books and reviews,
see the introduction of original papers)
learn the terminology, write a classification
(taxonomy) for the state of the art, and see
historical trends
define a problem and hypotheses (use bottom-up
empirical-inductive approach, make
experiments early in your project)
start to outline the paper right from the beginning
(there will never be “more time”), emphasize good
organization, use top-down deductive
approach in documents
reserve time for all phases in your project plan
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References (1)
[amc03] “Terminology – the key to understanding analytical science. Part 1:
Accuracy, precision and uncertainty,” AMC Technical Brief, September 2003
(www.rsc.org/pdf/amc/brief13.pdf).
I. Arbnor and B. Bjerke, Methodology for Creating Business Knowledge, 2nd ed.
Sage Publications, 1997.
I. Asimov, Words of Science and the History behind Them. London: Book Club
Associates, 1974.
P. Belliveau, A. Griffin, and S. Somermeyer (Eds.), The PDMA Toolbook 1 for
New Product Development. John Wiley & Sons, 2002.
W. Bennis and P. W. Biederman, Organizing Genius: The Secrets of Creative
Collaboration. Addison Wesley, 1998.
L. von Bertalanffy, General System Theory: Foundations, Development,
Applications, revised ed. New York: George Braziller, 1998.
P. Bock, Getting It Right: R&D Methods for Science and Engineering. Academic
Press, 2001.
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References (2)
D. Bohm and F. D. Beat, Science, Order and Creativity. Bantam Books, 1987.
C. B. Boyer, A History of Mathematics. Wiley, 2nd revision edition, 1991.
G. Burrell and G. Morgan, Sociological Paradigms and Organizational Analysis.
Ashgate, 1979
J. P. Calvez, Embedded Real-Time Systems: A Specification and Design
Methodology. John Wiley & Sons, 1993.
P. Checkland, Systems Thinking, Systems Practice: A 30-Year Retrospective.
Wiley, 1999.
C. M. Christensen, Innovators Dilemma. HarperBusiness, 2003.
T. M. Cover and J. A. Thomas, Elements of Information Theory. Wiley, 1991.
R. P. Feynman, The Pleasure of Finding Things Out. Perseus Publishing, 1999.
D. D. Gajski, Silicon Compilation. Addison-Wesley, 1988.
M. Gell-Mann, The Quark and the Jaguar. W. H. Freeman and Company, 1994.
A. D. Hall, A Methodology for Systems Engineering. Princeton, NJ: D. Van
Nostrand Company, 1962.
S. Haykin, Communication Systems. 4th ed. Wiley, 2001.
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References (3)
D. Hicks, “Six reasons to do long-term research,” Research and
Technology Management, pp. 8-11, July-August 1999.
T. Honderich (Ed.), The Oxford Companion to Philosophy, 2nd ed. Oxford
Univ Press, 2005.
R. K. Jain and H. C. Triandis, Management of Research and Development
Organizations: Managing the Unmanageable. John Wiley & Sons, 1997.
G. Klir, Facets of Systems Science, 2nd ed. Springer, 2001.
R. N. Kostoff, “Science and Technology Roadmaps,” IEEE Transactions on
Engineering Management, vol. 48, pp. 132-143, May 2001.
E. Kreyszig, Advanced Engineering Mathematics, 5th ed. John Wiley &
Sons, 1983.
K. Leppälä et al., Professional Virtual Design of Smart Products. IT Press,
2003.
Lars Lundheim, “On Shannon and ‘Shannon’s Formula’,” Telektronikk, vol.
98, no. 1-2002, pp. 20-29, www.tu-ilmenau.de/site/mt/uploads/media/
shannonLarsTelektronikk02.pdf.
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References (4)
Z. Michalewicz and D. B. Fogel, How to Solve It: Modern Heuristics, 2nd ed.
Springer, 2004.
E. Nagel, Structure of Science: Problems in the Logic of Scientific
Explanation. Hackett Pub Co, 1979.
R. Nau, BA 513: Ph.D. Seminar on Choice Theory, Fall 2008. Lecture notes,
Duke University, Durham, NC, available at
faculty.fuqua.duke.edu/~rnau/choice/.
H. Pagels, The Dreams of Reason: The Computer and the Rise of the Sciences
of Complexity. New York: Simon and Schuster, 1988.
G. Pahl, W. Beitz, J. Feldhusen, and K. H. Grote, Engineering Design: A
Systematic Approach, 3rd ed. Springer, 2007.
J. G. Proakis, Digital Communications. 4th ed. McGraw-Hill, 2001.
D. Schwanitz, Bildung: Alles was man wissen muss. Frankfurt, Germany:
Eichborn, 1999.
H. A. Simon, The Sciences of the Artificial, 3rd ed. MIT Press, 1996.
L. Skyttner, General Systems Theory, 2nd ed. World Scientific Publishing
Company, 2006.
A. S. Tanenbaum, Computer Networks, 3rd ed. Prentice Hall, 1996.
K. Ulrich and S. Eppinger, Product Design and Development. McGraw-Hill,
1995.
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References (5)
[VIM] International vocabulary of metrology — Basic and general concepts
and associated terms (VIM)
(www.bipm.org/utils/common/documents/jcgm/JCGM_200_2008.pdf).
D. J. White, Fundamentals of Decision Theory. North-Holland, 1976.
E. O. Wilson, Consilience: The Unity of Knowledge. Random House, 1999.
S. Wolfram, A New Kind of Science. Wolfram Media, 2002.
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Recommended Reading
P. Checkland, Systems Thinking, Systems Practice: A 30-Year
Retrospective. Wiley, 1999.
A. D. Hall, A Methodology for Systems Engineering. Princeton,
NJ: D. Van Nostrand Company, 1962.
L. von Bertalanffy, General System Theory: Foundations,
Development, Applications, revised ed. New York: George
Braziller, 1998. (Originally published in 1971.)
E. O. Wilson, Consilience: The Unity of Knowledge. Random
House, 1999.
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VTT creates business from
technology
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