Dear Klaus,
There is a need for care in epistemological analysis.
There are some standard analyses is about the possibilities of a theory to
exist.
One example is identifying a bound that shows that particular theory,
approach or set of theories cannot produce a useful result. An example of
this is a theory about a time changing situation that doesn't have time as
a major variable.
Another example, is a situation in which no bounds have yet been identified
to prove that a particular body of theories cannot exist. In which case, the
claim that that body of theories exsit remains valid until proven otherwise.
A third example, is a contradiction that proves that a theory as it is
proposed is false (the black swan contradiction). An example was the theory
that all swans are white was shown to be false as a result of the discovery
of Western Australian black swans.
I use the first example, to suggest that the outcomes of the DesignX
initiative cannot satisfy its aims.
The second example, indicates that predictive modelling for human behaviors
and all complex situations is proven to date.
The third example, can be used to show that human thinking does not depend
on words.
As an aside, another example, human behaviour designing posts on this list
is very predictable. I suggest given a quick topic over view using
simplistic data mining will indicate to a reasonable level of accuracy when
yourself and Ken will post to the list, the topics you will post on, and the
arguments you will present in that post - regardless of the apparent
randomness of the content, the timing and the nature of your lives,
emotions, learning and state of thinking.
You seem (along with Ken ) to be asking me to be non-judgmental. If you
mean personal attack, I think you will find my posts contain less personal
attack than many others. If you mean commenting that something appears
incorrect in something someone else writes, then how would you suggest a
critical argument should proceed? If you mean I'm guilty of being judgmental
because I sometimes write about theories in ways that contradict deeply held
beliefs or experiences of others and that upsets or embarrasses them, then
no that is something that I will not change.
Interestingly, criticising me for being judgmental is in itself might also
be seen as a judgmental personal attack? Perhaps you and Ken are correct,
and the evidence is there of being judgmental. In that case, there is
learning for me. You may be correct or you may be not, but to ensure you
are, evidence is necessary. Please could you describe examples of me being
unfairly or incorrectly judgmental about people? That should give you
freedom and permission for a significant personal attack? Let us see how you
go.
Best regards,
Terry
---
Dr Terence Love
PhD(UWA), BA(Hons) Engin. PGCEd, FDRS, AMIMechE, MISI
Director,
Love Services Pty Ltd
PO Box 226, Quinns Rocks
Western Australia 6030
Tel: +61 (0)4 3497 5848
Fax:+61 (0)8 9305 7629
[log in to unmask]
--
-----Original Message-----
From: [log in to unmask]
[mailto:[log in to unmask]] On Behalf Of Klaus Krippendorff
Sent: Saturday, 6 December 2014 1:20 AM
To: PhD-Design - This list is for discussion of PhD studies and related
research in Design
Subject: design theory
terry
in addition to ken's calling on you to be specific and non- judgmental
regarding those who have experiences/perspectives other then you proclaim:
you (and you are not alone) always insist on valid and predictive design
theory. can you articulate one, making clear the domain it theorizes, what
it predicts, and the nature of the evidence that would validate or lead us
to reject that theory with some confidence?
my suspicion is you can't.
please prove me wrong.
klaus
Sent from my iPhone
> On Dec 5, 2014, at 11:54 AM, Ken Friedman <[log in to unmask]>
wrote:
>
> Dear Terry,
>
> In your posts to this thread [1, 2 below], you criticise the rest of us
for taking an approach that is essentially old-fashioned and unworkable.
You¡¯ve said something like this quite often, in different words but the
same meaning. Our methods represent the past. Your methods of quantitative
modelling represent the future.
>
> You claim to have a method for modelling design in a predictable way by
accounting for complex dynamic systems with multiple loops of action and
behaviour.
>
> You have never demonstrated this method. You haven¡¯t explained it or
described it. You simply assert that the method exists. Starting from the
premise that this method exists, you use logic to derive conclusions. These
conclusions would be true if and only if your premises are correct. Without
the method, your premises are incorrect and the conclusions are false.
>
> List members have asked you for examples and evidence that allow others to
examine and test your claims. You do not provide these. Instead, you
occasionally offer unworkable or irrelevant examples. Some are quite
memorable. In one thread, you offered an unpublished and invalid paper on
14th-century Byzantine history. You stated that the paper was an example
that modelled complex dynamic systems with multiple loops of action and
behaviour. An engineer and computer scientist who works with the methods
described in the paper showed that the paper was methodologically inadequate
and empirically incorrect. In another case, you offered Hermann Hesse¡¯s
novel Magister Ludi as an example. Magister Ludi is fiction.
>
> I am skeptical to the claim that anyone can model complex dynamic systems
in design. If you have modelling methods for design that make responsible
predictions, let¡¯s see them.
>
> You have repeatedly stated that any design student can learn to model
complex dynamic systems. I am even more skeptical to this claim.
>
> Modelling complex systems is a challenge to senior physicists,
neuroscientists, and mathematicians. This kind of work requires fluent
mathematical skills that relatively few people in any field can master. I
can¡¯t see how undergraduate students or master¡¯s students can hope to do
so. When asked to show how undergraduate and master¡¯s students can master
these skills, you have not done so.
>
> You state that there is no need to support your claims with evidence. You
argue that logical argument is sufficient to demonstrate your claims. This
is not the case when you use the word ¡°reality.¡± ¡°Reality¡± refers to an
empirical world of human interaction, natural systems, and physical systems.
This requires more than logic. The claim that something is so in the world
requires evidence.
>
> Two issues affect these repeated debates. The first issue involves the
relation between theory and evidence. Questions and hypotheses come from
theory. Only evidence allows us to determine which theories and hypotheses
are correct in reality.
>
> The second issue involves the relation between logic and useful design
solutions. An argument or solution may be logically valid without being true
or useful. From Aristotle to Pierre Abelard, from Lewis Carroll to the
present day, philosophers, logicians, and scientists have demonstrated that
false premises lead to logically valid but false conclusions.
>
> Is there radical new way of doing design and design research? If there is,
we need examples and explanations. In research and in design, we must be
able to examine and understand ideas to put those ideas to work. You¡¯ve
made these kinds of claims for nearly fifteen years without providing
evidence. It¡¯s my sense that most people no longer take your claims
seriously. From time to time, the arguments become so problematic that one
or another among us feels compelled to say something.
>
> There is no evidence for a mathematical system that can predictably model
design as a complex dynamic system with multiple loops of action and
behaviour. There is no evidence for a mathematical system that models design
activity any better than the admittedly problematic mix of research,
experience, and intuition we rely on today. Serious research can close the
gap between pure intuition and outcomes that are partially predictable. The
notion of comprehensive mathematical modelling for generally predictable
outcomes is beyond reach.
>
> There are sound reasons for this in behavioural science and physical
science both. In behavioural science and in economics, Friedrich Hayek
offered convincing arguments for the unpredictable nature of human behaviour
in social groups.
>
> It is also difficult to model complex adaptive systems in nature.
Physicists, mathematicians, and engineers have attempted to model many kinds
of complex adaptive systems with limited success. Neuroscientists and
biologists have done no better. It has taken years of patient work to
achieve minor advances and occasional breakthroughs.
>
> When we attempt to model human action and interaction, problems cascade.
One reason for this is the fact that human social systems are open systems.
These systems are open to exogenous influences. It is inherently impossible
to model the full system because there is no way to model all nodes and
connections in a social world that may at any moment influence individuals
and groups in unplanned ways.
>
> We can improve our models to do better than we have done in the past.
Predictable certainty is impossible outside the world of science fiction.
You would not be as certain as you seem to be if you worked in any research
field that does this kind of modelling. You don¡¯t. Researchers in those
communities continually publish their results. They test different models
and approaches. They debate their approaches, changing their approaches to
better approximate reality. No one who does this kind of work with
recognised results claims the level of certainty you claim ¡ª and no one
believes that they can teach this kind of modelling at a professional level
in an undergraduate program.
>
> Don Norman wrote [3 below], ¡°Designers who believe they know enough about
human behavior to predict all the results are simply delusional -- and
therefore dangerous.¡±
>
> The question is whether you can demonstrate robust predictive models that
account for human behaviour. Forget novels and links to unpublished papers.
If you cannot show the rest of us how to do this in design, your claims are
irrelevant to the design field.
>
> To state that other approaches than your own are bound to fail when you
can't demonstrate your approach is irresponsible.
>
> It¡¯s time to move beyond fictional examples. The challenge you face is to
show that your concept of mathematical modelling works in reality.
>
> Does it work in the real world of human interaction? Do you know something
that Friedrich Hayek didn¡¯t know? If the rest of us are to take your claims
seriously, we need to see working models. You¡¯ve got to show that your
quantitative methods work, and you¡¯ve got to show that designers can learn
these methods well enough in a standard design education to deploy them.
>
> In his article, ¡°How Do We Know that Albert Einstein was Not a Crank,¡±
Jeremy Bernstein (1993: 17-18) writes, ¡°I would propose two criteria to
help us distinguish between crank science and the real thing:
¡®correspondence¡¯ and ¡®predictiveness¡¯. ¡ I would insist that any
proposal for a ¡. radically new theory in [any science] contain a clear
explanation of why the precedent science worked. What new domain of
experience is being explored by the new science, and how does it meld with
the old?¡±
>
> Bernstein (1993: 20) continues, ¡°The crank is a scientific solipsist who
lives in his own little world. He has no understanding nor appreciation of
the scientific matrix in which his work is embedded. I would gladly read a
paper on perpetual motion which began by explaining why we had all been
overlooking something about entropy, something that makes a correspondence
with the science we know. In my dealings with cranks, I have discovered that
this kind of discussion is of no interest to them. If you find a specific
flaw in their machine, they will come back the next day with a new design.
The process never converges.¡±
>
> You are proposing a scientific approach to design rooted in quantitative
modelling and robust prediction. Bernstein¡¯s criteria apply to your
proposals. While Bernstein (1993) explains this in ways appropriate to
physics, the same issues apply to design and design research when you claim
that you can produce robust predictive models.
>
> Bernstein (1993: 20) goes on ¡°The second criterion that genuine science
should satisfy is predictiveness.¡± This requires demonstration. Bernstein
explains predictiveness in several pages of careful discussion. Bernstein¡¯s
full article is available for download from the Teaching Documents section
of my Academia page:
>
> https://swinburne.academia.edu/KenFriedman
>
> How do your claims measure up?
>
> Start with the criterion of correspondence. Many earlier approaches to
design and design research work reasonably well. While they are not perfect,
they function in the real world ¡ª they function in reality. Just as there
was some merit in the physics that led from Kepler and Galileo to Planck and
Einstein, there is some merit to many of the design approaches that expert
designers used for much of the 20th century. Despite problems and
imperfections, the projects of outstanding designers work reasonably well.
The best researchers have been reasonably successful. If your approach
cannot explain why this is so, there is no correspondence.
>
> Predictiveness is simple. While it is not easy to demonstrate
predictiveness, the nature of predictiveness is simple. Simply demonstrate
that a model can make accurate predictions. You have never demonstrated
this. You talk about predictions in much the same way that people talk about
Hesse¡¯s glass bead game. Describing the game doesn¡¯t make the game real.
Describing the logical consequences of an imagined but unreal model is not a
case of prediction: it is a case of false premises at work. Without a real
model that can operate in the real world of human action and interaction,
there is no predictiveness.
>
> Without correspondence and prescriptiveness, Albert Einstein could well
have been a crank. To be sure that someone is not a crank requires
explanatory correspondence and predictiveness in the real world.
>
> Most of the posts in this thread on clinical research and clinical
guidelines call for pragmatic inquiry, iterative development, rich evidence,
and sound theory. Is this approach perfect? No. Pragmatic inquiry and
iterative development in an open system are always subject to change. This
is necessarily the case when we design with and for human beings.
>
> Don describes this well: ¡°The good designer is humble, willing to observe
and learn ¡ª over and over and over again. (The same is true of a good
psychologist, or anthropologist, or scientist.)¡± Iteration brings action
from the real world into the design process. It works in design and design
research just as it does in science. Real research always discloses things
that surprise us.
>
> In a famous critique, the sociologist Herbert Blumer criticised the
inappropriate use of quantitative models in social and behavioural science.
This is the result of a perspective more concerned with specific research
methods than with the nature of the empirical world.
>
> Blumer (1969: 24) wrote: ¡°Today ¡®methodology¡¯ in the social sciences is
regarded with depressing frequency as synonymous with the study of advanced
quantitative procedures, and a ¡®methodologist¡¯ is one who is expertly
versed in the knowledge and use of such procedures. He is generally viewed
as someone who casts study in terms of quantifiable variables, who seeks to
establish relations between such variables by use of sophisticated
statistical and mathematical techniques, and who guides such study by
elegant logical models conforming to special canons of ¡®research
design¡¯.¡±
>
> Blumer (1969: 27) respected the ¡°obdurate character of the empirical
world.¡± He writes (Blumer 1969: 21) that ¡°an empirical science presupposes
the existence of an empirical world. Such an empirical world exists as
something available for observation, study, and analysis. It stands over
against the scientific observer, with a character that has to be dug out and
established through observation, study, and analysis. This empirical world
must forever be the central point of concern. It is the point of departure
and the point of return in the case of empirical science. It is the testing
ground for any assertions made about the empirical world. ¡®Reality¡¯ for
empirical science exists only in the empirical world, can be sought only
there, and can be verified only there.¡±
>
> To speak of reality is to speak about the empirical world. Design and
design research require a robust cycle for generative action in the real
world of human engagement. While this often functions on the case-by-case
basis, these cases are fundamental to conceptual progress. Progress enables
us to fill gaps in what we think, what we know, what we understand, and what
we can do. For effective design and design research, we must bridge those
gaps. This is the pathway to new insights and significant growth. This is
what we need for the discipline of design research, and this is what we need
for the field of design.
>
> A workable method for modelling design that accounts for complex dynamic
systems with multiple loops of action and behaviour in a predictable way is
as imaginary as the glass bead game in Magister Ludi.
>
> So far, there is neither correspondence nor predictiveness in your claims.
>
> Ken
>
> Ken Friedman, PhD, DSc (hc), FDRS | Editor-in-Chief | Éè¼Æ She Ji. The
> Journal of Design, Economics, and Innovation | Published by Elsevier
> in Cooperation with Tongji University Press | Launching in 2015
>
> Chair Professor of Design Innovation Studies | College of Design and
> Innovation | Tongji University | Shanghai, China ||| University
> Distinguished Professor | Centre for Design Innovation | Swinburne
> University of Technology | Melbourne, Australia
>
> Email [log in to unmask] | Academia
> http://swinburne.academia.edu/KenFriedman | D&I
> http://tjdi.tongji.edu.cn
>
> ¡ª
>
> Reference
>
> Bernstein, Jeremy. 1993. Cranks, Quarks and the Cosmos. New York: Basic
Books.
>
> ¡ª
>
> [1] Terry Love wrote:
>
> ¡ªsnip¡ª
>
> I feel there is a problem in what you wrote.
>
> Which do you mean? Are you suggesting the design outcomes are:
>
> 1. None-predictable with the analysis tools that are currently being
> used in that field; or, 2. *Intrinsically* non-predictable?
>
> I can see that the issues of custom and habit of design make the
dynamics design outcomes less easy to predict because in reality they are
not an accurate proxy for designs. I can also see that dynamically changing
contexts make the dynamics of outcomes less easy to predict. Ditto for
variants in users, their education, and the dynamics of their individual
learning trajectories.
>
> It also presents a potential problem if the evidence gathering, analysis
and modelling tools that have been used are only suitable for situations in
which the outcomes are a fixed state. (In which case, dynamic modelling
tools for design assessment are needed. The latter are widely available and
used in other areas of design than visual design and communications.
>
> All of these are issues of 'less easy to predict' rather than
'intrinsically non-predictable'
>
> None of it however is reason to suggest the dynamics of design outcomes
in the areas you are working are *intrinsically* non-predictable.
>
> It¡¯s a strong claim you make. I'd like to see your reasoning as to why
the claim that communication designs are *intrinsically* non-predictable
should be true.
>
> If it's not true, then all that is required for better prediction is to
use better methods of modelling and analysis.
>
> Of course that would mean more cost - but for a research institute, that¡¯
s often not a bad thing?
>
> ¡ªsnip¡ª
>
> --
>
> [2] Terry Love wrote:
>
> ¡ªsnip¡ª
>
> I feel, however, that form of thought experiment takes design and design
research even further down the wrong path.
>
> It¡¯s the kind of thinking that leads to unhelpful concepts such as
'wicked problems'.
>
> The simple reality is the methods of analysis common to design and design
research to date focus on fixed states and fixed outcomes.
>
> They don't work when contexts and outcomes are dynamically varying and
change over time. It's time to move on to methods that do.
>
> As I've written before, the answer for design research and design is to
take on board and use analyses and modelling that work when contexts and
outcomes are dynamically varying.
>
> This of course, requires a different skill set from what many designers
and design researchers are taught.
>
> The historic alternative - pretending anything with varying outcomes or
contexts is impossible - is a position of the past. Time to move on.
>
> Incidentally, this focus on fixed outcomes, and a couple of other reasons,
is why the approaches presented by DesignX are likely to fail.
>
> Simply, as a field we can do better.
>
> It involves doing design and design research differently.
>
> ¡ªsnip¡ª
>
> ¡ª
>
> [3] Don Norman wrote:
>
> ¡ªsnip¡ª
>
> ?I disagree?
>
> On Thu, Dec 4, 2014 at 3:00 AM, Karel van der Waarde <[log in to unmask]>
wrote:
>
> "One of the reactions afterwards questioned the value of testing, by
stating: ¡°A good designer would have predicted most of those test results
beforehand. Those results are not very surprising.¡± My answer was of course
fairly standard: ¡¯They probably are not surprising, but they provide
quantifiable results about some of the tasks. Those responses are vital to
check if you make any progress. And they confirm that the assumptions are
correct.¡¯ "
>
> ?I consider myself an expert on human psychology, with multiple decades of
experience in addition to a deep knowledge of theory and experimental
results (some of which I contributed).? Nonetheless, whenever I conduct a
test or do field observations, I always discover things that surprise me.
>
> The person who says ¡°a good designer would have predicted most of those
test results ¡¡± is simply showing their arrogance and lack of actual
experience.
>
> The good designer is humble, willing to observe and learn ¡ª over and
> over and over again. (The same is true of a good psychologist, or
> anthropologist, or scientist.)
>
> Karel is wrong by apologizing, trying to excuse the need for tests by
saying that they are valuable (only) to provide quantitative results. They
are valuable because they show weaknesses in the designs. Designers who
believe they know enough about human behavior to predict all the results are
simply delusional -- and therefore dangerous.
>
> ¡ªsnip--
>
> --
>
>
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