Summary: A new presentation by Nobel Laureate Paul Krugman discusses economics. His insights apply broadly to sciences playing a key role in public policy, especially climate science. Let’s hope others learn from it.
Slide #1 from “What have we learned since 2008“, a presentation by Paul Krugman at CUNY, 19 February 2016. He is discussing economics, but these insights have powerful implications for the public policy about climate science — and the increasing number of other policy issues relying on scientific evidence.
Some annoying propositions:
- Complex econometrics never convinces anyone.
- “Complex” includes multiple regression.
- Natural experiments rule.
- But so do surprising predictions that come true.
Economics is a less-mature science than climate science, but is in some ways more developed. Their literature has superior standards for transparency (e.g., requiring archiving of methods and data). More importantly, economics has far more experience working with political decision-makers and the public. So what might be Krugman’s advice to them? We have only his slides, not his transcript; this is my interpretation of them.
“Complex econometrics never convinces anyone.
‘Complex’ includes multiple regression.”
“The criterion of the scientific status of a theory is its falsifiability, or refutability, or testability.”
— Karl Popper in Conjectures and Refutations: The Growth of Scientific Knowledge(1963).
Climate models are complex engineering code analogous to econometric models. Both have a core of hard science on which are built a web of assumptions and approximations. For climate models, their foundation is basic physics but their implementation of physical, chemical, and biological processes are tuned parameterizations.
Models are powerful research tools for scientists, but their use in major public policy debates requires higher standards of validity. Non-scientists have a century of experience evaluating the utility of scientists’ findings on matters where the costs and stakes are high. Our hard-won skepticism about such theories requires observational proof, not just abstruse calculations. For more about the policy use of models see…
- About models, increasingly often the lens through which we see the world.
- We must rely on forecasts by computer models. Are they reliable?
- A frontier of climate science: the model-temperature divergence.
- Do models accurately predict climate change?
“Natural experiments rule.”
“Probably {scientists’} most deeply held values concern predictions: they should be accurate; quantitative predictions are preferable to qualitative ones; whatever the margin of permissible error, it should be consistently satisfied in a given field; and so on.”
— Thomas Kuhn in The Structure of Scientific Revolutions(1962).
There is a common language of evidence between scientists and the public: proof through accurate predictions. Where possible, these are trump in the policy debate, validation that a theory provides a basis on which public policy can be reliably be made.
Oddly, there is little interest by climate scientists in producing these. Instead they prefer increasingly complex hindcasts which prove that models can be tuned to predict the past. This is part of a larger problem. Climate policy has gridlocked in large part due to scientists’ failure in the debate to follow the public’s expectations, such as those described in Robert K. Merton’s famous essay “The Normative Structure of Science” (1942) — or Popper’s insights that theories must be falsifiable (see the hostile comments at Professor Curry’s website).
Scientists’ response to the failure of the policy debate has been to double-down on their intransigence. Such as in the Orwellian-titled “Research integrity: Don’t let transparency damage science” by Stephan Lewandowsky and Dorothy Bishop in Nature, 25 January 2016 — They “explain how the research community should protect its members from harassment, while encouraging the openness that has become essential to science.” It’s folly to expect the public to trust scientists’ findings arrived at in secret processes.
Perhaps worse is this by words of Marcia McNutt: “The time for debate has ended. Action is urgently needed.” She speaks as a senior leader of US science, as editor-in-Chief of Science and the only candidate on the ballot for President of the National Academy of Sciences at their May election (said in the July 3 issue of Science).
“The game of science is, in principle, without end. He who decides one day that scientific statements do not call for any further test, and that they can be regarded as finally verified, retires from the game.
… Those among us who are unwilling to expose their ideas to the hazard of refutation do not take part in the scientific game.”
― Karl Popper in The Logic of Scientific Discovery
(1934).
“But so do surprising predictions that come true.”
In a sense, predictions are the milestones in the history of science, both failed predictions that undermined the current dominant paradigm (e.g., the Michelson–Morley experiment) and successful predictions that help establish new paradigms (general relativity’s prediction about the orbit of Mercury).
What are surprising predictions? Karl Popper said they were the strongest kind of evidence…
“Confirmations should count only if they are the result of risky predictions; that is to say, if, unenlightened by the theory in question, we should have expected an event which was incompatible with the theory — an event which would have refuted the theory.”
— Karl Popper in Conjectures and Refutations: The Growth of Scientific Knowledge(1963).
“Risky” or “surprising” predictions are not the only useful kind. Just as predictions are not the only kind of evidence validating theories. Yet they are great value when debating recommendations for large-scale policy decisions about existential threats to the nation or the world.
“Thus an extraordinary claim requires “extraordinary” (meaning stronger than usual) proof.”
— By Marcello Truzzi in “Zetetic Ruminations on Skepticism and Anomalies in Science“, Zetetic Scholar, August 1987. See the text here.
Conclusions
Krugman gives his conclusions at the end of the presentation…
What is the post-2008 experience trying to tell us?
- Liquidity-trap economics passes with flying color.
- Fiscal policy effectiveness confirmed.
- Monetary iffy at best.
- Neo-paleo-Keynesian aggregate supply in short run.
- Long run seems to reinforce, not diminish, that case.
He evaluates three theories of economics relative to analysis of the 2008 recession and the recovery: two proved accurate, one far less so. His admission of monetary policy’s “iffy” effectiveness shows remarkable candor, since criticisms of monetary policy (including mine) were long treated as outré by mainstream economists like Krugman.
The last two bullets refer to intramural disputes among economists. See these columns by Krugman about neo-paleo-Keynesian economics: “The Neo-paleo-Keynesian Counter-counter-counterrevolution” and “On the Neo-paleo-Keynesian Phillips Curve“.
Here we see one reason that economics, for all its limitations as an immature science, has become so influential in public policy debates: the willingness of its leaders to frankly assess their findings in the light of experience, and criticize them own work. Krugman and Brad DeLong (Professor of Economics at Berkeley) routinely do so — not just in technical language before their peers, but in easy-to-understand terms for the public at their websites.
This is something rarely seen in climate science, despite the gravity of the policy debate in which it plays such a key role.
Other posts about the climate policy debate
- Thomas Kuhn tells us what we need to know about climate science.
- How we broke the climate change debates. Lessons learned for the future.
- Climate scientists can restart the climate change debate – & win.
- Daniel Davies’ insights about predictions can unlock the climate change debate.
- Karl Popper explains how to open the deadlocked climate policy debate.
- Coming: Gavin Schmidt and Steven Sherword explain the policy gridlock.
- Coming: Why the policy debate is deadlocked. How we can restart it.
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