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The team let their neural network society evolve over generations, keeping track not only of the strategies that emerged, but also of the level of intelligence their networks evolved. They found it was during phases in which cooperation first starts to emerge in a society that brains tend to become bigger. "The strongest selection for larger, more intelligent brains, occurred when the social groups were first beginning to start cooperating, which then kicked off an evolutionary Machiavellian arms race of one individual trying to outsmart the other by investing in a larger brain. Our digital organisms typically start to evolve more complex 'brains' when their societies first begin to develop cooperation." explains Jackson.
And the more intelligent brains also produced more intelligent strategies, involving forgiveness and patience as well as deceit and trickery. Their results, so the scientists argue, provide some evidence for the social intelligence hypothesis. So next time you're tempted to do the dirty on someone, remember: without cooperation and kindness, you may never have evolved the brain that enables you to cheat in the first place.
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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The Australia Institute has released a very nice report by Richard Denniss titled, The use and abuse of economic modelling in Australia: Users' guide to Tricks of the Trade (PDF). The essay illustrates its critique with several recent cases related to claims about jobs in the mining industry, the poker machine industry and as a consequence of the carbon tax.
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Economic modelling has, for many people involved in Australian policy debates, become synonymous with the process of serious policy development. Proponents of policy change that are armed with economic modeling are often taken more seriously than those with 20 years experience working on the same problem. The modelling result that suggests tens of thousands of jobs will be lost or created often trumps logic or experience that suggests such claims are nonsensical.
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some of the claims based on 'economic modelling' that has been made in debates such as the likely impact of poker machine reform or the introduction of a carbon price can only be described as nonsense.
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fiscal and monetary policy are inextricably linked, and their research reflects the broad shift in economics from words to numbers — toward a level of empirical analysis that few outside the profession can readily grasp. But it contains a kernel of skepticism appropriate for these troubled times. In a world of uncertainty and constraint, cause and effect may not be what they seem. As a result, we must test and retest our assumptions — and try to prepare for the unexpected.
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“The most impressive thing about them as scholars,” says David Easley, an economist at Cornell University, “is that in recent years they have questioned the assumptions of the models they helped to create, and they have been at the vanguard of the efforts to go beyond them.”
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many physicists believe that the past, present and future all exist simultaneously in a block Universe and our perception of time is just an illusion. Sure, you can watch one scene in your movie morph into the next, but they are all pre-recorded on that little DVD. As for free will — well, that went out with Beta tapes.
But now, George Ellis at the University of Cape Town, South Africa, is challenging that notion and trying to restore the idea that "now" is special and that free will exists with his model of a crystallising block Universe. "Free will is such a controversial thing," says Ellis, yet, "it is indeed one of the underlying things which motivated me."
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many physicists believe that the past, present and future all exist simultaneously in a block Universe and our perception of time is just an illusion. Sure, you can watch one scene in your movie morph into the next, but they are all pre-recorded on that little DVD. As for free will — well, that went out with Beta tapes.
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The view that the past, the present and the future are of exactly the same physical character seems to be supported by Einstein's special theory of relativity, which describes how observers moving relative to one another may disagree about the order that events occur, preventing them from defining a unique and universal now (see the Plus article What is time?). However, in his prize-winning FQXi essay On the flow of time Ellis maintains that the most important property of time is that it unfolds. The past is already written, yes, but the future contains endless possibilities. To Ellis, the history of the Universe is a film that is still being made.
"People must take seriously the fact that time does evolve," says Ellis. If the models don't jibe with our perception of reality, he argues, maybe the problem is with the models. "Some of my colleagues seem to think their models trump reality!"
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With countless biological details emerging from cancer experiments, there is a growing need for minimal mathematical models which simultaneously advance our understanding of single tumors and metastasis, provide patient-personalized predictions, whilst avoiding excessive hard-to-measure input parameters which complicate simulation, analysis and interpretation. Here we present a model built around a co-evolving resource network and cell population, yielding good agreement with primary tumors in a murine mammary cell line EMT6-HER2 model in BALB/c mice and with clinical metastasis data. Seeding data about the tumor and its vasculature from in vivo images, our model predicts corridors of future tumor growth behavior and intervention response. A scaling relation enables the estimation of a tumor's most likely evolution and pinpoints specific target sites to control growth. Our findings suggest that the clinically separate phenomena of individual tumor growth and metastasis can be viewed as mathematical copies of each other differentiated only by network structure.
One of the most difficult things about treating cancer is that each case is a very individual process; tumors recruit blood vessels in order to grow, yet also send out new vessels of their own. This harnessing of the body’s resources is what eventually allows a tumor to metastasize — be carried into other parts of the body where growth continues. On the other hand, sometimes tumors don’t grow at all. Predicting each patient’s unique response to cancer and its progression is a large part of the battle, as an accurate estimate is required to begin appropriate treatment. The field of Oncology is always seeing exciting developments, and this latest one is no different — its benefits touch future and existing cancer patients, alike. Knowing that hindsight is 20/20, it would be a lot easier if there was a “fast-forward button” with which doctors could view each unique case before it develops.
Physicist Sehyo Choe and colleagues at the University of Heidelberg, Germany have developed such a button in the form of a mathematical model. By inputting data about the tumor and its current location of blood vessels, the model allows doctors and researchers to see how the tumor will grow and move — if at all — giving them a very accurate helping hand when it comes to prompt and accurate treatment. Tested on mice, the model accurately predicted the progression of all cancer-stricken subjects, giving researchers that amazing “fast-forward” capability.
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Predicting each patient’s unique response to cancer and its progression is a large part of the battle, as an accurate estimate is required to begin appropriate treatment. The field of Oncology is always seeing exciting developments, and this latest one is no different — its benefits touch future and existing cancer patients, alike. Knowing that hindsight is 20/20, it would be a lot easier if there was a “fast-forward button” with which doctors could view each unique case before it develops.
Physicist Sehyo Choe and colleagues at the University of Heidelberg, Germany have developed such a button in the form of a mathematical model. By inputting data about the tumor and its current location of blood vessels, the model allows doctors and researchers to see how the tumor will grow and move — if at all — giving them a very accurate helping hand when it comes to prompt and accurate treatment. Tested on mice, the model accurately predicted the progression of all cancer-stricken subjects, giving researchers that amazing “fast-forward” capability.
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Co-author Neil Johnson from the University of Miami says that “in the future, treatments will no longer have to be based on population averages. People will get individual treatment based on the predictions of our model.”
In May, the United Nations’ International Panel on Climate Change made media waves with a new report on renewable energy. As in the past, the IPCC first issued a short summary; only later would it reveal all of the data. So it was left up to the IPCC’s spin-doctors to present the take-home message for journalists.
The first line of the IPCC’s press release declared, “Close to 80% of the world‘s energy supply could be met by renewables by mid-century if backed by the right enabling public policies.” That story was repeated by media organizations worldwide.
Last month, the IPCC released the full report, together with the data behind this startlingly optimistic claim. Only then did it emerge that it was based solely on the most optimistic of 164 modeling scenarios that researchers investigated. And this single scenario stemmed from a single study that was traced back to a report by the environmental organization Greenpeace. The author of that report – a Greenpeace staff member – was one of the IPCC’s lead authors.
The claim rested on the assumption of a large reduction in global energy use. Given the number of people climbing out of poverty in China and India, that is a deeply implausible scenario.
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This sort of behavior – with activists and big energy companies uniting to applaud anything that suggests a need for increased subsidies to alternative energy – was famously captured by the so-called “bootleggers and Baptists” theory of politics.
The theory grew out of the experience of the southern United States, where many jurisdictions required stores to close on Sunday, thus preventing the sale of alcohol. The regulation was supported by religious groups for moral reasons, but also by bootleggers, because they had the market to themselves on Sundays. Politicians would adopt the Baptists’ pious rhetoric, while quietly taking campaign contributions from the criminals.
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Of course, today’s climate-change “bootleggers” are not engaged in any illegal behavior. But the self-interest of energy companies, biofuel producers, insurance firms, lobbyists, and others in supporting “green” policies is a point that is often missed.
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Do cell phones cause cancer? Nobody really knows for sure, but scientists are determined to keep an eye on the ever-evolving evidence that continues to accumulate on the subject.
That’s the gist of a report recently released by the World Health Organization’s International Agency for Research on Cancer (IARC), the United Nations body responsible for oncological studies. In the report, IARC scientists have classified cell phone usage as a possible cause of cancer, meaning that, while the data currently available is still inconclusive, the subject deserves further research before a call can be made one way or another.
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Today’s cell phones are, essentially, extremely sophisticated radios and, as such, emit electromagnetic waves. Much like the vast majority of radiation that surrounds us—from visible light to AM and FM radio waves—electromagnetic waves do not possess enough energy to interact directly with the tissues in our bodies in a way that can cause direct damage.
“The radiation that cell phones emit is nowhere near the kind of radiation that x-ray machines, for example, emit,” says Perras. “X-rays […] have much, much shorter wavelengths. Consequently, [they] carry much more energy and thus have much more penetrating power, which is required to be able to image the interior of the human body.”
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X-rays and other “hard” waves are called ionizing radiation because they can interact with the human body in a way that leads to the creation of chemical compounds called free radicals that can, in turn, be responsible for mutations and the incidence of cancer.
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Your concern is that insurers and rating agencies, regulators and a lot of people may be relying too heavily on these models. Is there something in particular that has occurred that makes you want to sound this warning, or is this an ongoing concern with these?
Clark: Well, the concern has been ongoing. But I think you’ve probably heard about the new RMS hurricane model that has recently come out. That new model release is certainly sending shockwaves throughout the industry and has heightened interest in what we are doing here and our messages…. [T]he new RMS model is leading to loss estimate changes of over 100 and even 200 percent for many companies, even in Florida. So this has had a huge impact on confidence in the model.
So this particular model update is a very vivid reminder of just how much uncertainty there is in the science underlying the model. It clearly illustrates our messages and the problems of model over reliance. -
But don’t the models have to go where the numbers take them? If that is what is indicated, isn’t that what they should be recommending?
Clark: Well, the problem is the models have actually become over-specified. What that means is that we are trying to model things that we can’t even measure. The further problem with that is that these assumptions that we are trying to model, the loss estimates are highly sensitive to small changes in those assumptions. So there is a huge amount of uncertainty. So just even minor changes in these assumptions, can lead to large swings in the loss estimates. We simply don’t know what the right measures are for these assumptions. That’s what I meant… when I talked about unknowledge.
There are a lot of things that scientists don’t know and they can’t even measure them. Yet we are trying to put that in the model. So that’s really what dictates a lot of the volatility in the loss estimates, versus what we actually know, which is very much less than what we don’t know. - 1 more annotation(s)...
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Karl Smith argues that informal economic arguments — models in the sense of thought experiments, not necessarily backed by equations and/or data-crunching — deserve more respect from the profession.
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misunderstandings in economics come about because people don’t have in their minds any intuitive notion of what it is they’re supposed to be modeling.
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