The recently published study, which suggested that night owls had a 10 percent higher chance of dying, has plenty of flaws. Plus, here are other studies that favor night owls.
In the fall of the year 1480, at a point not far from Moscow, two armies faced each other on the opposite banks of the Ugra River.
On the one side were the forces of the Grand Duchy of Moscow, whose ruler, Grand Prince Ivan III (known as “the Great” and the “gatherer of the Russian lands”), had recently rejected further payment of tribute to the Great Horde.
On the other were the forces of Grand Khan Ahmed bin Küchük, who had come to lay waste to Moscow and instruct the impudent Prince Ivan to mend his ways.
For weeks the two assembled hosts glared at one another, each wary of crossing the water and becoming vulnerable to attack by the other.
In the end, as though heeding the same inaudible signal, both withdrew and hastily returned home.
Thus ended more than two centuries of the Tatar-Mongol yoke upon the land of the Rus’.
Was this event, which came to be known as “the great standing on the Ugra River,” a model of what happened in Syria last week?
Almost immediately upon reports of the staged chemical attack in Douma on April 7, speculation began as to the likely response from the west – which in reality meant from the United States, in turn meaning from President Donald J. Trump. Would Trump, who had repeatedly spoken harshly of his predecessors’ destructive and pointless misadventures in the Middle East, and who just days earlier had signaled his determination to withdraw the several thousand Americans (illegally) stationed in Syria, see through the obvious deception?
Or, whether or not he really believed the patently untrue accusations of Syrian (and Russian) culpability, would Trump take punitive action against Syria? And if so, would it be a demonstrative pinprick of the sort inflicted almost exactly a year earlier in punishment for an obvious false flag chemical attack in Idlib? Or would we see something more “robust” (a word much beloved of laptop bombardiers in Washington) aimed at teaching a lesson to both Syrian President Bashar al-Assad and his ally, Prince Ivan III’s obstreperous heir Russian President Vladimir Putin?
The answer soon came on Twitter. Assad was an “animal.” Putin, Russia, and Iran were “responsible” for “many dead, including women and children, in mindless CHEMICAL attack” – “Big price to pay.”
Around the world, people mentally braced for the worst. Would a global conflagration start in Syria with an American attack on Russian forces? A grim trepidation reminiscent of the October 1962 Cuban Missile Crisis gripped the hearts of those old enough to remember those thirteen days when the fate of all life on our planet was in doubt.
Certainly there were enough voices in the US establishment egging Trump on. Besides, at home he still had the relentless pressure of the Mueller investigation, intensified by the FBI’s April 9 raid on his lawyer Michael Cohen. Trump’s only respite from the incessant hammering was his strike on Syria last year.
During the first Cold War both American and Soviet forces took great care to avoid direct conflict, rightly afraid it could lead to uncontrolled escalation. But now, in this second Cold War, western commentators were positively giddy at the thought of killing Russians in Syria…
…or rather killing more Russians, citing the slaughter of a disputed number of contractors (or “mercenaries” as western media and officials consistently called them, implying they deserved to have been exterminated). That’ll teach ‘em not to tangle with us! It was unclear whether the warning from Russian Chief of Staff General Valery Gerasimov that Russia would respond against an attack by striking both incoming weapons as well as the platforms that launched would be taken seriously.
After a slight softening of tone by both Trump and Defense Secretary General James “Mad Dog” Mattis on April 12, during which a team from the Organisation for the Prohibition of Chemical Weapons (OPCW) was approaching Douma to conduct an on-site examination, there emerged a slim ray of hope that Trump would step back from acting on the transparently false provocation. (The slimness of any such hope was illustrated by the fact seemingly the most restrained of Trump’s advisers was somebody nicknamed “Mad Dog.”)
When on the evening of Friday the Thirteenth (Washington time) news came that the US had initiated military action, together with France and (the country Russia had accused of staging the Douma fraud) the United Kingdom, many feared the worst. The hasty timing was clearly aimed at preempting the arrival of the OPCW inspectors.
Of greater concern was the extent of the assault? If Russians were killed, Gerasimov was serious.
As it turned out, the worst didn’t come. World War III didn’t happen. Or hasn’t – yet.
In fact nothing much happened at all. According to the official US reports, something over a hundred missiles were launched at three targets. All missiles reached their targets – “Mission Accomplished!” The other side, however, claimed to have shot down roughly 75 percent of the incoming Tomahawks.
In the end, the damage was even less than from the follow-up to Idlib last year. No one was reported killed, neither Syrian nor Russian nor Iranian. Western governments claimed to have struck a serious blow at Syria’s chemical weapons capability. Syrians and Russians scoffed that the missiles had hit empty buildings and that Syria had no CW to hit since 2014, as certified by the OPCW.
In the aftermath of the missile show, media carried unverified reports that Trump had wanted a stronger campaign but deferred to Mattis’s caution, no doubt reflecting the views of professional military men who didn’t want to find out whether Gerasimov was bluffing. Mattis also reportedly wanted Congress to vote on any action before it was taken but was overruled by Trump.
There was even some speculation that the whole thing was a charade worked out in cooperation with the Russians. Even if true (and it’s unlikely) the mere fact that Trump would have to engage in such a ruse speaks volumes about the weakness of his position. “Whatever Trump says, America is not coming out of Syria,” writes Patrick Buchanan. “We are going deeper in. Trump’s commitment to extricate us from these bankrupting and blood-soaked Middle East wars and to seek a new rapprochement with Russia is ‘inoperative’.”
That’s clear from the comments of US Ambassador to the United Nations Nikki Haley.
She states that America won’t disengage until three objectives have been met: that ISIS has been defeated (a pretext, since ISIS is on the ropes and remains alive only because of hostile actions taken by the US and others against Syria); Damascus is finally deterred from using chemical weapons (a falsehood, since they don’t have any); and Iran’s regional influence is blocked (which means we’re staying in effect permanently in preparation for a larger war against Iran and perhaps eventually Russia).
The last point is unfortunately true, as plans are underway to beef up a Sunni anti-Iran bulwark in eastern Syria to cut off Tehran’s so-called “land bridge” the Mediterranean. Most Americans in Syria are to be replaced with a so-called Arab force – the “Arab NATO” touted last year in connection with Trump’s maiden foreign trip as president. (As though the one NATO we already have weren’t bad enough!)
Saudi Foreign Minister Adel al-Jubeir has suggested troops from his country would participate. Aside from whether Riyadh can spare them from their ongoing task of wrecking Yemen, Saudi personnel are likely to become a prime target for Syrians itching to get a crack at their chief tormenters over the past seven years.
So was anything really settled on April 13? On this occasion the West chose not to “cross the river,” much as Khan Ahmed’s force declined to do in 1480. For their part, the Russians in Syria, like their ancestors on the Ugra, were on defense and had no need to risk offensive action.
Unfortunately, unlike the “the great standing on the Ugra River,” which resolved the question of Russian independence and sovereignty in that era, nothing has been resolved now. The question remains: will the US peacefully relinquish its position as the sole arbiter of authority, legality, and morality in a unipolar world in favor of a multipolar order where Russia’s and China’s legitimate interests and spheres of influence are respected? Or will we continue to risk plunging mankind into a global conflict?
Syria remains a key arena where one path or the other will be taken to finally wrap up what US Army Major Danny Sjursen calls “Operation Flailing Empire.” The irony is that peacefully “losing” our pointless and dangerous attempt to rule the world would only be to Americans’ benefit. That’s what Trump promised in 2016. He hasn’t delivered and it’s increasingly doubtful he can.
In the end, the threat of World War III hasn’t vanished. It has just been postponed.
Jermall Charlo destroyed Hugo Centeno to become Gennady Golovkin’s WBC mandatory challenger.
Philippines’ President Rodrigo Duterte ordered the half-year closure of Boracay to tourists starting April 26 to clean up the waste in the sea. What can be done on a personal level to combat this waste apocalypse facing global waters?
At 11:15 on a Monday morning in May, an ordinary looking delivery van rolls into the intersection of 16th and K streets NW in downtown Washington, D.C., just a few blocks north of the White House. Inside, suicide bombers trip a switch.
Instantly, most of a city block vanishes in a nuclear fireball two-thirds the size of the one that engulfed Hiroshima, Japan. Powered by 5 kilograms of highly enriched uranium that terrorists had hijacked weeks earlier, the blast smashes buildings for at least a kilometer in every direction and leaves hundreds of thousands of people dead or dying in the ruins.
An electromagnetic pulse fries cellphones within 5 kilometers, and the power grid across much of the city goes dark. Winds shear the bomb’s mushroom cloud into a plume of radioactive fallout that drifts eastward into the Maryland suburbs.
Roads quickly become jammed with people on the move – some trying to flee the area, but many more looking for missing family members or seeking medical help.
It’s all make-believe, of course – but with deadly serious purpose.
Known as National Planning Scenario 1 (NPS1), that nuclear attack story line originated in the 1950s as a kind of war game, a safe way for national security officials and emergency managers to test their response plans before having to face the real thing.
Sixty years later, officials are still reckoning with the consequences of a nuclear catastrophe in regular NPS1 exercises. Only now, instead of following fixed story lines and predictions assembled ahead of time, they are using computers to play what-if with an entire artificial society: an advanced type of computer simulation called an agent-based model.
Today’s version of the NPS1 model includes a digital simulation of every building in the area affected by the bomb, as well as every road, power line, hospital, and even cell tower. The model includes weather data to simulate the fallout plume. And the scenario is peopled with some 730,000 agents—a synthetic population statistically identical to the real population of the affected area in factors such as age, sex, and occupation. Each agent is an autonomous subroutine that responds in reasonably human ways to other agents and the evolving disaster by switching among multiple modes of behavior—for example, panic, flight, and efforts to find family members.
The point of such models is to avoid describing human affairs from the top down with fixed equations, as is traditionally done in such fields as economics and epidemiology. Instead, outcomes such as a financial crash or the spread of a disease emerge from the bottom up, through the interactions of many individuals, leading to a real-world richness and spontaneity that is otherwise hard to simulate.
That kind of detail is exactly what emergency managers need, says Christopher Barrett, a computer scientist who directs the Biocomplexity Institute at Virginia Polytechnic Institute and State University (Virginia Tech) in Blacksburg, which developed the NPS1 model for the government. The NPS1 model can warn managers, for example, that a power failure at point X might well lead to a surprise traffic jam at point Y. If they decide to deploy mobile cell towers in the early hours of the crisis to restore communications, NPS1 can tell them whether more civilians will take to the roads, or fewer. “Agent-based models are how you get all these pieces sorted out and look at the interactions,” Barrett says.
The downside is that models like NPS1 tend to be big—each of the model’s initial runs kept a 500-microprocessor computing cluster busy for a day and a half—forcing the agents to be relatively simple-minded. “There’s a fundamental trade-off between the complexity of individual agents and the size of the simulation,” says Jonathan Pfautz, who funds agent-based modeling of social behavior as a program manager at the Defense Advanced Research Projects Agency in Arlington, Virginia.
But computers keep getting bigger and more powerful, as do the data sets used to populate and calibrate the models. In fields as diverse as economics, transportation, public health, and urban planning, more and more decision-makers are taking agent-based models seriously. “They’re the most flexible and detailed models out there,” says Ira Longini, who models epidemics at the University of Florida in Gainesville, “which makes them by far the most effective in understanding and directing policy.”
A plume of radioactive fallout (yellow) stretches east across Washington, D.C., a few hours after a nuclear bomb goes off near the White House in this snapshot of an agent-based model. Bar heights show the number of people at a location, while color indicates their health. Red represents sickness or death.
The roots of agent-based modeling go back at least to the 1940s, when computer pioneers such as Alan Turing experimented with locally interacting bits of software to model complex behavior in physics and biology. But the current wave of development didn’t get underway until the mid-1990s.
One early success was Sugarscape, developed by economists Robert Axtell of George Mason University in Fairfax, Virginia, and Joshua Epstein of New York University (NYU) in New York City. Because their goal was to simulate social phenomena on ordinary desktop computers, they pared agent-based modeling down to its essence: a set of simple agents that moved around a grid in search of “sugar”—a foodlike resource that was abundant in some places and scarce in others. Though simple, the model gave rise to surprisingly complex group behaviors such as migration, combat, and neighborhood segregation.
Another milestone of the 1990s was the Transportation Analysis and Simulation System (Transims), an agent-based traffic model developed by Barrett and others at the Los Alamos National Laboratory in New Mexico. Unlike traditional traffic models, which used equations to describe moving vehicles en masse as a kind of fluid, Transims modeled each vehicle and driver as an agent moving through a city’s road network. The simulation included a realistic mix of cars, trucks, and buses, driven by people with a realistic mix of ages, abilities, and destinations. When applied to the road networks in actual cities, Transims did better than traditional models at predicting traffic jams and local pollution levels—one reason why Transims-inspired agent-based models are now a standard tool in transportation planning.
A similar shift was playing out for epidemiologists. For much of the past century, they have evaluated disease outbreaks with a comparatively simple set of equations that divide people into a few categories—such as susceptible, contagious, and immune—and that assume perfect mixing, meaning that everybody in the affected region is in contact with everyone else. Those equation-based models were run first on paper and then on computers, and they are still used widely. But epidemiologists are increasingly turning to agent-based models to include factors that the equations ignore, such as geography, transportation networks, family structure, and behavior change—all of which can strongly affect how disease spreads. During the 2014 Ebola outbreak in West Africa, for example, the Virginia Tech group used an agent-based model to help the U.S. military identify sites for field hospitals. Planners needed to know where the highest infection rates would be when the mobile units finally arrived, how far and how fast patients could travel over the region’s notoriously bad roads, and a host of other issues not captured in the equations of traditional models.
In another example, Epstein’s laboratory at NYU is working with the city’s public health department to model potential outbreaks of Zika, a mosquito-borne virus that can lead to catastrophic birth defects. The group has devised a model that includes agents representing all 8.5 million New Yorkers, plus a smaller set of agents representing the entire population of individual mosquitoes, as estimated from traps. The model also incorporates data on how people typically move between home, work, school, and shopping; on sexual behavior (Zika can be spread through unprotected sex); and on factors that affect mosquito populations, such as seasonal temperature swings, rainfall, and breeding sites such as caches of old tires. The result is a model that not only predicts how bad such an outbreak could get—something epidemiologists could determine from equations—but also suggests where the worst hot spots might be.
In economics, agent-based models can be a powerful tool for understanding global poverty, says Stéphane Hallegatte, an economist at the World Bank in Washington, D.C. If all you look at are standard metrics such as gross domestic product (GDP) and total income, he says, then in most countries you’re seeing only rich people: The poor have so little money that they barely register.
To do better, Hallegatte and his colleagues are looking at individual families. His team built a model with agents representing 1.4 million households around the globe—roughly 10,000 per country—and looked at how climate change and disasters might affect health, food security, and labor productivity. The model estimates how storms or drought might affect farmers’ crop yields and market prices, or how an earthquake might cripple factory workers’ incomes by destroying their cars, the roads, or even the factories.
The model suggests something obvious: Poor people are considerably more vulnerable to disaster and climate change than rich people. But Hallegatte’s team saw a remarkable amount of variation. If the poor people in a particular country are mostly farmers, for example, they might actually benefit from climate change when global food prices rise. But if the country’s poor people are mostly packed into cities, that price rise could hurt badly.
That kind of granularity has made it easier for the World Bank to tailor its recommendations to each country’s needs, Hallegatte says—and much easier to explain the model’s results in human terms rather than economic jargon. “Instead of telling a country that climate change will decrease their GDP by X%,” he says, “you can say that 10 million people will fall into poverty. That’s a number that’s much easier to understand.”
Given how much is at stake in those simulations, Barrett says, users always want to know why they should trust the results. How can they be sure that the model’s output has anything to do with the real world—especially in cases such as nuclear disasters, which have no empirical data to go on?
Barrett says that question has several answers.
First, users shouldn’t expect the models to make specific predictions about, say, a stock market crash next Tuesday. Instead, most modelers accommodate the inevitable uncertainties by averaging over many runs of each scenario and displaying a likely range of outcomes, much like landfall forecasts for hurricanes. That still allows planners to use the model as a test bed to game out the consequences of taking action A, B, or C.
Second, Barrett says, the modelers should not just slap the model together and see whether the final results make sense. Instead, they should validate the model as they build it, looking at each piece as they slot it in—how people get to and from work, for example—and matching it to real-world data from transit agencies, the census, and other sources. “At every step, there is data that you’re calibrating to,” he says.
Modelers should also try to calibrate agents’ behaviors by using studies of human psychology. Doing so can be tricky—humans are complicated—but in crisis situations, modeling behavior becomes easier because it tends to be primal. The NPS1 model, for example, gets by with built-in rules that cause the agents to shift back and forth among just a few behaviors, such as “health care–seeking,” “shelter-seeking,” and “evacuating.”
Even so, field studies point to crucial nuances, says Julie Dugdale, an artificial intelligence researcher at the University of Grenoble in France who studies human behavior under stress. “In earthquakes,” she says, “we find that people will be more afraid of being without family or friends than of the crisis itself.” People will go looking for their loved ones first thing and willingly put themselves in danger in the process. Likewise in fires, Dugdale says. Engineers tend to assume that when the alarm sounds, people will immediately file toward the exits in an orderly way. But just watch the next time your building has a fire drill, she says: “People don’t evacuate without first talking to others”—and if need be, collecting friends and family.
The evidence also suggests that blind, unthinking panic is rare. In an agent-based model published in 2011, sociologist Ben Aguirre and his colleagues at the University of Delaware in Newark tried to reproduce what happened in a 2003 Rhode Island nightclub fire. The crowds jammed together so tightly that no one could move, and 100 people died. Between the police, the local paper, and survivors’ accounts, Aguirre’s team had good data on the victims, their behavior, and their relationships to others. And when the researchers incorporated those relationships into the model, he says, the runs most consistent with the actual fire involved almost no panic at all. “We found that people were trying to get out with friends, co-workers, and loved ones,” Aguirre says. “They were not trying to hurt each other. That was a happenstance.”
The NPS1 model tries to incorporate such insights, sending its agents into “household reconstitution” mode (searching for friends and family) much more often than “panic” mode (running around with no coherent goal). And the results can sometimes be counterintuitive. For example, the model suggests that right after the strike, emergency managers should expect to see some people rushing toward ground zero, jamming the roads in a frantic effort to pick up children from school or find missing spouses. The model also points to a good way to reduce chaos: to quickly restore partial cell service, so that people can verify that their loved ones are safe.
If agent-based modelers have a top priority, it’s to make the simulations easier to build, run, and use—not least because that would make them more accessible to real-world decision-makers.
Epstein, for example, envisions national centers where decision-makers could access what he calls a petabyte playbook: a library containing digital versions of every large city, with precomputed models of just about every potential hazard. “Then, if something actually happens, like a toxic plume,” he says, “we could pick out the model that’s the closest match and do near–real-time calculation for things like the optimal mix of shelter-in-place and evacuation.”
At Virginia Tech, computer scientist Madhav Marathe is thinking along the same lines. When a Category-5 hurricane is bearing down, he says, someone like the mayor of San Juan can’t be waiting around for a weeklong analysis of the storm’s possible impact on Puerto Rico’s power grid. She needs information that’s actionable, he says—”and that means models with a simple interface, running in the cloud, delivering very sophisticated analytics in a very short period of time.”
Marathe calls it “agent-based modeling as a service.” His lab has already spent the past 4 years developing and testing a web-based tool that lets public health officials build pandemic simulations and do what-if analyses on their own, without having to hire programmers. With just a few clicks, users can specify key variables such as the region of interest, from as small as a single city to the entire United States, and the type of disease, such as influenza, measles, Ebola, or something new. Then, using the tool’s built-in maps and graphs, users can watch the simulation unfold and see the effect of their proposed treatment protocols.
Despite being specialized for epidemics, Marathe says, the tool’s underlying geographic models and synthetic populations are general, and they can be applied to other kinds of disasters, such as chemical spills, hurricanes, and cascading failures in power networks. Ultimately, he says, “the hope is to build such models into services that are individualized—for you, your family, or your city.” Or, as Barrett puts it, “If I send Jimmy to school today, what’s the probability of him getting Zika?”
So it won’t just be bureaucrats using those systems, Barrett adds. It will be you. “It will be as routine as Google Maps.”
Gervonta Davis displays elite talent and exemplary professionalism in dismantling Jesus Cuellar and capturing his second world title.
Josiah is wondering whether he can ever get paid fairly by staying at the same job for years — or whether he must keep changing jobs often to get paid what he’s worth. What do you think?
Are governments making promises about pensions that they might not be able to keep?
According to an analysis by the World Economic Forum (WEF), there was a combined retirement savings gap in excess of $70 trillion in 2015, spread between eight major economies..
As Visual Capitalist’s Jeff Desjardins notes, The WEF says the deficit is growing by $28 billion every 24 hours – and if nothing is done to slow the growth rate, the deficit will reach $400 trillion by 2050, or about five times the size of the global economy today.
The group of economies studied: Canada, Australia, Netherlands, Japan, India, China, the United Kingdom, and the United States.
MIND THE GAP
Today’s infographic comes to us from Raconteur, and it illuminates a growing problem attached to an aging population (and those that will be supporting it).
Since social security programs were initially developed, the circumstances around work and retirement have shifted considerably. Life expectancy has risen by three years per decade since the 1940s, and older people are having increasingly long life spans. With the retirement age hardly changing in most economies, this longevity means that people are spending longer not working without the savings to justify it.
This problem is amplified by the size of generations and fertility rates. The population of retirees globally is expected to grow from 1.5 billion to 2.1 billion between 2017-2050, while the number of workers for each retiree is expected to halve from eight to four over the same timeframe.
The WEF has made clear that the situation is not trivial, likening the scenario to “financial climate change”:
The anticipated increase in longevity and resulting ageing populations is the financial equivalent of climate change
-Michael Drexler, Head of Financial and Infrastructure Systems, WEF
Like climate change, some of the early signs of this retirement savings gap can be “sandbagged” for the time being – but if not handled properly in the medium and long term, the adverse effects could be overwhelming.
While implementing various system reforms like raising the retirement age will help, ultimately the money in the system has to come from somewhere. Social security programs will need to cut benefits, increase taxes, or borrow from somewhere else in the government’s budget to make up for the coming shortfalls.
In the United States specifically, it is expected that the Social Security trust fund will run out by 2034. At that point, there will only be enough revenue coming in to pay out approximately 77% of benefits.
A senior correspondent for German state media broadcast ZDF heute stunned his European audience during a report from on the ground in Syria when he gave a straightforward and honest account of his findings while investigating what happened in Douma. The veteran reporter, Uli Gack, interviewed multiple eyewitnesses of the April 7 alleged chemical attack and concluded of the testimonials, “the Douma chemical attack is most likely staged, a great many people here seem very convinced.”
It appears that all local Syrians encountered by the German public broadcast reporter were immediately dismissive of the widespread allegation that the Syrian government gassed civilians, which the US, UK, France, and Israel used a pretext for launching missile strikes on Damascus.
ZDF heute: The world continues to puzzle over whether the banned chemical weapons were used in Douma. ZDF correspondent Uli Gack is in Syria for us: “you were in a large refugee camp today and talked to a lot of people – what did you hear about the attack there?” Gack responded, “the Douma chemical attack is most likely staged, a great many people here seem very convinced.”
The German ZDF report is consistent with veteran British journalist Robert Fisk’s investigation upon being the first Western journalist to gain access to the site in Douma. Fisk reported early this week, “There are the many people I talked to amid the ruins of the town who said they had ‘never believed in’ gas stories–which were usually put about, they claimed, by the armed Islamist groups.”
Die Welt rätselt weiter, ob die geächteten #Chemiewaffen in #Duma eingesetzt wurden. ZDF-Korrespondent Uli Gack ist für uns in #Syrien – Sie waren heute in einem großen Flüchtlingscamp und haben mit vielen Leuten gesprochen – was haben Sie denn dort über den Angriff gehört? pic.twitter.com/euubha1a2U
— ZDF heute (@ZDFheute) April 20, 2018
ZDF (Zweites Deutsches Fernsehen) is one of Germany’s largest and oldest state-owned channels, which is funded in part through citizens paying a household licensing fee, and Heute is perhaps the most visible public news program in all of Germany.
According to the live report, some witnesses told ZDF that Islamist rebels killed victims with chlorine, filmed the scenes, then claimed an ‘Assad chemical attack’. Though interviewing “a great many people [who] seem convinced” that a chemical attack did not actually happen, the reporter did not attempt to censor what he consistently heard from locals who were said to be in the area when the events occurred.
Increasingly, it appears that mainstream media gate-keepers are losing it over the fact that so many highly visible and respected reporters and broadcasters have featured reports this week which publicly question the Syrian chemical attack narrative and US coalition missile strikes that followed.
One writer for the Guardian and Daily Beast who is a well-known pro regime-change advocate, for example, laments that “disinformation has taken hold” on Syria. In a series of tweets following Robert Fisk’s bombshell report for the Independent, Emma Beals, who often simply parrots whatever her ‘rebel’ sources tell her reacted to the recent profusion of high profile pundits questioning the established narrative on major British platforms.
My educated and informed friends are emailing me en masse asking me about what’s going on in Syria because “it’s so hard to work out the truth.” Having spent years busting a gut to dig it out, it’s heartbreaking to see the extent to which disinformation has taken hold.
My educated and informed friends are emailing me en masse asking me about what’s going on in Syria because “it’s so hard to work out the truth.” Having spent years busting a gut to dig it out, it’s heartbreaking to see the extent to which disinformation has taken hold.
— Emma Beals (@ejbeals) April 17, 2018
Beals advocates what we recently described as the highly simplistic “Disney version” of events in Syria:
Syrian War for Dummies: Disney Version–
Once upon a time, a country called Syria was ruled by a ruthless dictator named Bashar Al-Assad. He was a cruel man who gassed his own people. His actions caused a civil war in Syria. America and Europe tried their best to stop the devastating civil war, and even generously accepted many Syrian refugees. Eventually America went to Syria, defeated ISIS, and is now trying to restore stability.
This above version is quite popular among many Americans and Europeans and the Western mainstream media.
Meanwhile, almost simultaneous to German TV’s ZDF heute report from Syria, lawyers of the Bundestag (German federal parliament) issued a legal brief on the US-led strike on Syria. The government lawyers’ report was requested by the left-wing party Die Linke in response to the US coalition missile strikes which primarily targeted facilities in and around Damascus.
“The deployment of military force against a state to punish it for breaking an international convention is a violation of the international law prohibiting violence,” reads the report, as cited by the German Press agency DPA and translated by Sputnik.
The legal report further concluded that the US-led attack on Syria – which skirted the UN – but which was supported by the German government (though without German military participation) was based in chemical attack claims that the legal team deemed “not convincing”.