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Dene Bebbington: 21st Century Divination

This is an article by Dene Bebbington, who is a freelance writer.

Catastrophism is ever popular, especially in the last few years. Particularly since the EU referendum campaign our society has been suffering a malaise of constant worry about potential catastrophic events such as Brexit, climate change, and now the Covid-19 pandemic. What all three have in common is that mathematical models underpin the predictions. The tendency towards saturation media coverage, which is often misleading, has further skewed and damaged public understanding of these issues.

In the run up to the EU referendum economic forecasts said we could experience a recession if the vote was to leave the EU. It was claimed that unemployment would rise significantly, sterling would fall, inflation would rise, house prices would fall, etc. Though prolonged political uncertainty following the vote for Brexit did impact the economy, we didn’t enter a recession and the only parts of the forecast that came true were a fall in the value of sterling and the rise in inflation. Despite that some people still cling to the idea they can quantify how much GDP growth fell by, despite the difficulty in knowing the counterfactual.

The then-chancellor George Osborne claimed that in fifteen years every family would be £4300 a year worse off. Even short term economic forecasts are usually inaccurate and have to be refined throughout the year. For him to put a figure on GDP after fifteen years is so laughable that it was clearly a political exercise. Besides that, the figure conflated per capita GDP with household income.

Predictive failure is no surprise since economic forecasting has a patchy track record. Economist Prakash Loungani studied its record and discovered that forecasts failed to predict 148 of 150 recessions. The summary of a 2018 paper for the IMF he wrote with two other people puts the failure of forecasting into relief:

We describe the evolution of forecasts in the run-up to recessions. The GDP forecasts cover 63 countries for the years 1992 to 2014. The main finding is that, while forecasters are generally aware that recession years will be different from other years, they miss the magnitude of the recession by a wide margin until the year is almost over. Forecasts during non-recession years are revised slowly; in recession years, the pace of revision picks up but not sufficiently to avoid large forecast errors. Our second finding is that forecasts of the private sector and the official sector are virtually identical; thus, both are equally good at missing recessions. Strong booms are also missed, providing suggestive evidence for Nordhaus’ (1987) view that behavioral factors—the reluctance to absorb either good or bad news—play a role in the evolution of forecasts.

A spectacular example of the limits of forecasting was the failure to predict the 2008 financial crisis (though some people had warned of a potential crisis).

Since the referendum, and perhaps long before, sections of the population have been in a perpetual angst from thinking the worst. Recently there’s been the rise of Greta Thunberg and Extinction Rebellion asserting that we’re all doomed unless carbon dioxide emissions are drastically reduced. I accept the scientific consensus on climate change, but I don’t take climate predictions as gospel. Science is a process of continual discovery and refinement, and it’s not as if there haven’t been failed climate predictions.

The so-called ‘Climategate’ scandal in 2009 about the work of the Climatic Research Unit (CRU) at the University of East Anglia shone a light on academic coding standards, amongst other things. Their modelling software was so badly written that a programmer couldn’t reproduce previous results, and encountered many coding and database problems. Given the recent revelations about Neil Ferguson’s software for his flu pandemic model, there’s reason to suspect that poor standards of software engineering in academia are endemic.

Mathematical models can be useful in some circumstances – if they’ve been rigorously reviewed and tested, and everyone involved understands their assumptions and limitations. The reliability of short-term weather forecasts is a good example. Yet we shouldn’t fall into the trap of believing that all models are necessarily good at predicting the future just because they have mathematics which most of us may not be able to understand. Instead of lapsing into a belief in models like a form of 21st-century divination, we should remain ever sceptical and only accept the highest standards in producing them.

There seem to be four key elements of a useful predictive model:

1. The mathematics and algorithm

2. The theoretical assumptions

3. The input data

4. The software that implements the model.

Even if (1) is sufficiently correct, (2), (3) and (4) must also be correct. Climategate and reviews of Ferguson’s code throw (4) into serious doubt for their models. We can also doubt whether (2) and (3) are correct. For example, Ferguson predicted that Sweden would have a much higher number of Covid-19 deaths if they didn’t enact a lockdown rather than the restrictions they carried on with. Sweden’s deaths have stubbornly refused to match his prediction.

Going back nearly two decades we find Ferguson’s work was instrumental in the government’s response to the 2001 Foot and Mouth outbreak. The result was a mass slaughter of millions of cows and sheep, and the suicide of several farmers. Subsequent work by a Professor of Veterinary Epidemiology has claimed that Ferguson’s model was severely flawed. Ferguson himself acknowledged that he was working in real time with limited data. This is surely a reason to doubt the use of a model in that situation, even if we don’t know what would have happened if the modelling was ignored and a different approach to containing the disease been taken.

Whenever the output of models have been used in public policy making, all those involved should be held accountable. We need to consider whether potentially bad predictions are better than no prediction, especially since several models have, unfortunately, become inextricably linked with political and moral worldviews.

A rewritten version of Ferguson’s pandemic model was recently made available, but at the time of writing this the original code has still not been disclosed. Several reviews of the code have been written by experienced software engineers who have identified problems in it. One of those reviews was picked up by the Telegraph newspaper who reported this response from Imperial College:

The UK Government has never relied on a single disease model to inform decision-making. As has been repeatedly stated, decision-making around lockdown was based on a consensus view of the scientific evidence, including several modelling studies by different academic groups.

Multiple groups using different models concluded that the pandemic would overwhelm the NHS and cause unacceptably high mortality in the absence of extreme social distancing measures. Within the Imperial research team we use several models of differing levels of complexity, all of which produce consistent results. We are working with a number of legitimate academic groups and technology companies to develop, test and further document the simulation code referred to. However, we reject the partisan reviews of a few clearly ideologically motivated commentators.

Epidemiology is not a branch of computer science and the conclusions around lockdown rely not on any mathematical model but on the scientific consensus that COVID-19 is a highly transmissible virus with an infection fatality ratio exceeding 0.5pc in the UK

More details can be found towards the end of this piece at Lockdown Sceptics.

It’s revealing that Imperial’s response doesn’t address the specific criticisms of the code except to handwave them away by a comment that epidemiology is not a branch of computer science. However, their pandemic model is implemented as a computer program and can therefore be examined for bugs by any suitably experienced programmer. As a defence it’s as fatuous as saying that construction principles don’t apply when building a hospital.

Besides, if they’re now claiming that the pandemic model didn’t have any input to the decision to lockdown, then why did the SAGE report reference their model?

 

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30 thoughts on “Dene Bebbington: 21st Century Divination

  1. I wrote a piece at The Critic which explained that the errors and inconsistencies in Ferguson’s code unearthed by the Telegraph and others are essentially irrelevant. Any plausible model based on current widely-accepted theory would have come to much the same conclusion: the NHS would be overwhelmed by several thousand percent. Compared to that, variations of ~20% (as found by the Telegraph) are trivial and don’t affect the conclusion in the slightest.

    It’s striking that no credible alternative modeller has since come forward with a significantly different prediction. The only critics have been a mixture of commentators, programmers and computer scientists, all of whom have missed the essential point: with an R0 of 2.4 and an IFR of ~1%, what Ferguson predicted would have been what happened; the details of the implementation of the model, no matter whether it was clumsily or badly done, were beside the point.

    Of course you may disagree with those values but, at the time of the prediction the evidence was in favour of those values and there was no solid, convincing reason reason for thinking C-19 must certainly have materially different values.

  2. Great article. I didn’t like the reference to scientific consensus on climate change; it felt like you were covering yourself.

    The climate change consensus is in itself is propaganda, the studies that attempt to prove this (4 of them at last count) have all been discredited. There are plenty of eminent scientists who vehemently disagree with CAGW, just like there are many epidemiologists who disagree on the severity of Covid-19.

  3. “…I accept the scientific consensus on climate change, but I don’t take climate predictions as gospel….”

    Exactly what does this mean?

    There is no such thing as a ‘scientific consensus’. A consensus is a political term. Science operates entirely by observation and data. If one morning the moon flies out of orbit that will simply be a new observation to be fitted into, or to disprove, current theories of gravity. And scientists should be perfectly aware that this might happen, and be prepared to readjust theories accordingly.

    For your info, the UK Met Office agrees that current climate observations are completely compatible with natural changes. But they are ignoring these, and trying to drive a political move to alter people’s behaviour based on models which have completely failed to match observations. They then claim that there is a ‘consensus’ that humans are changing the climate.

    If you believe in the scientific method, then ‘observation’ should be a key word in driving your beliefs, and the word ‘consensus’ should be viewed with the same suspicion as you would view the word ‘propaganda’…..

  4. The reason for this is simple. We did not have sufficient information about a new disease to make an accurate prediction.

    Under those circumstances, it is certainly reasonable to take a ‘worst-case’ scenario as a guide for policy – but this should NOT BE MISTAKEN for ‘scientific advice. The scientific advice should have been ‘We don’t yet know.’.

    Administrators could then have taken extreme measures based on a worst-case scenario, but should have been prepared t0 revise these as new data came in and the science started to be grounded in reality.

    I have not been complaining about the initial imposition of the lockdown while we were unaware of the impact of the disease. I am, however, complaining about the lack of flexibility currently being shown – presumably in an effort to save political and academic face…

  5. “There seem to be four key elements of a useful predictive model:

    The mathematics and algorithm / The theoretical assumptions / The input data / The software that implements the model. ”

    Actually, there are a lot more. There is the fundamental question the model is asked – it could easily be the wrong one. There is the trick Climate Change proponents use – modelling only part of the phenomenon and ignoring aspects which would make the findings far less scary. There is the use of statistics to befuddle, there is the suppression of contrary views, there is the deliberate use of ambiguity in the final report enabling politicians to claim that the findings are not actually what they are – there are a whole host of techniques available to a determined activist scientist….

  6. I read your piece at The Critic and agree with it to some extent. My point is about the quality of work in models and whether the kind of prediction they do can in principle be accurate.

  7. AFAIK there is a consensus (i.e. most climate scientists are in agreement) about anthropogenic climate change.

    Could you provide a link to the Met Office agreeing that recent climate change is compatible with natural change?

  8. I’m not sure that taking the worst case scenario and acting accordingly was reasonable. In my view it would have been better to carry on with the voluntary social distancing and hygiene measures, and build up health care capacity, and only go for the nuclear option of a legal lockdown if actual rather than predicted deaths warranted it.

  9. An interesting article Dene, thank you.

    I have been ruminating on the way Covid-19 models and climate models are similar in many respects – or at least the way they are used to reinforce an activist view. As you and others have pointed out, in our current predicament, the models are massively sensitive to initial inputs – which if you are guessing at the start, make outcomes almost useless. The well known GIGO. Because climate is so complex- I believe some models have a million lines of code ( good luck reviewing that Sue Denim), and they only model bits of it in reality, and fudge-factor big chunks.

    Ignoring the workings of the models, there are a number of issues that I detect developing in epidemiology that have long been the case in climate science.

    1. Anyone whose predictions have been close to reality over the last 30 years will be sidelined, in favour of those who are always wrong but have a more extreme view – I am open to the idea that those extreme predictions suit politicians – but are they just mislead? I say sidelined above, but they are even censored and threatened with losing their job, and have done so. Watch this space – we already have YouTube and Facebook removals as we speak. Pressure put on Sweden to not show how the more draconian measures we have had applied were probably not necessary.

    2. If your model doesn’t produce the correct results – change the results. How much of the Covid data can we rely on – it isn’t so controlled yet – the reporting is though.

    3. If past events don’t fit your narrative, rewrite history. Every update of Earth’s temperature data gets a cooling of the past and a warming of the recent. This is not so well developed in epidemiology. Currently it is that Covid context is suppressed; past epidemics are ignored (even the 2014/15, 28k Flu death winter that nobody remembers). Would people be so scared if they realised the true context? Climate science just erases the history or “adjusts” it to fit. Only today I read another new paper using glacier records that show that Iceland was 3c warmer 8k years ago than today. It was as warm as today (according to records) in the 1930’s, but that has been “homogenised” now.

    Until this last few months epidemiology was little known, and its current strength, which they are now trying neuter, is a fairly diverse range of views as to what the data means and how the virus will proceed. Climate science has had 30 years to apply the thumbscrews to those who would deviate from the hymn-sheet. 30 years of teaching a theory as certainty, 30 years of recruiting scientists in ones own image, 30 years of ensuring no grants for those “denier” people.

    I know people are hoping for a thorough enquiry to shine a light on current events and decisions, hoping for scientific truth to come out – don’t count on it.

  10. “Anyone whose predictions have been close to reality over the last 30 years will be sidelined, in favour of those who are always wrong but have a more extreme view…”

    I wouldn’t be surprised if that’s the case because in general fear sells. Current climate change doesn’t frighten me too much as humanity has always had to, and always will need to, adapt to a changing climate. What would scare me is an ice age, a supervolcano eruption leading to a failure of crops for a year or more, a big asteroid hitting earth, or a gamma ray burst from a distant star. In contrast Brexit, AGW and Covid-19 are minor threats.

  11. Surely a “worst-case scenario does excuse a worst-case set of actions.

    If there was an army at the Channel, we could just nuke the lot – BUT……………….

  12. Dene – you never will.

    The Hadley Centre suffers from the problem I mention in my comment below in spades.

    They were part of the original AGW team from the outset, and no science is done that doesn’t support the theory. To express a doubt at all will get you the sack. They are these days at the extreme end of what has become quite a wide range for a consensus.

    Many in Climate Science (CS) will express private doubts, but to do it publicly is likely to see attacks on you personally (never your science) and loss of grants and even tenure. Gate keepers in CS are usual a small group of go-to people for peer review and they often block stuff from getting into magazines – see Climategate emails. At the same time pal review is rife. Magazine editors have ben threatened with the sack for publishing peer reviewed stuff, the “high priests” don’t like.

    There are hundred’s of peer reviewed papers showing a direct link between the “current bun” and the earth’s climate – very little showing any real link between CO2 and the Earth’s temperature. Yet the IPCC gives a couple pages to it their reports, and dismisses the link.

    As Richard Lindzen, MIT. Emeritus Prof. of Atmospheric Physic says. The believe that 1 extra molecule of CO2 in 10,000 molecules of the atmosphere (4 instead of 3) is causing catastrophic climate change borders on belief in magic.

    It is a religion not a science – you are starting to see elements of this in the current crisis.

  13. I totally agree with your last paragraph. There is more than enough evidence 8 weeks on for the government to have completely changed their tune, with scientific and statistical backup to save their faces. Yet the government steadfastly refuses to do so. The 2m rule is what’s preventing schools and businesses from reopening, though there is absolutely no scientific evidence to back this up. Why are the government so inflexible, what’s the agenda?
    I worry about children’s psychological and social development and those people who’ve spent decades building up good businesses, who are unable to operate. It’s time “The Science” was spun to reassure, not to perpetuate the misery.

  14. Dene B: “AFAIK there is a consensus (i.e. most climate scientists are in agreement) about anthropogenic climate change.”

    Don’t be obtuse. Yes, we all understand that most scientists whose next grant depends on their kissing the ring of the Climate Change Scam willingly drop to their knees and do what is required. If the definition of “consensus” is that climate scientists, just like other human beings, will do what they are paid to do and say what they are paid to say — then, yes, there is a “consensus”. But that is not science.

    Real scientists understand that consensus is meaningless – particularly purchased “consensus”. There was a nice consensus that Newton’s Theory of Gravitation was powerful — but when real-world observation showed the movements of the planet Mercury to be inconsistent with Newton’s Theory, it was “Goodbye Newton”.

    Anyone who wants to be taken seriously in science does not talk about “consensus”.

  15. The oddities of Mercury’s orbit didn’t directly lead to the demise of N’s theory of gravity.

    The anomalous precession of the perihelion of Mercury’s orbit was known for a long time before general relativity was discovered. People carried on with the “consensus” and tried to explain it using N’s laws, via a hypothetical planet, named Vulcan, orbiting between Mercury and the sun. But despite many people claiming to have observed it, Vulcan never showed up and then GR removed any need for its existence.

  16. The public should use consensus in science to guide them what to accept. It doesn’t mean the consensus won’t turn out to be incorrect, but the alternative is that people only accept what conforms to their worldview and biases. Of course, some already do that.

    As for AGW, I think the reason some people attack it is not because of the science per se, but the public policy implications of it. After all, someone claiming they accept the science while arguing that we shouldn’t do anything to reduce CO2 emissions is unlikely to be taken seriously. So they try to discredit the science.

  17. Simon A: “The oddities of Mercury’s orbit didn’t directly lead to the demise of N’s theory of gravity.”

    Of course, Simon. The story of the downfall of Newton’s Theory of Gravity is much more involved. What is important in this context is that observation (real-world measurements) trumps “consensus” in science. In fact, “consensus” is meaningless in science.

    “Consensus” is no more than an Appeal to Authority. Then one has to ask — Why do those particular individuals have “Authority”? Well, of course it is because they promote the “Consensus”. Talk about circular reasoning!

    Whenever we see the word “consensus” appear in the context of a scientific matter with public policy & economic implications, observation demonstrates that we should also think of the word “Scam”.

  18. Dene, good job you didn’t live in the early 20th century, or you would be supporting the ‘consensus’ of eugenics.
    As someone previously said correctly ‘consensus’ is the opposite of scientific thought and process.

  19. JimW, that doesn’t follow. Even if science shows that you can affect traits in the population by certain interventions or preventing some people from breeding, whether you choose to do so is a moral and political question, not a scientific one. Even though we’re in the 21st century a form of eugenics goes on if a woman decides to abort a foetus when a test shows it would have Downs syndrome.

    If you don’t think consensus is useful for Joe Public to know what to accept scientifically, then how do you think someone with the inability to judge the science should decide what to accept?

  20. Gavin (I’m not sure where this will appear as the “Reply” box has vanished from my screen).

    Please correct me if I’m wrong, but I think that the explanation of Mercury’s orbit via Vulcan could in principle have been right. The method used – deducing the existence of an hitherto unknown planet by otherwise unexplained movement of other planets – had successfully “predicted” the location of Neptune. I think that was done by the same man who thought Vulcan would solve the Mercury problem so he can’t be blamed for trying.

    I think what was particularly relevant to your point about consensus is that people tried very hard for a long time to find Vulcan as they didn’t know of any better reason for the perturbation of Mercury’s orbit. Several times they thought they’d found it. They only stopped looking when GR explained it so neatly (I think Einstein is reputed to have said that when he worked it out and found a near perfect match to measurements, it was the most exciting moment of his scientific life, which, given the competition, must have been fairly thrilling). But then there was no serious struggle from Vulcan die-hards.

    The key things were: a clear anomaly with a theory, smart people trying hard for a long time to make current theory work, new theory explains anomaly at a stroke (as well as everything the old theory predicted successfully).

    The difficulty in fields like climate modelling is getting a clear prediction which can be cleanly tested. If N’s theory of gravity had had the uncertainties and errors of climate models, there wouldn’t have been any anomaly in Mercury’s orbit to detect.

  21. Simon A: I believe you have it exactly right about the mysteries of gravitation. Newton’s theory made testable predictions about the orbit of Mercury — real-world observations showed those predictions were wrong. At that point, there were two possibilities:
    a. There were other masses in the Solar System that had not been included in the model (such as the hypothesized Planet Vulcan).
    b. Newton’s theory was wrong, or at least incomplete.

    A key part of Science is developing theories which are consistent with existing observations and which can predict things which no-one has yet observed. For example, Einstein’s theory predicted that light would be bent when passing a large mass — a prediction which was confirmed by observing the positions of stars during a solar eclipse.

    Climate modeling (to be more precise, its parent hypothesis of Anthropogenic Global Warming) fails to match known history, such as the Medieval Warm Period, the Little Ice Age, or the repeating Ice Ages themselves. And its clear principal prediction — that anthropogenic CO2 would lead to runaway Global Warming — has been demonstrated to be false.

    There is not even a scientific hypothesis for Anthropogenic Climate Change — only for Anthropogenic Global Warming caused by anthropogenic emissions of carbon dioxide. And that hypothesis has failed. What should concern us all is the continuing taxpayer support for a hypothesis which has been demonstrated to be wrong. We should also take note of the willingness of those who falsely call themselves “scientists” to prostitute themselves for the Almighty Research Grant.

  22. An interesting thing is that Ferguson could be “wrong” by, say, 30% (as people have claimed from running his code) in his forecast of the ICU requirements due to C-19 and still be useful, while a climate model may have an error of less than, say, 2% and be useless.

    It’s because F’s prediction was that the required ICU capacity would be more than 30x greater than was available so if he was 30% too high, it made no difference to the conclusion – the NHS would collapse.

    In contrast, if the climate modellers get their temperature predictions for, say, the year 2100, wrong by just 2% (of the Earth’s atmospheric temperature, ~288K), it makes all the difference in the world to what should be done.

    And since different climate models make predictions which differ by such amounts, it’s not at all clear that they’re useful.

  23. Simon A: “… it made no difference to the conclusion – the NHS would collapse.”

    That is the bogey-man the alarmists have used to scare people. However, it is not realistic. The NHS would not collapse — it would simply reach full capacity and then adjust.

    Once medical facilities were at capacity, the medical profession would practice the standard procedure of triage. People who would probably survive (the overwhelming majority in the case of C-19) would get a sympathetic word and a sticking plaster. Those who would probably die would get introduced to a priest. And the medical professionals would focus on those who were seriously at risk but salvageable. It is what happens whenever there is a multi-casualty incident, like a bus crash or a train crash — which temporarily overloads medical capacity in a particular area.

    Really, what would be the difference between C-19 maxing out hospital capacity versus the standard NHS practice of putting people who need dialysis on a waiting list? We don’t call that collapse.

  24. Dene, I don’t know you so will say this as politely as possible. Science and ‘belief’ are opposite ends of the spectrum. Consensus does not exist in science, it can’t. Science is about constantly testing and adjusting given outcomes of experiments and experience. No-one ‘believes’ in most scientific theories that underpin the majority of technology in the world, or our postulations about the universe. They are accepted as the best we have at any given time, but the whole point of the scientific method is to contantly test those theories and adjust.
    So the current fad of hiding behind ‘what science tells us’ is a falsehood. By its very nature science is a voyage of discovery which is forever changing. Politicians, whether for covid-19 policies or climate change , are misusing the concept of science as something they can use to justify their own decisions. And people who are little more than snake oil salesmen trying to make out they are scientists to justify their own agendas are charlatans.

  25. “I accept the scientific consensus on climate change, but I don’t take climate predictions as gospel.”

    Good article. Others have picked up on science not being a matter of consensus etc, “scientific consensus” being a recurring media motif serving no purpose than to censor opposition to the ‘scientific consensus’, as if ‘consensus’ were proof against contradiction, which it pretty much is in the media where the currency of truth has never been lower.

    Even if science consists in nothing more than the pronouncements of scientists, and that’s all the institution or enactment of science *can* be, we still need to distinguish between science understood in an ideal or Platonic sense as pure uninterpreted truth, what is known to be true independently of any particular human perspective, from science as practised at any point in time, i.e. the professional opinion of scientists.

    Suppose the scientific consensus about X happens to be true scientifically, that’s a fact about the institution of science not in itself a scientific fact, assuming ‘science’ here to be natural science rather than the human or social sciences.

    It’s confusing because the same word is used for two entwined but different things: the exercise of science and the ideal of science as a realm of pure truth. The philosopher Wittgenstein said we’re “bewitched by language” and this is the kind of thing he had in mind: the ideal meaning of a word or thing and its perfectly intelligible use in everyday talk however that might deviate from the ideal or Platonic meaning.

    In a YouTube video Professor Michel Chussodovsky claims that some scientists are making alarmist CV-19 claims to the media which contradict their own published papers! In a poll of scientists, asked if they’d ever faked results 2% answered yes. Asked if they knew any fraudulent scientists, the answer was 14% in the affirmative.

    I got that from a review of a recently published book ‘Fraud in the Lab: The High Stakes of Scientific Research’. It’s also a theme in Hector’s novel where a psychology professor’s research project is shown to be pure invention, in the service of his own aggrandisment. But even if scientists were telling the truth and universally agree it doesn’t follow that they’re scientifically correct.

    Another angle here which bears on the university is the wholesale rejection of Truth by the academic left. The philosopher Nietzsche argued that there could be no such thing as truth in itself, only interpretation: your perspective and his and mine. Even the purest mathematical theory is bound to serve some interest if only as an expression of the will of its human author.

    From the perspective of “life” as a field of competing natures or wills, he interprets science as “will to truth”. However we idealise Science/Truth, its laws or codes can have no source than the creature we happen to be. Truth is no more or no less bodily in origin than lies, or anything else that could be thought or said.

    The notion of standing in judgement on the value of truth over lies itself presupposes a value judgement: valuing the ideal or theoretical over the concrete or real. No spiritual realm could or should take precedence over the exigencies of life itself.

    For Nietzsche, science as pure truth derives from the Christian idea of the one true God, its ontological antecedent. The scientist himself being the latest incarnation of what he called “ascetic values”: the man of God or “priestly type” having renounced faith in the one true God for faith in Truth itself.

    Put crudely, science has succeeded Christianity as a refuge for intellectual or nerdy types who use their wiles to undermine “life” understood as “healthy” aristocratic or warrior virtues. Christianity is to be understood as “Platonism for the people”, Socrates the prototypical Western intellectual representing a decline from when winning and victory were prized over mere truth or correctness.

    The pithiest rebuttal of N’s rejection of truth is Roger Scruton’s: if someone says there’s no truth, they’re asking you not to believe them. Which also points to the function of Truth as an independent arbiter without which power must prevail.

    Nietzsche is the philosopher of fascism exalting power as the supreme value. His denial of truth isn’t some purely intellectual affair but integral to his pitiless aristocratic ideal, that the greater mass of men should not only exist but suffer in the service of their betters.

    But because of his rejection of common morality or bourgeois respectability he’s the go-to thinker for the academic left who are as arbitrary about what qualifies as science as they are in selecting what serves their purpose from Nietzsche’s corpus.

    People familiar with N know this already. But it’s impossible to imagine a thinker more at odds with the egalitarian ethos which we typically associate with the left. The other great philosopher who’s a kingpin on the academic left is the conservative nationalist Martin Heidegger one time Nazi party member. Then again if there’s no such thing as truth…

  26. JimW, yes, I’m aware that the consensus I referred to is about what most scientists working in a particular field accept as the current state of knowledge, and that science itself is a continual process of discovery – I said the latter in my article.

    What no one on here who complains about my reference to scientific consensus – obviously a figure of speech for what most scientists working on the subject accept – has explained is how Joe Public is supposed to do to decide which science on AGW (or anything else) to accept. If it’s not to accept the consensus then what is it?

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