Terry,
Your approach to theory and modelling is misguided. You are proposing useless short cuts to avoid the problems of real research.
Deriving theory from data is expensive and time consuming. Results are always limited until researchers accumulate enough cases to permit generalisation. That’s why it is difficult to say anything meaningful about the world, and that’s why every serious research field takes years to develop into something useful.
You seem to be saying that we can apply mathematical models from other domains to design whether they work or not. Finding out whether they work costs too much, takes too much time, and the results are always too limited to justify the work. There are no short cuts for research.
The problem with medical research is expense and time — it costs too much, it takes too long, and much of what we learn only helps a few patients. Progress is slow and painful.
If, on the other hands, people took your approach in medical research, medicine would not be making slow, painful progress. There would be no progress at all. When people simply borrow inappropriate mathematical models from other fields, models can work whether or not patients die.
Ken
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Terry Love wrote:
—snip—
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….
—snip—
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