This document discusses theoretical models used in ordinary research to understand phenomena, and artefactual models used in design research to develop artifacts that control targeted outcomes. Theoretical models use vocabulary, rules, and generalizations to describe and predict behaviors, which are tested against real-world observations. Artefactual models describe how artifacts will behave based on relevant theoretical models and untested assumptions, and are tested through simulations and empirical tests. Both aim to improve models and control the world, with design research focusing on developing useful artifacts.
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some thoughts about design research
1. Some thoughts on design
research, models and
simulations
Peter Sloep
Friday, May 4, 12
2. Overview
Part I: ordinary research, the essentialia
Part II: design research, the differences
Part III: models
Friday, May 4, 12
3. Caveat
A quick and dirty introduction to some
aspects of the philosophies of science and
technology
Sometimes less than accurate, sometimes
plain wrong, but always in an attempt to be
useful.
Friday, May 4, 12
6. Theoretical models
smoke therapy
Ia Ib
S1 S2 S3
healthy Ic cancer dead
therapy
Friday, May 4, 12
7. for a model to be useful, you need
descriptive apparatus, vocabulary
rules for how to use it, grammar, syntax
rules that uses the vocabulary and syntax
to describe possible model behaviours
Friday, May 4, 12
8. rules about possible behaviours are
interesting
they are generalizations, subsume instances
of model behaviour
form: whenever X then Y
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9. all kinds of vocabularies and syntaxes
math.: diff. equations, 鍖nite automata,
probability calculus
computer languages: Stella, Netlogo
ordinary language (but formal languages
are much more powerful because they
allow inferences from axioms)
Friday, May 4, 12
10. What use are models?
Are the generalizations true of the world?
you test a model
predict future behaviour, check if it
occurs
yes: more con鍖dence
no: adapt or fully reject model
Friday, May 4, 12
11. A word on statistics
some models are stochastic, their
behaviour is stochastic
but all data are subject to chance
variation; you use statistics for the latter,
to quantify uncertainty in your decision
to accept the model or reject it
Friday, May 4, 12
12. What use are models?
Are the generalizations useful in the world?
you explain the world by using model
generalizations on phenomena
you control the world by using the
model to predict phenomena
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13. Design research,
focused on artefact
design for control
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14. artefacts are designed and developed to
help control the empirical world
there always are desired phenomena, the
artefact causes them to occur
there thus is a targeted state of the world
and an obtained state of the world
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16. building an aeroplane to 鍖y
setting up an organization (bank) to lend
money to investors
building a learning network to facilitate
competence development, creativity and
knowledge sharing of non-formal learners
Friday, May 4, 12
17. Artefactual models
As the artefact is to perform a function,
you now build a model that describes the
artefacts behaviour, using bits and pieces of
relevant theoretical models
Such a model I call an artefactual
model, to distinguish it from theoretical
models
Friday, May 4, 12
18. Test
to the extent it incorporates theoretical
models, con鍖dence carries over from them
to the extent it rests on untested
assumptions (structural and parametric)
you test it
experts, independent tests
you thus contribute to theor. model dev.!
Friday, May 4, 12
19. Simulate
Use the artefuactual model to explore the
artefacts behaviour through simulations
Empirical tests are costly, dangerous,
unethical, etc.
Simulations are pseudo-performance tests
of the artefact
They may lead to design improvements
Friday, May 4, 12
20. Empirical test
Carry out empirical tests of artefact
performance
effectiveness - gap between actual and
desired behaviour
ef鍖ciency - costs of obtaining desired
performance level increase
sustainability - costs of maintenance
Friday, May 4, 12
22. Model ingredients
1. identify the system and its boundaries: draw
a causal network
2. identify variables and constants
3. identify state variables (laws of succession,
variables that describe state transitions)
and and input variables (variables that drive
change)
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23. 1. choose output variables, what changes you
want to measure (could be a state variable)
2. choose a suitable modelling language
analytic, using math. equations, often
differential equations
numerical, using computers, dedicated
language, e.g. Netlogo, Stella, Brahms, ...
Friday, May 4, 12