Tinfoil hats

The Oz has been running a string of articles accusing the Bureau of Meteorology of a conspiracy to falsify temperature data to promote the theory of global warming. The latest (no link) is by Maurice Newman, chair of the Prime Minister’s Business Advisory Council.

The ultimate source of this nonsense is Jennifer Marohasy, formerly a Senior Fellow at the IPA, well known to long term readers here. She has pushed all kinds of anti-science nonsense on her blog, even running to attacks on the Big Bang theory. Her material got so crazy that even the IPA had to let her go.

Newman’s tinfoil hat antics have attracted a lot of attention and criticism, given his prominent role in advising the Abbott government. It’s obvious enough that this kind of delusional thinking can’t be confined to one topic.

The problem is that this kind of lunacy is the rule, not the exception on the political right, and particularly in the Newman demographic (conservative older males). The more “hardheaded” they imagine themselves to be, the more prone they are to idiotic self-delusion. Examples such as Nick Minchin, Alan Oxley, Don Aitkin, Peter Walsh, and Dick Warburton come to mind .

In fact, I can’t immediately think of anyone fitting this profile (60+ politically active conservative male) who isn’t a member of the tinfoil hat brigade. It’s little wonder that the Abbott government is so disconnected from economic reality, when its thinking is informed by people like this.

120 thoughts on “Tinfoil hats

  1. phoenix :
    Trewin, B. 2013, A daily homogenized temperature data set for Australia, International Journal of Climatology, Volume 33, see especially page 1524)

    Nice try at obfuscation but it doesn’t wash. Climate change is not just about modelling by one specialist group. Its about a collection of observational data and modelling outputs all of which are saying the same thing. We are stuffing the planet and our energy production and economic systems are at the heart of it.. Conversely we know there is a push of powerful people and organizations with no expertise but lots of money who are opposing these findings and even keep changing their tunes. Which is all explicable when you understand the support these arguments give to greedy rich psychopathic fat cats.

    So your selective limited identification of articles you don’t like is problematic.


    I find this a pity as you do indirectly raise an interesting problem which is extremely worthy of discussion.

    In philosophy of science theories we now have Kuhnian paradigm ideas and Popper hypothesis testing/falsification. But with the success and scale of science has come the need to integrate what used to be somewhat siloed disciplines. To cope with this need we have applied conceptual and mathematical modelling, and this data homogenization is one small example. Unfortunately as any good practitioner knows ‘all models are wrong’ but some are useful’. Here is a popular text on the challenge BURNHAM, K. P. & ANDERSON, D. R. 2002. Model selection and inference:A Practical Information-Theoretic Approach (2nd Ed.). And this side of the philosophy of science is still evolving and hence open to thoughtless criticism.

    What seems to have happenned is we have found modelling fantastically useful but we havent done fully develop a coherent theory of how to do modelling (with the possible exception of Bayes?) – or at least not that I’ve seen so far. e.g. there is no Hippocratic Oath for modellers though there is awareness of the traps captured in the old GIGO epithet http://en.wikipedia.org/wiki/Garbage_in,_garbage_out

    Occasionally you hear reference to Occam’s razor and parsimony but the answer to the question ‘How many parameters does it take to model an elephant’ still lingers as a reminder that more work is needed and not just on the stats tools.

    Unfortunately this limitation also provides people such as yourself with a club to hit sincere honest, if at times a bit eccentric, scientists who offend the world view of denialists.

    At the same time you fail to apply this same concern where it is desperately needed – to the problematic nature of empirical modelling and the dreaded metamodelling and metanalysis to the biggest laugh in the room – neoclassical economics and its bag of roosters – whose credibility relies on the sad fact that most serious scientific modellers have been unaware of the mixture of useful insight and ad hocery it comprises and are uninterested in falsifying its worst howlers.

    In summary you are right to wonder about modelling and raise the issue. But if you must can you do this in a balanced informed fashion and not just indulge in rhetoric and bias confirmation it would be preferable.

  2. @phoenix
    “Tinfoil Hats” is useful shorthand for “This person is barking mad. Back away slowly, and don’t make eye-contact.”

    There’s also a certain amount of gentle mockery in it. It’s not so long ago that anyone who wondered if the fossil fuel industry was attempting to suppress evidence of climate change was accused by the tribal Right of wearing a tinfoil hat.

  3. @Newtownian I don’t think there is any point in debating with Phoenix. The post is not aimed at persuading people like him, it’s about the problem they pose for a reality-based politics.

  4. Homogenized is such a good word, It sounds so scientific and technical. It means “We adjusted the data” As in I homogenized the figures in my business case, Or I homogenized the results in my mine assay. Even Professors should know this.

  5. Tks John. Points taken. I’ll try to reduce my post lengths.

    That said I still think there is a place form discussing models of all kinds and their outputs and how to view them. Hence my response.

    Recent dabbling in Bayesian Belief Nets and the challenge of communicating how models work to colleagues has brought home to me how even smart people can have trouble understanding them and so have to use ‘faith’ in the model authors because they simply dont have sufficient time to understand them fully.

    Regrettably then ‘Tinfoil hat’ wearers exploit these uncertainties in bad faith and make communication still harder when it comes to climate change and sustainability issues.

  6. Thank you JQ for allowing my post to go through moderation and I thank other ‘believers’ in the pseudo-science of climate change, who have a preference for virtual over observational data for responding to my posts.

    My personal preference is to ‘believe’ the observational data over the virtual. in any case you can get a look at some of the raw data here and form your own ‘beliefs’.

    Click to access Changing_Temperature_Data.pdf

    It is not my job to convert anybody as everyone has the right to their own opinion based on their world view.

    Kind regards,


  7. @Newtownian

    Exactly how does science differ from social science and Marxism.

    Science is a broad endeavour based on evidence and analysis of evidence. This applies to both the social sciences and Marxism.

    Science is a technique that has wide applicability.

  8. @phoenix

    If people are described as idiots, fools, kooks, cranks, or ‘tinfoil hats’, that has little or no value as evidence that they’re wrong; but it also has little or no value as evidence that they’re wrong. If A calls B a ‘tinfoil hat’, that tells us nothing, or almost nothing, about which of them is likely to be right on any issue on which they disagree.

  9. Sorry, I meant ‘little or no value as evidence that they’re wrong, but also little or no value as evidence that they’re right’. Being insulted doesn’t make you any more likely to be correct.

  10. That is one way of looking at it J-D,

    With faith based arguments (like AGW) no one is correct or wrong, as there are no answers, only infinite questions….unless you ‘believe’.

    But, if one is trying to make assertions, then it is far better to demonstrate credibility contained in ones own cranium, before ridiculing what someone else puts on theirs.

  11. Ivor :
    Exactly how does science differ from social science and Marxism.
    Science is a broad endeavour based on evidence and analysis of evidence. This applies to both the social sciences and Marxism.
    Science is a technique that has wide applicability.

    Tks for asking.

    Its a bit of a grey scale but IMHO there is a distinct demarcation between hard science compared to religion, social science, and ideology be it Marxism, neoliberalism (current flavor of the month), belief in witches or paranoia based conspiracy theory.

    The latter develop a set of central narratives which become locked in place and defended by various tools, some of which are useful and worthy of respect e.g. logic, some of which have no place in seeking wisdom – e.g. rhetoric, dogmatic appeals to authority. The longer things go on the more rigid the narratives become. Some scientists will use the latter tactics but in the long run they fail the laugh test…a sense of humour is essential in science given the surprises the universe throws up whereas ideology and religion are marked by a lack thereof.

    The modern version of hard science is/must be open to the central narrative in any given discipline being overturned or seriously modified. A good recent example is epigenetics http://en.wikipedia.org/wiki/Epigenetics which very satisfyingly complicated the nature v. nuture inherence debate – but has done so consistent with previous broader biological knowledge.

    Separately there are complications which may explain your own puzzlement – science’s success has been so great that it looks like there is no difference to softer disciplines. More dodgy disciplines have claimed to be hard science e.g. pyschology and economics. Science is hard to communicate so its findings often has to be taken “on faith” operationally by time poor users. To understand its concepts the terminology and concepts need to be first learnt essentially as rote which should be followed by questioning but isnt always. And many have turned it into or presented it as a faith like belief system – termed Scientism.

    By way of exemplifying the difference I recently shared a student with a friend from a social science background and experienced how different the grand narrative development process was to the science/experiment/measurement/model/testing approach style (many engineers use this) I was used to. The knowledge extracted and analysed by the student was interesting and very stimulating but it was still replete with aspects which prevented really hard reality checks.

    A problem arising I think is that its is very hard in practice to frame hypotheses and test them for social science, ideology etc. It does happen (ca 50 failed marxist attempts seems not a bad test). But its still pretty haphazard and hasnt buried the central narrative for a hard core. Now it is possible to test hypotheses in some social sciences to some degree but its limited by the complexity of many such disciplines and as importantly a lack of powerful (efficient/concise, elegant, predictive, testable) ‘atomic theories’.

    Beyond this you should note this divide has long been known. If you are interested in knowing more about the divide I suggest reading SNOW, C. P. 1959. The Two Cultures. Its a classic.I work at the science/social science interface so its been a fascination for some time and this is a great little read.

  12. @phoenix

    ‘Being insulted does not make you more likely to be correct’ is one way of looking at things, and ‘being insulted does make you more likely to be correct’ is another way of looking at things; but ‘being insulted does not make you more likely to be correct’ is an accurate way of looking at things, and ‘being insulted does make you more likely to be correct’ is a misguided way of looking at things.

    The course of wisdom is to proportion belief to evidence, and no kind of faith changes that. Faith can’t stop an error from being an error.

    I am satisfied, on the basis of the evidence, that a direct effect of an increase in the atmospheric concentration of carbon dioxide is to make the planet’s temperature higher than it would otherwise have been, and I’m more than happy to explain how if you doubt this conclusion.

  13. @Newtownian

    Introducing and separating “hard” science from other science does not answer the question, which was:

    Exactly how does science differ from social science and Marxism?

    Experimental science also is not representative of science. Only some science progresses by experiment.

    There is certainly no value in concepts such as “dodgy disciplines” except where a discipline is not based on evidence – astrology, theology.

    All science is based on hard evidence. It is just that some science is more capable of being impacted by politics.

  14. @Ivor
    As I said it is a bit of a grey scale – but in my experience and professional work, which does span examples on both sides, at the coal face, as well as involving application, has shown me the demarcation is there and is quite striking.

    This is because people from only one side of the fence or other – are pretty clueless about the other side in part because they arent very aware of the different ways of thinking and approaching problems in the hard v. soft sciences – among which actually I would include astrology and theology – afterall astrology provided a starting point for understanding the seasons and their patterns -.

    CP Snow’s observations of us inhabiting different silos explains the problem nicely.

    Regarding what is different about hard science – where properly controlled experiments reflecting a deep and complex network of basic theories and models which are internally consistent – e.g. all the classical hard sciences – physics, chemistry, biology, neurology, geology – as well as their application via engineering and applied sciences – they most certainly do proceed centrally by experiment.

    Experiment isnt everything of course – but it provides something approaching objective tests of beliefs based on theory and that is where social sciences (management, economics, sociology) seem to fall down in my experience. The latter are not useless by any means but they are crippled.

    To illustrate consider alchemy – which afterall did bring us such useful products as gunpowder to improve genocide and arsenic to kill rats. Alchemy v. chemistry is a useful analogy here I think. Alchemy an analogy based on hard evidence was useful – but it didnt show the underlying relationship between say arsenic, carbon, sulphur, oxygen and nitrogen – chemistry did and so we are now much much better at killing things.

    Returning to social science and Marxism. In both I see many differences for example:
    – they have at the centre narratives – hard science doesnt – it just looks that way because the insights it accumulates are so elegant and powerful they look similar but they are subtlely different.
    – social sciences and ideologies and religion appear to be based on a belief we actually understand people and society sufficient to create a grand narrative. Sorry but we dont even understand free will v. determinism which is fundamental. We’ve got many useful insights based on hard science crossover in particular – like Maslow’s hierarchy of needs – so dont get me wrong. I’m not anti social science etc. – but it aint hard science.
    – complexity – hard science deals with problems bit by bit one as a time and then assembles them into its paradigm/grand narrative to which it then applies blow torches like experiments on predictions. This used to be viewed by some perjoratively as convergent thinking suggesting small mindedness . Whereas social scientists referred to themselves as being divergent thinkers. Leaving the perjorative aside this does reflect different approaches.

    A final quibble – evidence is too often not as hard as we would like to believe. Take physics. Its shown that ‘hard’ rock is actually mostly vacuum. This is utterly counterintuitive to our senses but it illustrates the limits of our senses which ideology and social sciences tend to rely on.

    Separately ‘hard evidence’ is/seems often contradictory. So for me getting ‘hard evidence’ is only the first step – its also about how it all fits together, getting rid of inbuilt biases and self-delusion (a problem for our ‘leaders’ as the current incumbent illustrates) and ensuring consistency which means trial and error modification of theories.

    I’m afraid too much social science and ideology like marxism and neoliberalism is not about this. Its not even about politics. In the west at least its about people being locked into old style monotheistic religion based thought patterns of good and evil. This model has its uses but it also encourages inflexibility and dogmatism to an extent I cant abide.

    In summary

  15. @chrisl

    In short Yes – for the most part in respect to the key information and our prospect and what they say about the need for digit extraction.

    But since such as simple answer exposed me to rhetorical devices and oversimplification – my considered answer is – it depends what you mean by the term ‘climate science’ which is an unfortunate shorthand. My friend Ivor’s responses illustrate the confusion imprecision in terminology can raise. So here is the long version.

    ‘Climate science’ is one of those grab bag terms which captures a lot of hard long proven theory plus work in progress – a bit like evolution. The complexity exposes it to nitpicking so its necessary to understand its structure.

    Breaking it down into its components first there is the hard science stuff we known now –
    – the heat exchange physics – too much increase in heat trapping gases will change this – we have lab data and consistent natural experiments – Venus Mars and the Moon.
    – the oceanic circulation stuff – the ocean is the ultimate sink for CO2 but its assimilative capacity is limited and measurable though full precise estimation is still going on.
    – melting the Antarctic and Greenland will be disastrous and lead to feed backs and tipping points – that is simple mass balance – see Tiny Tim and the Other Side.
    – superficially small changes in temperatures have big disastrous climate impacts – hard – look at the ENSO example
    – ocean acidification is happening and measured
    – Greenland and Antarctic melting – measured directly using gravitational satellite data
    – anthropogenic CO2 is a big factor – compare emissions rates with ocean removal and atmospheric increases over the past –
    – preindustrial CO2 fluctuation – well documented from Antarctic ice cores

    etc. etc.

    – future CO2 emissions and impacts – that is part of hard science too though more by way of running the ‘natural experiment’ against all sense and basic risk assessment and management principles – but hey your children can always move to Mars or an earth orbiting habitat on a Virgin spaceship. But scientists unlike climate deniers prefer to do small scale experiments and modelling as best they can – because they dont think this experiment is a good idea.

    – Is climate denial science a hard or soft science? Probably neither, but more like scholastic religion it started with a denialist narrative when some rich pigs like the Kochs realized they were not in a good game and ever since the method has been using obfuscation, bribery, belief in the market God and His invisible hand, bullying, rhetoric and selective quotation to advance the narrative ii.e. its based is bad faith and not approves hard science tools at any rate.

    – The study of climate deniers is probably still a soft science. We have theories on why climate deniers are happy to take a chance on turning the world of their descendents into a living hell – hatred of greenies, psychopathy, short term greed, stupidity, their job is to generate spin – but I havent seen a full statistical testing of the alternatives and that would be hard as they might not being cast as nutters. So its still a soft/proto science.

    – What to do is also a soft science as its based on the rather dodgy social science of economics and faith in the market.

    In summary – the core of climate science is hard science which says we are up s@#t creek when translated into the vernacular. Beyond that is what to do – which is more in the field of softer social science because our institutions, laws, governance are predicated on exponential growth dominated by neoliberal ideology whose existence is being challenged. And they dont like it but will do anything to maintain their hegemony and access to big dinners and comfy chairs.

    Hope that helped.

  16. Yes that helped. I think the problems occur because of the inter-twining of hard and soft science. That Co2 causes warming is undoubtedly true but it doesn’t mean that every prediction/scenario follows from that.I think there is a bit of trouble with predictions of average temperatures and sea ice. Predictions are hard,especially about the future!
    Do you think that modelling is hard science or soft science?

  17. @chrisl

    That is an interesting question. I commented above but need to ponder this further. That said provisionally:
    – There are first conceptual models which might be also viewed as our beliefs or hypotheses or paradigms. You might even say a sky god is a conceptual model of sorts.
    – But how do you sort the wheat from chaff? The useful models from the delusions, flights of fancy, seductive ideals etc. And how do you take us out of the equation ? i.e. be objective. Trying for objectivity and openness to change is what ideologies and social sciences dont do or dont do well in my opinion.

    To become more objective and ‘hard’ a science needs to take models beyond the conceptual is as follows.

    1. You use the language of mathematics to capture their essence and make predictions. – People arent very good calculating things so this makes them hard to fudge up front though hindsight curve fitting is a danger.
    2. You test their predictions and sensitivity to variations in inputs.
    3. You compare the model predictions to other concepts and underlying more basic theory.
    4. You compare to real world observations (H0 tests) and experimental results (H1 tests).

    So modelling starts being soft at the conceptual stage. If it is still standing after the above tests you can view it as increasingly ‘hard’.

    But note that modelling per se is not a hard or soft science – its (increasingly) part of the scientific process. Especially where the aim in to integrate many/disparate inputs. Some models are very simple. Others like those involved in predicting future temperatures.

    When you have a model, predicting the future isnt actually hard – but whether the model is credible, reliable, accurate etc. is the question. So validation is actually the challenge.

    In respect to this scientists take the position you should validate what you can without doing harm which they are trying to do – like flood modellers using historical records do when advising on housing development. Like climate change experts they cant predict absolutely when and how big a future flood will be but you ignore their insights at you peril – as development mad Queensland did to its cost in 2012.

    Meanwhile fossil fuel companies are doing their best to validate their view of no significant climate impact – based on no modelling or science worth dick that I am aware of. Or alternatively they do believe in climate change and don’t give a stuff and are only driven by short term profit.

    Either of the latter is not pretty and pose the question which side you prefer and hence vote for indirectly at elections.

    Finally in regard to melting sea ice and increasing albedo – I trust you have looked at Charctic http://nsidc.org/arcticseaicenews/charctic-interactive-sea-ice-graph/

    The data and models may be a little noisy but the trend is obvious and in line with the models – in fact its probably faster than expected – basically every year since 2007 has been at the upper end of historical melt data so you dont just need to selectively focus on 2012.

  18. @Newtownian

    Maybe we could just dispense with this notion of hard science. Science is based on evidence and cross examination.

    Naturally science, once it has made findings, constructs narratives based on findings. Geologists have narratives about the earths formation. Biologists have narratives about evolution.

    Narratives just mean an intergrated understanding.

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