Ken,
Thank you for your comments.
In case you haven't come across it....
A standard method of creating predictive models of complex situations with unknown behaviours is to identify causal relationships, create the model, then test the model for boundary conditions and behaviour over time correspondence, then identify what (sparse) data is needed to calibrate the model, then, and only then, collect the small amount of calibration data and undertake the calibration. Prior to identifying causal relationships is to address practical aspects of the model/theory structure and what is manageable and useful. This enable pre-theory/modelling decisions to be made. This prior analysis is where my question was aimed.
This well-established conventional theory/modelling approach appears to be completely different from your positivist way of seeing the world.
When I view your posts, you seem to be on one hand pushing either a data driven a-theoretical approach of the kind found in some areas of business (gather lots of data and make a theory about it, regardless of whether the theory is causally robust **), or the classical scientific approach of focusing on a single factor and gathering data to identify the functional relationship in scientific terms
With respect, I suggest that neither approach is that useful or relevant in design and design research.
Instead, I suggest what is needed is theories that provide useful predictions of behaviour that are grounded in, and derived from, well-established and justified theories from other fields and then are calibrated on the basis of reference data relevant to the design instance; rather than attempting to create theories derived from their own data.
There are several reasons for preferring the above route and avoiding deriving theories direct from data, including:
1. Design decisions involve multiple causal relations across a variety of disciplines
2. Design theory development specific to individual design instances must be undertaken fast
3. From experience, going the 'theory derived from data' approach is too expensive, takes too long and the results are typically too specific and ephemeral.
** Given any data there is no limit to the number of different theories that can be made to describe the data and relationships between it. Many will be nonsense. A test is whether a proposed theory comports well with existing well justified theories in other fields... which means you might as well just start there, before gathering the data....
Regards,
Terence
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Dr Terence Love
PhD(UWA), BA(Hons) Engin. PGCEd, FDRS, PMACM, MISI
Love Services Pty Ltd
PO Box 226, Quinns Rocks
Western Australia 6030
Tel: +61 (0)4 3497 5848
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www.loveservices.com.au
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-----Original Message-----
From: [log in to unmask] [mailto:[log in to unmask]] On Behalf Of Ken Friedman
Sent: Sunday, 21 February 2016 5:45 PM
To: PhD-Design <[log in to unmask]>
Subject: Predictive Models
Dear Terry,
Your post on predictive models or theories is worth a quick note. It contradicts your description of mathematical theory based only on analysis and reasoning.
You write, “The real challenge is to look at possible approaches to creating design theories that predict design outcomes as the consequences of design in the world.”
Your approach has nothing to do with the world.
In the recent thread on colour, you used a question to bait the hook. The question was not a search for information. Your replies show that you had already reached a conclusion. You used the responses to your query to announce already-formed opinions. Through the entire thread, you paid no attention to anything people had to say. Rather than attend to the issues or arguments, you seize on a phrase here or there to launch into your next disquisition.
It is a form of misplaced Platonism to believe that you can create a mathematically accurate predictive model of anything in the absence of data on one side of the modelling process and testing on the other.
If you believe that “The real challenge is to look at possible approaches to creating design theories that predict design outcomes as the consequences of design in the world,” you should not neglect the significant issues that people raise.
In your replies to Klaus, you ducked out of your claims on colour to argue that you thought it would be useful to work with ideas on colour as a test case for “possible approaches to creating design theories that predict design outcomes as the consequences of design in the world.” But you’re not interested in looking at the world. You are looking at “the realms of non-linear n-order calculus, which on one hand offers potential benefits in identifying optimal positions in m-dimensional design space.”
You have never demonstrated a single case to show that you have working mathematical models “that predict design outcomes as the consequences of design in the world.” These posts reminds me of theoretical descriptions of perpetual motion machines — such a machine may be possible in a perfectly isolated system, a pure Platonic world of forms disconnected from the real world. A working model is impossible in the real world subject to thermodynamics. Most design works on the scale of human interaction. On this scale, your approach is equally problematic.
It would be refreshing if you were to demonstrate your approach by publishing some working results.
If all the rest of us were equally content with describing imaginary outcomes, we could end world hunger, cure cancer, and reverse catastrophic climate change by “identifying optimal positions in m-dimensional design space.”
Ken
Ken Friedman, PhD, DSc (hc), FDRS | Editor-in-Chief | 设计 She Ji. The Journal of Design, Economics, and Innovation | Published by Tongji University in Cooperation with Elsevier | URL: http://www.journals.elsevier.com/she-ji-the-journal-of-design-economics-and-innovation/
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
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