The discussion this week was really productive. Some questions, notes,
and comments are appended below. There were also a ton of really
insightful comments that people sent in, that we never got to talk
about in class. Do check them out - they are brilliant (doc).
We are light on readings for this week. Instead let's try and come up
with our equivalent to the Polya 'check list' for tackling problems in
climate science and Earth sciences. As several people pointed out, our
issues may be as much about choosing the right problem as they are how
to proceed in solving it. Feel free to interpret the task as loosely as
Please do draw from your own research experiences and fields. And give
it plenty of thought.
It will be interesting to see what the areas of overlap and differences
are. One goal of the whole class was to explore whether we could
identify ways of making our research more efficient in achieving an
understanding of messy systems. This exercise is a pretty concrete step
in that direction (not to mix metaphors).
Make sure to send everything to David (& me too) - I'll be in
Delaware next class.
So no new reading for this week, but we will come back to the following-
Figg - what the heck is a model anyway? (pdf)
Polya - how to solve it excerpts (pdf)
- How do you define (what are
the elements of) a good problem? What is a “good problem”?
- Are there a set of
principles that would make research/progress on a complex problem more
- Can we made a Polya list
for a complex problem? Draw off experience you have on your own
- Is it meaningful to break
it up a problem in a complex system into smaller problems and trust
when you glue it back together (either mathematically, physically or
mentally) you are going to be able to solving your problem (closer to
Please think about what would make for a good case study. We have 2
papers lined up about the atmospheric general circulation for the week
after next. But it'd be great if we can think of two or three more
problems that are examples of good (or bad) problems that we can look
at to cogitate what makes them good (or bad). We might pick examples
that have been answered, or also, as Justin M suggested, problems that
have not yet been answered. Can we apply our check-list (see above!) to
get some sense of their tractability. We need to avoid being too
exclusive or specialized in these case studies, so it'd be good to come
up with lots of possibilities we can pick from.
More random notes (from David):
Understanding & prediction
Culture: need to have a sense of self-criticism; full disclosure
CANT SEPARATE THE MODEL FROM THE PROBLEM.
Problem goal model/tool (could include many
types of models, as defined by Levins). Levins states: “A satisfactory
theory comes from a cluster of models”. But how sure can you be that
when you glue the submodels together – mathematically or mentally – the
model will actually be a good model/theory?
Model Problem goal (this approach could render
the “if I have a hammer, all problems are nails” syndrome); or short
ciurcut the though process to define what SET of tools are best for the
[As you refine your tools, are you still working on the initial
GOALS can be described as (by levins):
1. Generality – wide spread applicability
2. Precision – sacrifices realism for accuracy;
3. Realism – including all the details
For a given model, you are sacrificing something to gain something
If you started fresh (no models at your disposal), would you use the
same models to solve your problem?
Examples of models/problems we can discuss:
Weather Forecast Model (good example of problem -> goal -> tool)
Testing a hypothesis that comes from collecting paleo data (or any
other data). For example, D/O events -> CLIMBER -> hone
hypothesis -> is model appropriate, based on what you know? ->
next step taken ?
Cloud Resolving Model –