If AEI’s revenue from Exxon was 5% of their total, and, say, they got another 5% from Big Tobacco, and another 5% from the arms manufacturers etc…

Does it follow that any report they write on smoking is not influenced by their funding, simply because they received also received money from Exxon - while SIMULTANEOUSLY arguing that their position on fossil fuels is not influenced by Exxon, because they also received money from Big Tobacco?

Surely they don’t mean that their detractors think they solicit donations from Big Tobacco to write positive spin for arms manufacturers? Or solicit donations from Microsoft to pay for criticisms of gun control laws?

It seems to me the ‘bad guys’ are one big team - they know it, hence the need for think tanks, lobby groups to provide the figleaf.

I know the retort - “The AEI like Liberty and Freedom. So do Big Tobacco, the Oil Majors etc…hence they are generous with their funding…what’s so amazing about that?”

It denies that Think Tanks are just an empty shell, a vessel to pour money into and to employ lobbyists and disgruntled academics. They have no raison d’etre other than their role to channel corporate dollars into spun words.

Think Tanks need money - the ’supply’ comes from Bad Business.

What product is ‘demanded’ in return by Bad Business? Biased reports, research, what more can they deliver to their backers?

Remember a Think Tank is not a grassroots committee meeting at someone’s kitchen table, or painting the placards in the backyard.

It is not the ’second nameplate’ on some academic’s office door.

It is an expensively staffed organisation, which could not survive on goodwill or orthodox research funding.

I agree AEI hasn’t blown the ‘transparency’ angle as such, their detractors will point out their crime when this ‘research’ is quoted in future.

What worries me more is what this ‘research’ is used for.

It will get demolished in the NY Times, but what about years later when some hick Lib/Nat Candidate from Dubbo is quoted in the Rural Times at the CWA meeting saying that “Wasn’t it proven that Climate Change was a hoax/communist plot/satanic influence?”

If they are genuinely interested in the objective truth, there has to be a chance that this will happen

If it was a grant for original research, and the AEI cared about reality, they wouldn’t tell their writers what to write to in advance. They may commission them to examine the IPCC and then publish what ever conclusions come out of it.

More generally, while a scientist may hope their hypothesis is correct, they should never cherry pick data too support it. That way lies creationism, HIV denial and the “smoking doesn’t cause cancer” tobacco researchers.

That sort of rubbish might be fine for employees of think tanks and other advocacy bodies, but for scientists, it’s disgraceful.

As DD points out, it’s simply shilling for vested interests, and consequently, has absolutely no place in science.

I think we need Rafe to give us some quotes from the history of science, but there isn’t anything wrong with starting from a conclusion (imagined or based on real data) and seeing whether your model can predict it. This is *good* not *bad* science. There are essentially two ways models can be tested:

1) We can see whether our model predicts observable behavior
2) We can see whether our our model predicts behavior that isn’t observed.

The second of these is equally as important as the first, since it tells us whether our model is over-parameterized i.e., can simply fit any pattern. This is essentially what you are doing by saying “Given model X, is there a way to reach conclusion Y”. There may be no way of doing this (e.g., “there is no way of reaching the conclusion global warming does not exist based on the data”), which is good to know, since it tells us that our model is not overparameterized. If you are using distributional models, you may end up with something like (”Based on our model, the chance of global warming not occuring is ridiculous negligible”).

I realize that the current debate is more political, but in many areas there are sophisticated methods for determining what a model can and can’t predict where you are basically trying to find the borders of the predicatble function. This is done via the generation of representative points from the *entire* possible state space and then you see whether the models parameters allow them to be predicted. I don’t see why the question at hand is any different to this. A good example of this comes from people looking at the discount rate in the Stern report. This is basically a free parameter within a certain range. However, apart from fairly idiotic estimates of the where range ends, you find out that doing something about global warming is worthwhile. If we had asked a question like “Given we don’t want to pay anything now, what is the best way to attack the Stern Report”, we would have found out that the model *cannot* predict doing nothing is the best alternative. This is because we have worked out what the model *cannot* predict via testing on data points from the entire possible state-space.

There is in fact a third way that we might like to test our model. In case some of the global-warming deniers happen to come up with a model that doesn’t show warming versus just I don’t believe it ranting, then we can compare that model with the more reasonable ones proposed. In this case, we could look at the overlap in the distribution of data that each model is predicting, and then see how well the non-overlapped predictions relate to real data.

Also, the number of climate scientists out there in the public sphere advocating greenhouse policy prescriptions suggests that many scientists have problems maintaining a strict separation between positive and normative positions.

To take an example closer to home, it doesn’t bring me great comfort that John Quiggin is not paid to come to a particular conclusion. Everyone who is familiar with his work knows (and he advertises) that he comes from a left wing perspective. His readers bear this in mind and consider his work on its merits. Why can’t you apply the same approach to the work of privately-paid scientists?

In fact, I would argue that the minute someone’s background or funding matters more than their black and white output is the minute science dies.

I also don’t see anything wrong with paying for a conclusion. This is done all the time. In less controversial areas, we wouldn’t think twice about accepting it (e.g., Please find me the solution to mathematics problem A; Please tell my why theory X leads to symptoms Y). If the result of that is some poor quality research that then gets used by a government with an entire team of people thinking of policies and evaluating the research (as is the case in Australia), or, for that matter, poor quality work that wasn’t sponsered, then surely the attribution of blame should either go on the government for incompetence in interpreting all of the results from what amounts to a huge body of work, or for corruption, for deliberating using dodgey work.

This is an attempt to muddy the waters, not clarify them. It’s all too typical of the denialist’s tactics, which have already cost us dearly in lost time (the later we address the problem, the tougher the action needed).

To use your analogy, you appear to be emulating the kind of leftist who used the term “McCarthyists” to apply to anyone who criticised organisations as Communist fronts even when they were in fact Communist fronts.