Separating the roles of theory and direct empirical evidence in belief formation: the examples of minimum wage and anthropogenic global warming

by VipulNaik4 min read25th Jun 201466 comments

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I recently asked two questions on Quora with similar question structures, and the similarities and differences between the responses were interesting.

Question #1: Anthropogenic global warming, the greenhouse effect, and the historical weather record

I asked the question here. Question statement:

If you believe in Anthropogenic Global Warming (AGW), to what extent is your belief informed by the theory of the greenhouse effect, and to what extent is it informed by the historical temperature record?

In response to some comments, I added the following question details:

Due to length limitations, the main question is a bit simplistically framed. But what I'm really asking for is the relative importance of theoretical mechanisms and direct empirical evidence. Theoretical mechanisms are of course also empirically validated, but the empirical validation could occur in different settings.

For instance, the greenhouse effect is a mechanism, and one may get estimates of the strength of the greenhouse effect based on an understanding of the underlying physics or by doing laboratory experiments or simulations.

Direct empirical evidence is evidence that is as close to the situation we are trying to predict as possible. In this case, it would involve looking at the historical records of temperature and carbon dioxide concentrations, and perhaps some other confounding variables whose role needs to be controlled for (such as solar activity).

Saying that your belief is largely grounded in direct empirical evidence is basically saying that just looking at the time series of temperature, carbon dioxide concentrations and the other variables can allow one to say with fairly high confidence (starting from very weak priors) that increased carbon dioxide concentrations, due to human activity, are responsible for temperature increases. In other words, if you ran a regression and tried to do the usual tricks to infer causality, carbon dioxide would come out as the culprit.

Saying that your belief is largely grounded in theory is basically saying that the science of the greenhouse effect is sufficiently convincing that the historical temperature and weather record isn't an important factor in influencing your belief: if it had come out differently, you'd probably just have thought the data was noisy or wrong and wouldn't update away from believing in the AGW thesis.

I also posted to Facebook here asking my friends about the pushback to my use of the term "belief" in my question.

Question #2: Effect of increase in the minimum wage on unemployment

I asked the question here. Question statement:

If you believe that raising the minimum wage is likely to increase unemployment, to what extent is your belief informed by the theory of supply and demand and to what extent is it informed by direct empirical evidence?

I added the following question details:

By "direct empirical evidence" I am referring to empirical evidence that  directly pertains to the relation between minimum wage raises and  employment level changes, not empirical evidence that supports the  theory of supply and demand in general (because transferring that to the  minimum wage context would require one to believe the transferability  of the theory).

Also, when I say "believe that raising the minimum wage is likely to increase unemployment" I am talking about minimum wage increases of the sort often considered in legislative measures, and by "likely" I just mean that it's something that should always be seriously considered whenever a proposal to raise the minimum wage is made. The belief would be consistent with believing that in some cases minimum wage raises have no employment effects.

I also posted the question to Facebook here.

Similarities between the questions

The questions are structurally similar, and belong to a general question type of considerable interest to the LessWrong audience. The common features to the questions:

  • In both cases, there is a theory (the greenhouse effect for Question #1, and supply and demand for Question #2) that is foundational to the domain and is supported through a wide range of lines of evidence.
  • In both cases, the quantitative specifics of the extent to which the theory applies in the particular context are not clear. There are prima facie plausible arguments that other factors may cancel out the effect and there are arguments for many different effect sizes.
  • In both cases, people who study the broad subject (climate scientists for Question #1, economists for Question #2) are more favorably disposed to the belief than people who do not study the broad subject.
  • In both cases, a significant part of the strength of belief of subject matter experts seems to be their belief in the theory. The data, while consistent with the theory, does not seem to paint a strong picture in isolation. For the minimum wage, consider the Card and Krueger study. Bryan Caplan discusses how Bayesian reasoning with strong theoretical priors can lead one to continue believing that minimum wage increases cause unemployment to rise, without addressing Card and Krueger at the object level. For the case of anthropogenic global warming, consider the draft by Kesten C. Green (addressing whether a warming-based forecast has higher forecast accuracy than a no-change forecast) or the paper AGW doesn't cointegrate by Beenstock, Reingewertz, and Paldor (addressing whether, looking at the data alone, we can get good evidence that carbon dioxide concentration increases are linked with temperature increases).
  • In both cases, outsiders to the domain, who nonetheless have expertise in other areas that one might expect gives them insight into the question, are often more skeptical of the belief. A number of weather forecasters, physicists, and forecasting experts are skeptical of long-range climate forecasting or confident assertions about anthropogenic global warming. A number of sociologists, lawyers, and politicians often are disparaging of the belief that minimum wage increases cause unemployment levels to rise. The criticism is similar: namely, that a basically correct theory is being overstretched or incorrectly applied to a situation that is too complex, is similar.
  • In both cases, the debate is somewhat politically charged, largely because one's beliefs here affect one's views of proposed legislation (climate change mitigation legislation and minimum wage increase legislation). The anthropogenic global warming belief is more commonly associated with environmentalists, social democrats, and progressives, and (in the United States) with Democrats, whereas opposition to it is more common among conservatives and libertarians. The minimum wage belief is more commonly associated with free market views and (in the United States) with conservatives and Republicans, and opposition to it is more common among progressives and social democrats.

Looking for help

I'm interested in thoughts from the people here on these questions:

  • Thoughts on the specifics of Question #1 and Question #2.
  • Other possible questions in the same reference class (where a belief arises from a mix of theory and data, and the theory plays a fairly big role in driving the belief, while the data on its own is very ambiguous).
  • Other similarities between Question #1 and Question #2.
  • Ways that Question #1 and Question #2 are disanalogous.
  • General thoughts on how this relates to Bayesian reasoning and other modes of belief formation based on a combination of theory and data.

 

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Show me someone who makes predictions of the future by "just looking at the data," and I'll show you someone who's using a theory but not admitting it.

Yeah, in the AGW case it sounds like the question's more like "to what extent is your belief the result of climate models, and to what extent is it the result of a linear regression model?"

Theory also influences what data you consider in the first place. (Are you looking at your own local weather, global surface temperatures, stratospheric temperatures, ocean temperatures, extreme weather events, Martian climate, polar ice, or the beliefs and behavior of climatologists, and over what time scales and eras?) See also philosophy of science since at least Kuhn on theory-laden observation: http://plato.stanford.edu/entries/science-theory-observation/

The difference should be framed as: are you using a theory developed by fitting known data, or a theory developed from first principles?

I strongly disagree. "Fitting" data is not a theory-neutral process. As khafra points out, if you just have two time series, you can do linear regression to see if they seem correlated, and make predictions based off that. But for this to work requires lots of assumptions - one might even call it a 'theory' - about the world. For how this can go wrong, see the pirate theory of global warming.svg).

Conversely, "first principles" as they exist in reality are usually grounded in experiment. This is most glaring in the case of climate models. What does their code implement? Conservation of mass? Experimental result. Heat transfer? Experimental result. Cloud formation? Experiment. Optical properties of gases, experiment, solar spectrum, experiment, black-body radiation, experiment, Earth's geography, experiment, seasonal cycles, experiment. This is all data! Using this data is just as much "just looking at the data" as linear regression.

Point taken, and I agree. I'll try to better formulate what I meant:

Some theories are developed using data about the system you want to study. E.g., past climate data.

And some theories are developed using data about other systems. Either similar but causally unrelated ones (e.g., greenhouse effect in an actual greenhouse), or models which are so simplified that there's a serious worry they may be too simplified to apply to the original system (e.g., black-body radiation). They also have the advantage that if they work on the system you want to study, then they let you explain it in terms of other things which you already understand.

On an abstract Bayesian level, they're all the same; we don't compartmentalize data about past climate from data about the optical properties of gasses. But for humans who work in different fields the difference matters.

[-][anonymous]7y 7

In reference to your request for thoughts, It seems like in both cases you could have parties switch their professed beliefs about the systems, without actually switching their behavior. This kind of pivot can and does definitely happen among some politicians. Should a reference to it be included?

Here are some potential examples:

Parties currently opposed to taking action on Anthropogenic climate change:

"Anthropogenic climate change has models which explain it and data which confirm the models. And it is good as a practical matter, because it is predicted to cause some areas to become more temperate, which will increase the yields of particular crops, so unlike the opposing party, we don't need to do anything to keep anthropogenic climate change from happening."

Parties currently opposed to keeping minimum wages low:

"Minimum wage caused unemployment has models which explain it and data which confirm the models. And is is good as a practical matter, because it is predicted to increase automation in low skill fields, which will increase yields of particular services, so unlike the opposing party, we don't need to keep the minimum wage low."

[-][anonymous]7y 4

"Minimum wage caused unemployment has models which explain it and data which confirm the models. And is is good as a practical matter, because it is predicted to increase automation in low skill fields, which will increase yields of particular services, so unlike the opposing party, we don't need to keep the minimum wage low."

That's pretty much what Yvain said in “The death of wages is sin”.

Other possible questions in the same reference class (where a belief arises from a mix of theory and data, and the theory plays a fairly big role in driving the belief, while the data on its own is very ambiguous).

Best suggestion I can think of: radiation hormesis in humans. Theoretically, as far as I know, one would expect radiation injury to increase about linearly with exposure for small exposures, but people have floated the idea that such radiation exposures might cause disproportionately less injury, or even prove beneficial. (I haven't looked at the data on this and can't offer a super-informed opinion.)

Noone can. LNT is the favored hypothesis, but the problem is that at levels in the vicinity of background radiation, the expected effect is so small that the necessary sample-sizes to confirm it empirically are entirely unreasonable. The people arguing hormesis sometimes point to the lack of any detectable cancer spikes in natural experiments (areas with higher background radiation) but that is at most mildly suggestive - to many confounding factors. In order to settle this, one would have to.. I dont know - build an automated high speed cancer detection aparatus for insects, breed and test a few million bugs in a salt mine for an ultra-low radiation baseline, then do it again after elevating the radiation levels in said salt mine in steps? And even if you did that, people would likely challenge the validity of the animal model.

[-][anonymous]7y 0

Well, if LNT is valid for all radiation levels comparable to or greater than natural background levels, then it's valid for (almost) all practical purposes.

No, knowing for sure would have practical implications. The world entire is radioactive, and LNT has had really major impact on the regulation of all things nuclear, so the small effects get multiplied by very large numbers of people affected. It LNT is wrong, not transitioning to an all fission grid ages ago was 100% certainly a dire, dire mistake, and radio-logical medicine can be used somewhat more aggressively. It thus actually matters that we don't know the answer to this question for sure. It is just an obnoxious experiment to design.

[-][anonymous]7y 2

I mean, what practical difference would it make whether the optimal level of radiation is exactly zero or non-zero but a couple orders of magnitude below the natural background?

(besides the health effects of working in places like LNGS -- but then again people who work in such places take a larger-than-average number of flights (e.g. in order to attend conferences) and the cosmic ray exposure during flights would compensate for that)

We do not know that the optimal level isn't 3 times average background. Or that the body does not adapt to constant low exposures in a way it does not to acute ones. Both of which are theoretical possibilities. - Earth has been getting less radioactive over geological time, and the mechanisms of cell repair are of very ancient origin. LNT isnt just an unconfirmed theory at levels below background. It is a line which is extrapolated from data derived from people who had doses much higher than that. "Hiroshima survivors". "Ill advised handling of multiple naked near-critical masses". sort of doses. The correlation is rock solid as we go from 90% mortality to 50, to 25, and it points at "Zero exposure, zero morbidity" more or less.. but once you get to doses within an order of magnitude of background, the expected morbidity is a very small percentage, and detecting it among all the other things that cause similar damage is just impractical. So we assume. It's a solid assumption, but it is an assumption. It could be wrong.

[-][anonymous]7y 0

We do not know that the optimal level isn't 3 times average background.

OK, I think I misunderstood what you meant by “in the vicinity of” in the ancestor.

As to the first question, I think that you need to define "Anthropogenic Global Warming." Does it simply mean that mankind's activities are have or will lead to an increase in global surface temperatures? Does it mean that mankind's CO2 emissions have or are likely to lead to an increase in global surface temperatures? Does it mean that mankind's CO2 emissions are likely to cause warming which will then start a positive feedback loop, leading to dangerous levels of warming? Or does it mean something else entirely?

I realize that you want to keep the question brief, but these are extremely important distinctions.

By contrast, it's much clearer what it means to increase the minimum wage and what unemployment means.

Responses to your questions, in forms like "I attribute most of my belief to the theory" or "70% data, 30% theory", will be basically useless. There are too many variables for the reader to fill in. Using the AGW example: what alternative data set are we considering? One in which (A) average surface temperatures stayed flat despite the increase in CO2? Or one in which (B) temperatures rise over several decades, but within a range only slightly larger than the largest several-decade change in a few pre-industrial centuries?

If I'm pondering scenario A I'll say that my belief is mostly data-based. After all, in that scenario I'd figure the theory doesn't apply, in that (e.g.) there's some negative feedback I'd overlooked. The data of A would sway me. On the other hand, the data of B wouldn't, even if the effect size were smaller than expected. Thinking about scenario B, I'd say my view is mostly theory-based. So my response depends entirely on how I fill in the details of your question. I think similar points apply to the minimum wage question as well.

Historical example: exponential population growth.

Excellent post!

Regarding ways that the questions might be disanalogous: For temperature data, I don't think that many people would question the data, average temperatures seem like good, hard facts to me. But some people might question unemployment data that they were presented with, stating that the measure of unemployment is flawed because it only measures people actively looking for work who are still eligible to receive unemployment. Some people 'fall off' and just become long term unemployed that no longer get counted in the statistics.

Perhaps you might note that some measure of 'percentage of population that is employed (adjusting for demographics changes)' would work better as the 'data' for some people?

Also, the post made me realize that in both of these two cases, the belief that I actually have (agree with both hypotheses), were formed due to the theory, and not due to looking at any empirical data. That is, when I hear empirical data in support of climate change, I think: 'well, obviously!', not 'here is the data that should be strengthening my belief in climate change'. I also realize that I haven't investigated and seen any data either way regarding whether minimum wages really do increase unemployment or not, and maybe I should do that.

For temperature data, I don't think that many people would question the data, average temperatures seem like good, hard facts to me.

In my experience a non-negligible number of people do take issue with the relevant temperature data, although I have no good hard numbers on this. (Probably no one does, given the difficulty of taking a representative sample of people who dispute the occurrence/magnitude of AGW.)

In my experience a non-negligible number of people do take issue with the relevant temperature data,

I think it's significant that of the well-known surface temperature indices, there is one -- GISS -- which has the highest recent temperatures. AFAIK, the GISS index is put together the authority of James Hansen who is pretty well known for his advocacy on the warmist side of the debate.

I think it's significant that of the well-known surface temperature indices, there is one -- GISS -- which has the highest recent temperatures.

For the purpose of assessing the rate of global warming, whether a temperature index has the highest recent temperatures is less important than whether it has the highest difference between more recent temperatures and less recent temperatures.

AFAIK, the GISS index is put together the authority of James Hansen who is pretty well known for his advocacy on the warmist side of the debate.

The code for generating the GISS index is available online, as is a more user-friendly reimplementation of the GISS algorithm. So it should be possible to independently reproduce the GISS index oneself without relying on "the authority of James Hansen".

[Edit, 27 hours later: not really sure why someone's downvoted me for pointing these things out.]

For the purpose of assessing the rate of global warming, whether a temperature index has the highest recent temperatures is less important than whether it has the highest difference between more recent temperatures and less recent temperatures.

I believe that GISS also wins by that standard.

So it should be possible to independently reproduce the GISS index oneself without relying on "the authority of James Hansen".

Evidently, when creating a temperature index, judgments must be made about what data to use; how to crunch the numbers; and so on. Presumably that's why the leading temperature indices don't all agree. In the case of GISS, those judgments seem to have been made in such a way as to favor the warmist side of things. I strongly suspect this is the result of some kind of bias.

In both cases, there is a theory (the greenhouse effect for Question #1, and supply and demand for Question #2) that is foundational to the domain and is supported through a wide range of lines of evidence.

No. The evidence for whether minimum wage laws produce unemployment is inconclusive. The recent studies showed no significant effect.

A number of weather forecasters, physicists, and forecasting experts are skeptical of long-range climate forecasting

Being skeptical of long-range climate forecasting shouldn't lead you to believe that CO2 has no effect. It should lead you to believe that we can't quantify the effect. You widen your uncertainty interval instead of moving it's center.

General thoughts on how this relates to Bayesian reasoning and other modes of belief formation based on a combination of theory and data.

If you want to focus on Bayesianism don't ask people whether they believe but ask them for a probability.

No. The evidence for whether minimum wage laws produce unemployment is inconclusive. The recent studies showed no significant effect.

When I meant "supported through a wide range of lines of evidence" I was referring to the theory in its generality, not necessarily its application to the context at hand. So I meant that the theory of supply and demand on the whole is supported through several lines of evidence, not necessarily its application to the minimum wage issue (where the evidence alone does seem inconclusive). That was the point of the question.

Thanks for raising the issue, and sorry for the confusion I engendered.

So I meant that the theory of supply and demand on the whole is supported through several lines of evidence

The idea that supply and demand are a factor that can change some commercial interactions isn't controversial. Human psychology on the other hand is quite complex. There is good evidence that most humans aren't completely rational utility optimizers.

reduce

Widen?

Yes, I corrected the error.

Second proposition is not a good choice, because the emperical evidence pretty strongly suggests that the link.. Uhm, does not exist. Which has caused me to question the theory quite a lot. That is pretty basic- theories that don't get support by the data are to be discarded.

Edit: Actually, let me put this in stronger terms. Looking into the literature behind this supposed link is one of the things that made me extremely skeptical about the discipline of economics as it is currently practiced in it's entirety. Because most of the underlying studies focused on teens, a group that theory predicts wage increases should have vastly outsized impact on, and which has also had cultural pressures reducing employment. That reeks of fishing for results, and when despite this, the empirical data returned a null result, the theory was not tossed onto the rubble pile of history, and economists keep right on arguing against all minimum wage increases.

I mean, bravo for not lying about what the data says, but minus 9000 points for then not taking it onboard.