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"Moral certainty is always a sign of cultural inferiority. The more uncivilized the man, the surer he is that he knows precisely what is right and what is wrong. All human progress, even in morals, has been the work of men who have doubted the current moral values, not of men who have whooped them up and tried to enforce them. The truly civilized man is always skeptical and tolerant, in this field as in all others. His culture is based on "I am not too sure.""
an extra portion of red meat a day, where a portion is 85g or 3 oz — a lump of meat around the size of a pack of cards or slightly smaller than a standard quarter-pound burger — is associated with a hazard ratio of 1.13, that is a 13% increased risk of death. But what does this mean? Surely our risk of death is already 100%, and a risk of 113% does not seem very sensible? To really interpret this number we need to use some maths.
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Since we assume Sam is consuming an average amount of red meat, we shall take him as an average man. We can see the effect of a hazard-ratio of 1.13 for Mike by multiplying all the
columns by 1.13 and recalculating
and the life expectancy from age 40, which gives us 39 extra years for Mike. So the extra meat is associated with (but did not necessarily cause) the loss of one year in life expectancy. Over 40 years this is 1/40th difference, or roughly one week a year or half hour per day. So a life-long habit of eating burgers for lunch is associated with a loss of half an hour a day, considerably more than it takes to eat the burger. As we showed in our discussion of microlives a half-hour a day off your life expectancy is also associated with two cigarettes a day and each day of being 5 Kg overweight.
Of course we cannot say that precisely this time will be lost, and we cannot even be very confident that Mike will die first. An extremely elegant mathematical result says that if we assume a hazard ratio
is kept up throughout their lives, the odds that Mike dies before Sam is precisely
. Now odds is defined as
, where p is the chance that Mike dies before Sam. Hence ![\[ p=h/(1+h)=0.53. \]](/MI/d40b9c2650170fdc3055016f03d71148/images/img-0003.png)
So there is only a 53% chance that Mike dies first, rather than a 50:50 chance. Not a big effect.
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Finally, neither can we say the meat is directly causing the loss in life expectancy, in the sense that if Mike changed his lunch habits and stopped stuffing his face with a burger, his life expectancy would definitely increase. Maybe there's some other factor that both encourages Mike to eat more meat and leads to a shorter life.
It is quite plausible that income could be such a factor — lower income in the US is associated with both eating more red meat and reduced life expectancy, even allowing for measurable risk factors. But the Harvard study does not adjust for income, arguing that the people in the study — health professionals and nurses — are broadly doing the same job. But, judging from the heated discussion that seems to accompany this topic, the argument will go on.
[A]lthough political (and religious) ideology has no place in deciding scientific questions, the practice of science is inherently political. In that sense, science can never come before politics. Scientists everywhere enter into a social contract, not least because they are not their own paymasters. Much, if not most, scientific research has social and political implications, often broadly visible from the outset. In times of crisis (like the present), scientists must respond intellectually and professionally to the challenges facing society, and not think that safeguarding their funding is enough.
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conflates scientific judgments with judgments about action:
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It is impossible to achieve complete certainty on many complex scientific problems, yet sometimes we still need to take action. The sensible course is to turn to the expert scientists for their consensus view. When doctors found I had blockages in the arteries around my heart I asked them for their expert view as to what I should do. They recommended a bypass, I took their consensus advice, and here I am. That is how science works.
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A year after the Fukushima reactor catastrophe, we can start to estimate its effects on people’s medical and mental health. Curiously, it’s the mental impact that we can predict best. As a recent Green Blog post by Matthew Wald explained, the medical effects are expected to be too weak and widely dispersed to measure. According to one theory, the increased radiation received by hundreds of thousands of citizens will cause an increase in their cancer rate — but an increase too tiny to detect amid the large number of cancers that will occur anyway. According to a rival theory, radiation at these low levels will cause scarcely any cancers at all. Scientists just don’t know.
The psychological impact, however, is plain. Precisely because damage from very-low-level radiation cannot be detected, people exposed to it are left in anguished uncertainty. Many believe they have been fundamentally contaminated for life. They may refuse to have children for fear of birth defects. They may be shunned by others who fear a sort of mysterious contagion.
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Such great psychological danger does not accompany other materials that put people at risk of cancer and other deadly illness. Visceral fear is not widely aroused by, for example, the daily emissions from coal burning, although, as a National Academy of Sciences study found, this causes 10,000 premature deaths a year among Americans. It is only nuclear radiation that bears a huge psychological burden — for it carries a unique historical legacy.
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To be sure, people knew that nuclear energy had a good side. Already in the 1950s, radiation therapy was saving lives by the million. But thoughts of “good atoms” were overwhelmed by the terrors of the Cold War. Outcries against bomb tests focused on the radioactive materials they spread around the world on the winds. Debates over fallout shelters offered an image of a dead planet scourged by radioactive dust. In short, fear of nuclear war drove home images of radiation as an insidious contamination that was uniquely deadly on a global scale.
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How many people died? It's one of the first questions asked in a war or violent conflict but it's one of the hardest to answer. In the chaos of war many deaths go unrecorded and all sides have an interest in distorting the figures.
The best we can do is come up with estimates but the trouble is that different statistical methods for doing this can produce vastly different results — see the Plus article Body count which reported on controversy surrounding the death toll of the last Iraq war. Statisticians know how well different methods do in theory and under ideal assumptions, but wars rarely adhere to these. And we can't concoct a war in the lab to see which method does best.
But recently a unique document has helped throw some light on the question of how to count the dead.
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two methods that are commonly used. One is based on household surveys: a random sample of households from around the region are asked to name their dead and statisticians estimate the total figure from the responses, much like an opinion poll gauges the mood of the nation from the opinions of a random sample of people.
The other involves taking two independent samples of deaths recorded throughout the region and comparing them. If the overlap between the two samples is large — if many deaths appear in both — then chances are that the overall number is relatively small, as you've managed to "catch" many deaths twice. Conversely, if the overlap is small, then the overall number is probably large. There are mathematical equations that make this intuition rigorous and give you an estimate of the total number. The method was initially developed for ecologists trying to estimate the number of animals that live in an area. It's called capture/recapture, as in this case the method works by capturing a number of animals, tagging them, releasing them back, and then capturing another sample to see how many animals got caught twice.
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numbers are essential in establishing truth. It's important to get them right. Exhaustive approaches like the Kosovo Memory Book take years to complete and, in the absence of complete information, all you're left with is a statistical approach.
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Don’t fall into the trap of feeling guilty, especially if you have the luck of studying in such a rich place. All this bullshit like, “Somalian children are starving....” No! Somalian children are not starving because you have a good time here. There are others who are much more guilty. Rather, use the opportunity. Society will need more and more intellectual work. It’s this topic of intellectuals being privileged—this is typical petty-bourgeois manipulation to make you feel guilty. You know who told me the best story? The British Marxist, Terry Eagleton. He told me that 20 or 30 years ago he saw a big British Marxist figure, Eric Hobsbawm, the historian, giving a talk to ordinary workers in a factory. Hobsbawm wanted to appear popular, not elitist, so he started by saying to the workers, “Listen, I’m not here to teach you. I am here to exchange experiences. I will probably learn more from you than you will from me.” Then he got the answer of a lifetime. One ordinary worker interrupted him and said, “Fuck off! You are privileged to study, to know. You are here to teach us! Yes, we should learn from you! Don’t give us this bullshit, ‘We all know the same.’ You are elite in the sense that you were privileged to learn and to know a lot. So of course we should learn from you. Don’t play this false egalitarianism.”
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it’s no longer a question of knowledge. Today many, even sociologists, have this wonderful idea of how, although we live in a society of knowledge—even scientific knowledge—[it] is becoming more and more contingent, non-binding. I think it was the German theorist Ulrich Beck who drew attention to the simple fact: today we speak about expert opinions. Are we aware how paradoxical this term is? The idea is that we ordinary people have opinions. They tell you the truth. Now experts all of a sudden are telling us different opinions and we have to decide how, who knows, if even they don’t know. This is the tragedy of our predicament of freedom of choice. The problem is...we are often forced to choose without having serious cognitive coordinates of how or what to choose.... The price is that science is no longer a homogenous science but it’s turning into kind of a pluralistic field of opinions.
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if practical ethics should take empirical uncertainty into account, surely it should take moral uncertainty into account as well. In many situations, we don’t know all the moral facts. I think it is fair to say, for example, that we don’t currently know exactly how to weigh the interests of future generations against the interests of current generations. But this issue is just as relevant to the question of how one ought to act in response to climate change as is the issue of expected temperature rise. If the ethics of climate change offers advice about how best to act given empirical uncertainty concerning global temperature rise, it should also offer advice about how best to act, given uncertainty concerning the value of future generations.
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The standard account of making decisions under uncertainty is that you ought to maximise expected value: look at all hypotheses in which you have some degree of belief, work out the likelihood of each hypothesis, work out how much value would be at stake if that hypothesis were true, and then trade off the probability of a hypothesis’ being true against how much would be at stake, if it were true. One implication of maximizing expected value is that sometimes one should refrain from a course of action, not on the basis that it will probably be a bad thing to do, but rather because there is a reasonable chance that it will be a bad thing to do, and that, if it’s bad thing to do, then it’s really bad. So, for example, you ought not to speed round blind corners: the reason why isn’t because it’s likely that you will run someone over if you do so. Rather, the reason is that there’s some chance that you will – and it would be seriously bad if you did.
Catastrophe models are controversial. Proponents say they bring science to underwriting and synthesize the latest understanding of storms and climate change to insurers. Opponents say they're gee-whiz black boxes that manufacture instant justification for high rates for insurers.
Writing at VoxEu.org Victor Ginsburgh, of the Université Libre de Bruxelles, notes that experts aren't so expert in many situations:
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professional musicians are unable to distinguish between the tonal superiority of a violin built by Stradivari (which would cost up to $4 million) from that of a new American instrument (a couple of thousand). . .
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there is lack of concordance between wine judges. Hodgson’s (2008) [here in PDF] result is even stronger, since he finds that only about 10% of the judges are able to replicate their score within a single wine medal group.
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"The reserve of modern assertions is sometimes pushed to extremes, in which the fear of being contradicted leads the writer to strip himself of almost all sense and meaning. One specimen of this method will suffice:
"It is reasonably certain that a petty chieftain named Arthur did exist, probably in South Wales. It is possible that he may have held some military command uniting the tribal forces of the Celtic or highland zone or part of it against raiders and invaders (not all of them necessarily Teutonic). It is also possible that he may have engaged in all or some of the battles attributed to him; on the other [hand, this attribution may belong to a later date."
This is not much to show after so much toil and learning"
--- AHistory of the English-Speaking Peoples / Winston Churchill
John Maynard Keynes thought that most economic decision-making occurs in ambiguous situations in which probabilities are not known. He concluded that much of our business cycle is driven by fluctuations in “animal spirits,” something in the mind – and not understood by economists.
Of course, the problem with economics is that there are often as many interpretations of any crisis as there are economists. An economy is a remarkably complex structure, and fathoming it depends on understanding its laws, regulations, business practices and customs, and balance sheets, among many other details.
Yet it is likely that one day we will know much more about how economies work – or fail to work – by understanding better the physical structures that underlie brain functioning. Those structures – networks of neurons that communicate with each other via axons and dendrites – underlie the familiar analogy of the brain to a computer – networks of transistors that communicate with each other via electric wires. The economy is the next analogy: a network of people who communicate with each other via electronic and other connections.
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Scholars can become so trapped in their methods – in the language and assumptions of the accepted approach to their discipline – that their research becomes repetitive or trivial.
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Then something exciting comes along from someone who was never involved with these methods – some new idea that attracts young scholars and a few iconoclastic old scholars, who are willing to learn a different science and its different research methods.
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The global climate change debate has a lopsided empirical basis - in the economy of nature but not political economy - and this has contributed to a peculiar moralising trajectory. I have three main concerns with this: i) climate change has displaced other important concerns, for example of the 1 billion people living in unacceptable poverty; ii) a fixation on global CO2 levels alone distracts from what we can practically do, and even from caring about other aspects of the environment that we want to protect; iii) the debate has induced a kind of millenarian meltdown in which otherwise sensible people have lost all sense of proportion and hope.
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The science of climate change is not my target. It is clearly a fact that the world's regional climates are changing substantially and at unprecedented speed, and this is a result of greenhouse gases produced by human activity (in particular by the industrialisation of the West). Denying the science is just silly. But 'science' does not have the legitimacy or resources to tell us what we should do about climate change. We have to work that out for ourselves.
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In trying to tackle climate change by directly dealing with the causal mechanism of CO2 levels we have framed the situation as an enormous collective action problem - how to persuade 7 billion people to adopt the new morality of carbon rationing (and prevent free-riding). Everyone who thinks this through recognises that it is impossible to realise without enormous government coercion (severe rationing along the lines of China's one-child policy). That requirement explains many climate change warriors' antipathy to democratic principles on this point - it seems easier to persuade all 200 governments to be adopt carbon authoritarianism than to persuade all those people individually (e.g. James Lovelock). However even the government coercion approach fails - see the failures of every inter-governmental treaty, from Kyoto to Copenhagen - and the reasons are obvious.
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, I don't think that all such pleas for more moderation should be granted. In particular, there is a tendency to advocate suspended judgment rather than definite opinion as the appropriate response to thorny ultimate questions.
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Our index shows prominent surges in policy uncertainty around the time of major elections, the outbreak of wars and after the Sept. 11 attacks. It shows another surge after the bankruptcy of Lehman Brothers Holding Inc. in September 2008. Policy uncertainty has remained at high levels ever since.
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Comparing the two reveals several episodes that involve large surges in economic uncertainty but little or no jump in policy uncertainty. Examples include the Asian financial crisis of 1997 and several instances of recession jitters in the second half of the 1980s. In short, the data refute the view that economic uncertainty necessarily breeds policy uncertainty.
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The flood disaster arguably was exacerbated by poor decision making under flawed decision processes -- decision makers chose the certainty of ignorance over the uncertain nature of uncertainty judgments. Indeed, as van den Honert describes the rainfall forecasts were inaccurate, but this did not mean that they would have been without value.
Ultimately, the only way that Queensland gets out of this situation will be to build sufficient water retention capacity to simultaneously meet the conflicting objectives of flood mitigation and water storage as a drought buffer. In other words, there is a technological fix here that can dramatically reduce uncertainties -- but such a strategy will cost money.
POSTED BY ROGER PIELKE, JR. AT 8/30/2011 06:55:00 AM
A fundamental problem with climate science in the public realm, as conventionally practiced by the IPCC, is the essential ink blot nature of its presentation. By "ink blot" I mean that there is literally nothing that could occur in the real world that would allow those who are skeptical of scientific claims to revise their views due to unfolding experience. That is to say, anything that occurs with respect to the climate on planet earth is "consistent with" projections made by the climate science community. Some scientists go further and argue that climate science cannot be shown to be incorrect based on experience because its projections are probabilistic. The result is that people tend to see in climate science other things than those that can be resolved empirically -- which fosters politicization and tribal behavior.
The ink blot nature of climate science would be a non-issue if it were a field like philosophy or cosmology in which people were debating non-empirical claims for academic interests. But climate science -- or at least a very visible part of that field -- has set forth on an evangelistic path in trying to convince the unconvinced of their views among politicians and the general public.
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the ink blot nature of climate science leaves climate scientists in a position of arguing from authority or demanding that people need "trust us." The typical mode of engagement with skeptics by many visible climate scientists is to argue how right they are (and wrong/evil the skeptics are) -- but what skeptics need instead is to hear what it would mean for climate scientists to be wrong. If one cannot be wrong, then experience cannot be used to adjudicate claims.
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the climate science community argues that uncertainties/variability are so large as to make such claims not inconsistent with their views
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In its AR4 report the IPCC says:
The uncertainty guidance provided for the Fourth Assessment Report draws, for the first time, a careful distinction between levels of confidence in scientific understanding and the likelihoods of specific results. This allows authors to express high confidence that an event is extremely unlikely (e.g., rolling a dice twice and getting a six both times), as well as high confidence that an event is about as likely as not (e.g., a tossed coin coming up heads). Confidence and likelihood as used here are distinct concepts but are often linked in practice.
Here are some specific definitions to help you answer some questions.
A. "high confidence" means "about 8 out of 10 chance of being correct".
B. "extremely unlikely" means "less than 5% probability" of the event or outcome
C. "as likely as not" means "33 to 66% probability" of the event or outcome
So here are your questions:
1. If the IPCC says of a die that it has -- "high confidence that an event is extremely unlikely (e.g., rolling a dice twice and getting a six both times)" -- how should a decision maker interpret this statement in terms of the probability of two sixes being rolled on the next two rolls of the die?
2. If the IPCC says of a die that it has -- "high confidence that an event is about as likely as not (e.g., a tossed coin coming up heads)" -- how should a decision maker interpret this statement in terms of the probability of a head appearing on the next coin flip?
Please provide quantitative answers to 1 and 2, show your work.
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There is insufficient information to do the calculation rigorously. Actual calculation would require assignment of the rest of the second-order probability (the "confidence") to different probability distributions.
In the absence of that information, a reasonable decision-maker might
(a) decide to ignore the second-order probability and use the existing PDFs ("extremely unlikely" for double sixes and "as likely as not" for the coin toss
(b) hedge against incorrect estimates of the "true" probabilities by assigning additional likelihood to "tail" events. In the coin toss event this doesn't really matter - if you only have two possible outcomes that are approximately equally likely, being somewhat wrong about the PDF won't make much difference for planning. But if you have reason to worry that an event someone called "extremely unlikely" might actually be just "unlikely", you might want to invest extra in hedging against it. (As well, of course, as extra research).
This just returns to my more general claim, that the issue for decision-makers is deciding what PDF to act "as if" is the "true" PDF, even when there really is no "true" PDF. -
-1-Paul Baer
Thanks ... I agree with this answer, and have these thoughts about the implications:
(a) ignore the second order stuff (the IPCC generally does) -- bad idea, as this means ignoring potentially relevant info (see #3 below)
You write: "the issue for decision-makers is deciding what PDF to act "as if" is the "true" PDF, even when there really is no "true" PDF"
As I said on the other thread, I get what you are saying, but I have a hard time translating this to practical situations. I think that the decision calculus has to factor in what it means to be "wrong" in a probability judgment as related to the outcomes of a decision based on such judgments. Expressing the view that such judgments cannot be "wrong" is not the way to go.
I return to the notion that context matters in such judgments and flipping a coin has little in common with ratings of mortgage-backed securities.
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Deep in the comments on an earlier thread Paul Baer offers the following hypothetical:
In my statistics class, I ask my students "what is the probability that when I flip this coin, it will land heads." (And yes, assume it's a fair coin.)
Of course they answer 50% or some equivalent.
Then I flip it and hold it covered on the back of my hand. Then I ask, "What is the probability that this coin is heads." There's usually some puzzlement. Someone says "50%". And I say, "but either it's heads or it isn't. How can there be a fifty percent chance it's heads?"
Then I ask "what odds would you give me if I bet that it's not heads?" Eventually those who know what betting odds mean understand the point. Even when something has happened (like, the deck has been shuffled and the card that will be dealt could be known under some epistemic conditions DIFFERENT FROM OURS) we have to ACT as if the odds are, well, what we think they are. -
To which I responded:
Consider the following case:
Answers gladly accepted.
You flip a coin in your class and ask for the probability of a head. A savvy student replies:
[S1]: The odds of a head are 50-50
You then reveal to the class that the coin is not fair, in fact there is a 75% chance of a tail. You ask the student, now what are the odds of a head? (All while the flipped coin sits on your hand)
The student now replies:
[S2] The odds of a head are 25%.
Q1. Now would it be fair to say that [S1] was incorrect? - 1 more annotation(s)...
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Reasoning about climate uncertainty
Judith Curry
Received: 1 April 2011 / Accepted: 14 June 2011 # The Author(s) 2011.
Climatic Change DOI 10.1007/s10584-011-0180-z
This article is published with open access at Springerlink.comAbstract. This paper argues that the IPCC has oversimplified the issue of uncertainty in its Assessment Reports, which can lead to misleading overconfidence. A concerted effort by the IPCC is needed to identify better ways of framing the climate change problem, explore and characterize uncertainty, reason about uncertainty in the context of evidence-based logical hierarchies, and eliminate bias from the consensus building process itself.
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Scientific uncertainty: a user’s guide
Seamus Bradley
Abstract. There are different kinds of uncertainty. I outline some of the various ways that uncertainty enters science, focusing on uncertainty in climate science and weather prediction. I then show how we cope with some of these sources of error through sophisticated modelling techniques. I show how we maintain confidence in the face of error.
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