## Posts Tagged ‘Mathematical model’

### Fatality League: Without the 70+ deaths, Sweden would be doing very well

April 14, 2020

The pandemic is far from over, but as some countries contemplate or begin to relax their lockdowns, it is not too early to begin to look at some of the emerging data.

With its 10.1 million population Sweden has suffered over 1,000 deaths due to Covid-19. In the fatality league (deaths/million of population), Sweden has by far the worst numbers in Scandinavia and lies among the worst 10 globally (and in Europe) as of 14th April 2020. (Countries with fewer than 100 deaths are not included). Almost 90% of the fatalities in Sweden are of those over 70. Without these included, Sweden would have a fatality rate just one tenth of that observed.

I merely observe that in a mathematical model which gives little value to the lives of those over 70, Sweden would be doing very well indeed.

The questions are accumulating but any attempt at answers will have to wait at least a year.

Death League (as of 14th April 2020)

Some of the questions that will have to be addressed in Sweden at some time are:

• Most countries have used “worst case” models (which are always ridiculously alarmist) but Sweden has used, it seems, “best case” models. Why?
• Do the mathematical models give a lower “value” to the lives of the 70+?
• Policy has been to restrict the movement of those over 70 to reduce load on the health service in case they are infected. However the infection carriers are the young and the mobile. Has the policy led to more of the 70+ being infected or less?
• How much of the spread of infection was initiated/due to returning, asymptomatic tourists from the Alps who were neither tested nor quarantined?
• How many of the 70+ were “prisoners” to infection in their care homes?
• Was the main source of infection in the care homes through infected but asymptomatic staff?
• Sweden has more than doubled Intensive Care Places in the last month. By establishing “priorities” for intensive care, Sweden has succeeded in ensuring that they have not been full to the limit. How many of the 70+ were denied Intensive Care due to the “priorities” established by the Social Welfare Board? (Priority is based on “expected life remaining”).
• Was it optimal that unlike in other countries, leadership was abdicated by politicians in favour of the officials of the National Health Board.
• …….
• ….

### We learn about climate only when the models are wrong!

March 29, 2013

When a forecast based on a mathematical model is correct, we learn nothing.

A mathematical model is merely a theory, a simplification of reality or an approximation to the real world. By definition a mathematical model is a hypothesis.  When forecasts are incorrect, we can return to our model and improve it and make a new hypothesis. A forecast is then a test of the model but in just one particular set of circumstances. Being correct does not prove the theory behind the model. It does of course add to the body of evidence that the model may be a satisfactory representation of reality and it does allow further forecasts to be made without tweaking the model. For learning to take place the mathematical model must be the falsifiable hypothesis of the scientific method.

It seems to me that Solar Science has a much healthier (scientifically) attitude to models and forecasts than “Climate Science”. When observations don’t match a climate forecast, the observations are impugned rather than the models being improved. This is, I think, because the forecast climate results have been used to establish huge revenue flows in the political arena (whether as taxes or carbon credits or just as research funding). There has been a vested interest in denying the observations and calling the science “settled”. Once the science is “settled”  the climate forecast and its underlying model become sacrosanct and take on the certainty of prophecy. Instead of being falsifiable hypotheses, climate model forecasts have taken on the character of unfalsifiable prophecies!

No scientist would presume to claim that we know or understand all solar effects. Or that we know and understand the role of the oceans or of the water vapour and dust and aerosols in the atmosphere. “Climate” is contained in the thin, chaotic layer of atmosphere which surrounds us. Yet “Climate Science” makes the arrogant assumption that the effect of trace amounts of carbon dioxide on climate is known definitively. Filling a real greenhouse with higher concentrations of carbon dioxide does not make that greenhouse any warmer than one filled with normal air – but the plants do grow faster with access to the additional CO2!! But – claim the climate priesthood –  in the real atmosphere, carbon dioxide causes other forcings (clouds? aerosols? precipitation effects?) which maximise warming which means that our model is still valid. Why not just admit that we don’t know what we don’t know?

The behavioural issue of course is whether it is worth trying to control something as poorly understood as climate rather than ensuring that we have the wherewithal to adapt to whatever changes may come. Another ice age will surely come whether in 10 years or a 100 years or 2,000. It will then be our ability to harness all available energy sources around us which will determine our capacity to adapt.

Learning from forecasts when they are wrong – not just in science but also in business and project management and technology development – has long been a hobby-horse of mine and is why forecasts need to be wrong.

When there is no difference there is no learning.

• I take prophecies to be a promise about the future  based primarily on faith and made by prophets , witchdoctors, soothsayers and politicians such as ”You will be doomed to eternal damnation if you don’t do as I say”,
• I take “forecasts” to be an estimate of future conditions based on known data with the use of calculations, logic, judgement, some intuition and even some faith. They are extrapolations of historical conditions to anticipate – and thereby plan for -future conditions.

……. Over the last 30 years I have spent of a lot of time conducting and participating in reviews. Reviews of research projects, of construction projects, of organisations and processes, of designs, of strategies and action plans, of businesses and of companies. The common features  in all these different reviews, that I have found the most penetrating, have been the comparisons not only between forecast values  and actual values, (which may be any values indicating performance and capable of being extrapolated), but also between past forecasts and current forecasts.

Whether considering construction progress or costs or sales figures or cash flow or profit or number of patents applied for, it is the differences between forecast and actual values, or values forecast before and values forecast later which have led to learning. In all these fields we are in the area of the behaviour of complex systems; and where people and their behaviour is involved any system is inevitably a complex system.

When a forecast is fulfilled there is usually an air of congratulation, satisfaction and self-adulation and this leads to a deadly complacency that everything is “settled science” and well understood. In any enterprise of any kind, that kind of complacency is the kiss of death. It is the differences which lead to questioning, to proper scientific scepticism, to further investigation and ultimately to an increase of understanding and – perhaps – a better forecast. (Of course, ignoring all such differences  and to merely “continue as before” can be equally fatal).

Which brings me to climate (which is not a science by any stretch of the imagination) and solar cycles. They are both in the realm not only of where “what we know is a great deal less than what we don’t know” but they are also both in the region where “we don’t even know what we don’t know”. We do not even know all the questions to be asked. They are both complex systems where – by definition – the complexity lies in the multitude of the processes involved and their interactions.

When climate – which is contained in the 100 m of ocean and 20 or so km thick, turbulent and chaotic atmospheric layer (and which is dimensionally miniscule in relation to the 140 million km of the earth-to-sun system) – is so complacently considered to be “settled science” then we have shifted into the area of faith and soothsaying and prophecies. When climate modellers are smug enough to believe they have understood the climate system and believe that their models are complete, then the models produce outputs which are not forecasts but prophecies. (No doubt soothsayers and shamans have sometimes made accurate prophecies but I still would not buy a used car from one of them)! Weather is in the realm of forecast (though you could argue that the most accurate forecast is still that “the weather tomorrow will be like today”) but climate is not yet there.

This kind of “arrogance” which pervades some of the climate “scientists” is not so prevalent when it comes to the study of Solar Cycles. There is a clear understanding that “we don’t know what we don’t know”. In addition to the 11 year and 22 year cycles, other cycles are hypothesised for 87 years, 210 years, 2300 years (or maybe 2241 or 2500 years) and 6000 years. We have no idea what causes these cycles. Even the 11 year cycle which has been most studied produces  surprises every day but is properly in the area of “forecast” (and hopefully never again will be in the area of prophecy). ….

…… We seem to be in a solar minimum. We may be seeing a 210 year cycle – or maybe not. There are changes to the forecasts not only regarding the maximum level of sunspot activity but also about when it will occur and what the length of cycle 24 might be. There is speculation as to what effect the length of the solar cycle may have on climate – but we haven’t a clue as to what mechanisms may be involved.  This is not to say that there isn’t much speculation and hypothesising. There is a great deal of comment about the effect these changing forecasts may have on global warming or cooling or climate disruption.  In some quarters there is much glee that the forecasts have been “wrong”. Some comments question the intelligence of the forecasters.

But of course the forecasts themselves say nothing about how the behaviour of the sun may impact our climate. They do not pretend to be prophecies or to be statements of inevitable outcomes. All they do say is that we don’t know very much – yet – about the sun. But we do know enough to make some tentative forecasts.

But I am very glad that people continue to be brave enough to make forecasts and I am quite relieved that the forecasts are not spot on. That at least ensures we will continue learning.