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Wielding Supercomputers To Make High-Stakes Predictions
aarondubrow writes "The emergence of the uncertainty quantification field was initially spurred in the mid-1990s by the federal government's desire to use computer models to predict the reliability of nuclear weapons. Since then, the toll of high-stake events that could potentially have been better anticipated if improved predictive computer models had been available — like the Columbia disaster, Hurricane Katrina and the World Trade Center collapse after the 9/11 terrorist attacks — has catapulted research on uncertainty quantification to the scientific and engineering forefronts." (Read this with your Texas propaganda filter turned to High.)
...thanks a lot....Ass
sounds a bit shaky to me...
blindly antisocialist = antisocial
Seems to me all the supercomputer models are predicting the disaster called global climate change is powered by human CO2 emissions. We have predicted it. It has a decided human cause against which we can take direct action. Over the next 50 years billions of people will be displaced. Trillions or more of infrastructure will be lost to rising oceans.
Are we doing anything? Seems to me the whole prediction thing is useless if we are unwilling to take action on the results.
Is it because the results are wrong or is it because it involves money in peoples pockets.
We can make the predictions, we need to remove the barriers to action.
Since then, the toll of high-stake events that could potentially have been better anticipated if improved predictive computer models had been available — like the Columbia disaster, Hurricane Katrina and the World Trade Center collapse after the 9/11 terrorist attacks — has catapulted research on uncertainty quantification to the scientific and engineering forefronts
How sure are we that the tolls could have been better anticipated?
We should leverage a super computer to calculate the potential that each high-stake event can be better anticipated by a super computer model. Then simply pool our resources and use greater predictive computing power for the events we have the most potential to anticipate.
I put it to you that once such a model can be computed, it will be trivial to use predictive computer models to determine which super computer will predict the the most accurate results. Thus, we can leave it alone to the task of the predicting, knowing that it has a potentially better chance of anticipating the anticipation.
Furthermore, we could use the predictive models to better anticipate which researcher will be able to quantify the amount of uncertainty quantification needed to quantify quantum uncertainty; They would also be the ones who could finally tell us what quantity of Schrödinger's cat is undead.
Some things can be well-modeled by using good input data and fine-grained analysis, which may require supercomputers.
A problem arises when inherently chaotic (in the mathematical sense) systems are modeled. No amount of computing power will improve the quality of the results.
It may be hard to know what type of system you are dealing with.
And by definition, black swans cannot be modeled at all.
Prove anything by multiplying Huge Number times Tiny Number
What was old is new again.
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Having a supercomputer won't help predict rare events unless you have a particular mathematical model for those events already (see physics). If you don't have a model for how rare events occur (terrorism events, natural disasters) then a computer (of any type) won't help you predict them. If you want to build a model then you needs lots and lots of events (and nonevents) and associated data to try and build a model. If you have a lot of data, perhaps you'd need a supercomputer to investigate the interim models you come up with before you arrive at a final model. You can't predict rare events. Modeling, and statistics in general, is designed to make statements about things given sufficient data. By definition, a one-off event or extremely rare event doesn't provide enough data to allow generalization (or inference).
The only way to validate the model is to apply it and see if it works. The problem with hish risk disasters is that they don't happen all that often so it's hard to validate the model. I mean sure you can special case it to death to get it to predict "the Columbia disaster, Hurricane Katrina and the World Trade Center collapse" but if you special case it too much it loses predictive ability for similar but not identical events.
The reason there is so much "uncertainty" (not for me but many others) around climate change is that it is practically a singular event that'll occur 50-100 years in the future. Of course the models can be validated as we go but how much validation is enough? When it's too late?
I like supercomputers in the same way I like architectural monuments - there's an element of beauty in stretching technology to ever more extreme goals, but I'm far from convinced that there's an objective, practical, point to any of the calculations they make.
I'm very sceptical about climate change prediction - because, without any calculation, it's blindingly obvious that climate will change (all evidence suggests vast changes throughout history) and - because mankind is significant among life on earth - obviously we should assume a fair chunk to be 'man made'. I seldom see the questions that matter addressed... for example, in what ways can we expect climate change to be beneficial to mankind? When we ask the wrong questions, no matter how large-scale or accurate our computation, it will be worthless. Don't get me wrong, I see immense value in forecasting... but I don't see available computational power as a limiting factor... in my opinion there are two critical issues for forecasting: (1) collecting relevant data accurately; (2) establishing the right kind of summaries and models. While some models are computationally expensive - in my opinion - the reason for attempting to brute-force these models has far less to do with objective research and far more to do with political will to have a concrete answer irrespective of its relevance... The complexity of extensive computation is exploited to lend an air of credibility, in most cases, IMHO.
"Don't worry about the future. Or worry, but know that worrying is as effective as trying to solve an algebra equation by chewing bubble gum. The real troubles in your life are apt to be things that never crossed your worried mind, the kind that blindside you at 4 p.m. on some idle Tuesday."
The reason is simple: avoidable disasters occur not because we haven't done enough calculations - but because the calculations we do are done for the wrong reasons and produce irrelevant results. If we want to move forwards, we need more observation and more intelligent consideration. Iterating existing formulas beyond the extent possible with off-the-shelf technology, IMHO, is unlikely to yield anything significant.
The title is misleading and not really correct, because it doesn't describe the main thrust of the project. What the group at Texas is trying to do is change the way computer models make predictions, because they recognize that predicting events like Katrina or 9/11 with any kind of accuracy, based on essentially no data, is basically impossible, and that even when prediction is possible, it's still full of uncertainty.
They don't want the models to spit out a single answer (e.g. "There will be a sea level rise of 10 centimeters within 20 years"), but rather a probability distribution ("The sea level rise over the next twenty years can be modeled as a normal distribution with a mean of 10 centimeters and a standard deviation of 5 centimeters"). The distribution is supposed to be based on uncertainties that arise at various stages in the process of modeling, such as model assumptions and data collection.
Personally, I think in certain cases these techniques are great, and in other cases they are worse than useless. If you have a model that's supposed to predict terrorist attacks, it will happily tell you that with 90% confidence the probability of a terrorist attack in Location X in the next year is between 1 and 3 percent. This may be perfectly correct, but highly misleading, because fundamentally the event is not probabilistic, and the only reason it appears to be is that a key piece of data is missing. As such, what the computer should really do is say the following. "Dear DHS: I don't know whether the terrorists are planning the attack. If they are, it is very likely to occur. If they aren't, it won't happen. Please do your job and go find out whether they are or not, and let me do more interesting things. Sincerely, Computer."
And by definition, black swans cannot be modeled at all.
... because after all these years, I'd still let Natalie Portman model me with a bowl of hot grits anyday.
This is a topic of great interest in aerodynamics. Aim is to understand how uncertainties in the input data (flow conditions, geometric imperfections, ....) affect the predicted aircraft performances. Some research has already taken place in Europe, for example see the project nodesim (http://www.nodesim.eu).
Anonymous Defenders of Texas, forwaaaard maaaarch!
Check your premises.
win the lottery? :)
Who bothers modding down ACs?
To have a right to do a thing is not at all the same as to be right in doing it