Knowability and No Ability in the Earth and Climate Sciences



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Knowability and No Ability in the Earth and Climate Sciences

ESS 590, ATM 588

Spring '06: JHN 377, Flint-Washburn library, 2.30 Wednesdays

We're sending this email now because we'd like to make a running start on a seminar class/meeting we're planning next quarter. We'd thought of you all as being particularly interested in the topic (and some of you likely to disappear soon from UW). We'd really like to keep the meetings small so that discussions are fluid, but if you think of other good people, do please let them know.
Beginning with somewhat beer-soaked origins, we've recently been trying to think about what makes for a good problem in our field. Why are some questions more tractable than others? How do you identify a good problem in advance? Are there common elements that can be identified in different fields? Apart from a slightly groan-worthy title, what we will do for the class is not very well defined yet. Some of the specific questions we'd like to ponder:
-Why are some problems and hypotheses more likely to lead to enlightenment (or to the reduction in ignorance), while others are more likely to further obscure the truth? How does one construct a hypothesis that has the intrinsic property of knowability?
-What are the roles of intuition and experience/deduction in formulating a question that is knowable when it is probed using scientific reasoning?
-When do models build knowledge? What types of models are most influential in shaping the way we think? Are they the same models that keep the scientific invesigation on the pathway to truth?
-How does one avoid working on a problem that "dies when the investigtor dies" (Michelangelo)?

If these questions seem like they'd be interesting to sit around a table and cogitate on, let us know, and one thing we'd like you to start thinking about is a paper, or papers, that you've found to be good examples of elegant approaches to important problems. By starting on this now, we hope to build up a series of case studies we can all explore together and gain from everyone else's experiences and ideas.

It is not clear we will be able to come up with concrete or world-shattering answers, but we do think these are important questions to think about. Attached below are some more thoughts resulting from a mixture of caffeine and hops.
Cheers,
David and Gerard

Outline

.


  1. Introduction

  2. Background Material: Popper, Kuhn and Company

  3. Background: cont.

  4. Models

  5. Models and the Complex Polya Checklist

  6. Models in Climate Sceince

  7. Abrupt Climate Change, Part I

  8. Abrupt Climate Change, Part II

  9. A Complex Polya List for Problem X

  10. Summary


Readings

Week 1 and 2: The Basics: Popper and Kuhn and commentaries
Thornton, Stephen, 2005: “Karl Popper”. In “The Stanford Encyclopedia of Philosophy” Edward Zalta, Ed. (online at http://plato.stanford.edu/entries/popper ).
Bird, Alexander, 2005: “Thomas Kuhn.” In “The Stanford Encyclopedia of Philosophy”, Edward Zalta, Ed. (online at plato.stanford.edu/archives/spr2005/entries/thomas-kuhn )
Popper, K. “Normal Science and its dangers”. In “Criticism and the Growth of Knowledge”. Ed. Imre Lakatos. Cambridge University Press. 1970 pp 51-58.

Lakatos, Imre. (1970). Excerpts from “Falsification and the methodology of scientific research programmes”. In Criticism and the growth of knowledge (pp. 91-196). Lakatos & A. Musgrave (Eds.), New York: Cambridge University Press.

Published online under the title “Science as Successful Prediction”, http://www.stephenjaygould.org/ctrl/lakatos_prediction.html


Week 3:
Brush, Stephen G., 1974: “Should the history of science be rated X?” Science, 183, 1164-72.

McComas, William F., 1998: The principle elements of the nature of science: dispelling the myths. In “The Nature of Science in Science Education. W.F. McComas (ed), Pg 53-70.


Feyerbabend, P. Consolations for the specialist. In “Criticism and the Growth of Knowledge”. Ed. Imre Lakatos. Cambridge University Press. 1970. pg 197-229.
Week 4&5 Models and the Complex Polya Checklist:
Levins, Richard, 1966: “The Strategy of Model Building in Population Biology”. American Scientist. Pp 421-431. I recommend reading 421-23, and 430-31 for sure, with the rest being optional.
Sections 3-5 of “Models in Science”. Frigg, R. and Hartmann, S., 2006. "Models in Science", The Stanford Encyclopedia of Philosophy (Spring 2006 Edition), Edward N. Zalta (ed). Online at plato.stanford.edu/archives/spr2006/entries/models-science
Polya, G., 1957: "How to Solve It". Princeton University Press, 2nd ed. An outline of his strategy is found here: www.math.utah .edu/~pa/math/polya.html

Week 6: Models in Climate Science
.Lorenz, E.N., 1966: The General Circulation of the Atmosphere. American Scientist, 54, 407-20.

Held, I.M., 2005: The gap between simulation and understanding in climate models. Bull. Amer. Met. Soc., 1609-14.

Week 7: Abrupt Climate Change, Part I
Week 8: Abrupt Climate Change, Part II
Week 9: Complex Polya Checklist for Problem X


Other readings – notes:
Chapter 5 of “Logik der Forschung (The Logic of Scientific Discovery), by Karl Popper. Springer Verlag, Vienna, 1934.
Induction vs Hypothesis testing:

“The Problem with Induction”, by David Hume.


How scientific knowledge evolves:

“The Nature and Necessity of Scientific Revolutions” . Chapter 10 of “The Structure of Scientific Revolutions” by Thomas Kuhn.


Lakatos, I. (1970). Falsification and the methodology of scientific research programmes. In I. Lakatos & A. Musgrave (Eds.), Criticism and the growth of knowledge (pp. 91-196). New York: Cambridge University Press.

Published online under the title “Science as Successful Preditcion”, http://www.stephenjaygould.org/ctrl/lakatos_prediction.html



Some extra thoughts (made before we started the course);

What is science?

A body of knowledge? A process? A culture – an agreement for how to build knowledge?
Science vs. engineer? Both are problem solvers.
Is normal science just problem solving – going for the low-hanging fruit. Science provides a methodology for evaluating which of two hypotheses are farther from the truth, and helps illuminate anomalies …
Popper seems to outline a method for a mature science (or for a well defined system) for getting closer to the truth (the process by which we build knowledge).

Pre-science: perhaps we can’t falsify things, but we are building a body of information to hone hypotheses. What additional

Pre-science and science both tell stories about how the world works. In the case of science, the stories are analogies based on a knowledge we are reasonably confident (through tests and time) is likely to be on the right track.

A pre-science tells stories on weaker foundation. For the latter, how do you get closer to the truth? How do you make sure you are systematic way? What is the systematic way?


Do we do science the way we report science?
Is a mature science hallmarked by theories that are of lower dimensionality than what they are intended to explain. And by theories that make surprising predictions, that can be (and eventually are verified).?
What if we thought of science as a conscious striving to define falsifiable hypothesis? This would include science and a prescience.
If Kuhn is just reporting on the ‘description’ of changes in understanding, how can you be sure that you are any closer to the truth? What if you have settled into only one attractor / ‘truth well’ , and are still far from the truth?
Will the same field undergo multiple revolutions where basic understanding is shown to be wrong?
Can a complex system be understood scientifically?

Climate? Human body? Can you define a system of rules or culture (like the rules for science by popper) that help us move closer to the truth?



Before we meet:

Email students and have them bring papers that were particularly influential to them (or in their discipline)

-- are their common characteristics in methodology or presentation that make it a particularly powerful or persuasive work?

Some questions to address:

How do you evaluate whether a problem is tactable/doable?

-- goal: to minimize the risk of picking an intractable problem.

Examples of questions that are still out there that are not solvable/knowable.

Which questions are fundamental (aesthetics)? Which questions are profound (complexity)? And how do we know they are fundamental or profound (as opposed to influential)?

-- give examples of fundamental/profound questions. Have any fundamental/profound questions been solved?

Do fundamental/profound questions always lead to principles you can understand? Do they have to lead to something you can explicitly model?

Contemporary examples

Good examples: Origin of Species; Lorenz's 1963 paper: Deterministic nonperiodic flow; Hasselman's 1976 paper; Basic radiation (Eddington?, Tyndall?); Stommel's book "Gulf Stream"; Gaia (?)

Bad examples: Conveyor Belt; Bergen School; Fractals; Gaia (?)
Format:

Weekly summary by students (in writing).


What we had in mind before we began the course:

Week 1. The basics Popper, Kuhn and Lakatos.

Week 2. Levins (1966), Lorenz (1966), Polya(?); readings on the use of Models

Week 3. Held, McIntyre(?)

Week 4. (possible) case study – ENSO

Week 5. (possible) case study – Milankovitch

Week 6. (possible) case study – Global Warming

Week 6. (possible) Ocean thermohaline circulation. \

Week 7. (possible) Parameterizations (clouds, ocean mixing)

Excerpts that are particularly good.





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