Lorenz, 1966 (link)
Held, 2006 (link)
Group notes (link)
Jimmie's summary (link)
Combo Polya checklist (link)
David's in class notes
Comments on Held:
There are a lot of simple models because they are used for a lot of different purposes.
As tools/machines/data advance, you should re-evaluate your problem to see how you can move further along in understanding.
Use of MIPS:
• Sometimes the results are simply tuning. [Individual models are tuned to observations.]
• Sometimes the MIPS result in the identification of systematic biases.
Isaac’s hypothesis/model for improving comprehensive models.
Build elegant model to understand and this knowledge will give a more
efficient path to improving the comprehensive models. Problems:
• are you sure the idealized models relevant to the real world?
• Are the results of the idealized models used to confront the models? Why or why not?
• Is the enterprise too difuse to progress
efficiently in the skill of simulation and in building knowledge of the
system (ie, constructing a theory of climate)?
• Should all simple models be designed to confront the comprehensive models?
• Simple models can be used to understand, or to constrain ….
• Simple models can be used to shape, define, articulate ideas …
To move the modeling progress forward, do you need new administrative or programmatic elements or incentives for scientists?
Building knowledge using this give and take approach with comprehensive
models and conceptual models is time consuming and people intensive.
How can you afford to do it, in practice?
Perhaps all conceptual models should have disclaimer statements and
honest assessments of limitations/potential show-stoppers…
If you have only a few conceptual models that are going to be the
research horses, then who defines what these models will look like?
Isn’t it important to test simple models against each other?