Friday, November 16, 2018

Ground Up Rethink of Disaster Relief

Disaster relief has not kept up with the volume of mega natural calamities, a four-fold increase since 1970. We can do the Trump blame game and deny or diminish relief – coincidentally worse in blue states and territories than red – we can let people waddle in rebuilding hell, often exacerbating economic realities and national policies or keep pouring billions and billions of dollars in reactive relief. You’d think we could plan against such horribles better, but kicking the can down the road is a new American tradition. That we seem always to choose the most expensive alternatives, that fixing or responding without a precise plan is always more expensive than preventing or being truly ready for what is inevitable.
Look at Japan this past year, particularly devastated by natural disasters. “The economy Japan's economy shrank in the third quarter as natural disasters hit spending and disrupted exports… contracted by an annualised 1.2% between July and September, preliminary figures showed… A devastating earthquake and typhoon were among the disasters to hit Japan this year, and prompted the bigger than expected [GDP] contraction.”, November 14th.
According to its corporate website, One Concern (a new company applying artificial intelligence analytics to help government disaster relief efforts), over the last 40 years, 3.3 million people have died in natural disasters, which have inflicted $2.3 trillion in economic damage. As populations increasing will live in cities – One Concern estimates that 60% of the earth’s population will live in cities by 2030 – a natural disaster will have a much greater potential for death and damage with such a concentration of people.
The United States continues to lumber under the vestiges of red state climate change denial. We see a reluctance from developing nations to embrace severe limitations on greenhouse gas emissions and countries like ours that place business earnings over human safety concerns. But we are spending and will spend so much more than any aggregation of business advantages by not grappling with climate change variables and not redesigning our disaster relief efforts to reflect modern capacities and realities.
As the United States deals with retrofitting its energy generation and consumption policies, which it will sooner or later, it also needs to revisit how governments analyze and deploy once a natural disaster hits. That’s where artificial intelligence can benefit us all, increasing effectiveness and implementing cost efficiencies.
One Concern is launching a machine learning platform that provides cities with specialized maps to help emergency crews decide where to focus their efforts in a flood. The maps update in real-time based on data about where water is flowing to estimate where people need help the most. It’s the latest in a wave of AI-powered tools aimed at helping cities prepare for an era of severe, and increasingly frequent, disasters.
“Since 1980, the U.S. has suffered from 219 climate disasters that cost over $1 billion, with the total cost exceeding $1.5 trillion. In 2017 alone, these disasters cost the country $306 billion. Since 2000, more than 1 million people have perished from these extreme weather events. As climate change heralds more devastating natural disasters, cities will need to rethink how they plan for and respond to disasters. Artificial intelligence, such as the platform One Concern has developed, offers a tantalizing solution. But it’s new and largely untested. And the urgency is growing by the day.”, November 15th.
One Concern is the brainchild of Stanford University student Ahmad Wani, a native Kashmiri who watched helplessly in his native country as floodwaters decimated everything around him. He vowed to address the misfeasance in disaster relief. Even in modern countries, disaster relief agencies deal with static maps and old-world analytical tools that cannot process the complexity of vulnerabilities, temperatures, weather patterns, population concentrations, wind history, likely structural failures, access risks and availabilities, water availability and the inevitable changes during a disaster that simple were not anticipated. Simply, we are living in relatively unsophisticated dark ages when it comes to real time disaster analytics.
“After surviving the devastating flood in Kashmir, Wani returned to Stanford, where he was studying structural engineering. He began contemplating how to predict a disaster’s damage. The idea was that if city officials could anticipate which areas would be most harmed, they would be able to deploy resources faster and more efficiently throughout the disaster zone. But he had a problem: analyzing a single building using traditional structural engineering software took seven days on Stanford’s supercomputer. ‘We had to recreate that for the entire city’ for the idea to work, Wani says. ‘We didn’t have seven days or seven years. We wanted to do it in three to five minutes.’
“He decided to focus first on earthquakes, which are more of a threat than floods in California. Wani teamed up with fellow Stanford students Nicole Hu, a computer scientist who focuses on machine learning, and Tim Frank, an earthquake engineer, to build an algorithm that can digest data about how a building was built and how it’s been retrofitted over time. This data is combined with information on the building’s materials and surrounding soil properties to extrapolate what happens to this system when shaking occurs. Then, when a quake hits, the model absorbs new information coming from on-the-ground emergency responders, 911 calls, or even Twitter to make its predictions of the damage more accurate.
“Because the model identifies patterns by looking through large amounts of data, it needs less computing power than the previous method of asking a computer to perform complex physics equations to understand how shaking will impact a structure. The trade-off is accuracy: Hu estimates that the algorithm is only about 85% accurate. With more data over time, that number will improve, but the team believes that it’s good enough to paint a broad picture of damage immediately after a quake. (Of course, they won’t know for sure until a major earthquake hits.)
“Wani, Hu, and Frank started One Concern in 2015 and then released its earthquake platform, called Seismic Concern, in 2016. Seismic Concern predicts the damage caused by earthquakes on a block-by-block level and is now used by eight different municipalities, including the cities of San Francisco, Los Angeles, and Cupertino.
“Now, the company is launching Flood Concern, a constantly evolving risk map that crunches huge amounts of data based on the physics of how water flows, information about previous floods, and even satellite imagery to approximate the depth, direction, and speed of the water–and determine which areas of a city are most at risk. Layered on top of the damage prediction is demographic data, so that emergency planners can see what areas of a city might have particularly vulnerable populations, like a significant percentage of seniors or disabled citizens. With that kind of information, planners can figure out which areas should be evacuated, where to put shelters, and what critical infrastructure–like schools or hospitals–needs the most help when flooding begins.”
There is a double message in this story. One deals with the use of modern tools to address the rise in natural disasters. The other deals with an immigrant, one of the best and the brightest, that Donald Trump wants to prevent from entering the United States, a person of color with a different religious background who is a “them” and not “us.” You tell me who the biggest loser is under than less-than-subtle Trump nationalist policy.
I’m Peter Dekom, and never before has the world required more international cooperation, more group think, to solve complex problems… and the United States needs to remember that it became great because it was a nation of immigrants!

Thursday, November 15, 2018

Schizotypy – The Rise of Conspiracy Theory

The mid-terms are over. The Senate, almost never in doubt given the vast array of Democratic seats at risk and the very few GOP slots not firmly within Republican control, belongs to Donald Trump. Anything from treaties to judicial appointments that requires only Senate approval are his. The House, regardless of voter restrictions and gerrymandering in red states, belongs to the Democrats. Expect House committees to launch multiple investigations previously stifled by Trump’s GOP colleagues, noting also that all appropriations bills must originate in the House. The country seems poised for increasing gridlock, polarization on steroids. War is not pretty. The 2020 presidential election is not that far off, and as Donald Trump rolling rallies prove, the President never stopped running for office.
But as this election has illustrated, smaller and smaller margins are determining the victor in all but the most red/blue committed regions. Segments of voters who never mattered before, either because they didn’t vote or because their numbers are so few, clearly made a difference. Social media has empowered tiny pockets of strange believers to aggregate online to create a hitherto otherwise small force of voters who have finally made a difference. Donald Trump’s base, a mixture of angry and displaced blue-collar workers, evangelicals, ultra-right-wing social conservatives and conspiracy theorists, has proven to be a solid and almost immutable bloc of his most committed supporters.
Conspiracy theorists are perhaps the most interesting subset of the Trump base, a breed apart. They see secret cabals in government (the Q-Anon vs. deep state) and espouse strange and scientifically unsustainable beliefs (like the flat-earthers), often believe that MSM (mainstream media) is left wing collective aimed at toppling Donald Trump and traditional white Christian American values or that Democrats are recruiting undocumented aliens from overseas to increase the number of non-white voters favoring liberal diversity polices.
Back on August 19th in my Conspiracy Theories, Right on Q blog, I noted: Until recently, political conspiracy theorists have mostly arisen from that segment of any population that is out of political power, attacking the incumbents with dark secrets about how the winners stole that power from “the people.” Mostly, these people sit on the periphery of activism and usually do not vote in what they think is a system that will ignore their choice no matter the election process. But a strange phenomenon has occurred: today’s dominant conspiracy theorists come from the political party that controls both houses of Congress, the presidency, and most state legislatures and governorships. Huh?
It started with an unlikely, highly illogical, candidate whose political positions shifted like desert sands. To knock off a substantial flock of Republican presidential wannabes, Donald Trump instinctively understood that he could not use convention tactics. He needed a destabilizing political campaign laced with allegations against opponents that were devastating but unproveable. Like accusing Ted Cruz’ father of having participated in the assassination of John F Kennedy. Conspiracy theorists perked up their ears.
They also joined in a chorus of accusers that fabricated a litany of truly absurd claims against Hillary Clinton… and Donald Trump knew he had found a force that no presidential candidate had ever used before. “Lock her up!” they cried. Trump quoted statements from popular conspiracy theorists, people who had been making a living as “shock jocks” in the radio world where there were enough listeners to create a viable ad-supported model, as if they were true. The Internet was born to support conspiracy theories, and particularly older users – used to believing what they read in public media without question – were particularly vulnerable.
Beleaguered Trump used conspiracy theories to dislodge his opponents, one-by-one, with catchy epithets (Lyin’ Ted Cruz, Crooked Hillary) and totally fabricated statistics that resonated with this “whacky” conspiracy theory body of people… who actually became voters again. Trump was the underdog, and the old-line GOP and their “liberal” Democrats were the enemy, up to dirty tricks and rigging elections. Conspiracy theorists love underdogs.
But who are these individuals with such passionate beliefs in the absurd? How do they wind up so committed to outlandish and easily refutable beliefs, unwilling to entertain any logical explanation to the contrary? In Volume 10, Number 6, November 2015, the Journal of the Society for Judgment and Decision-Making reproduced excepts from an article by   Gordon Pennycook, James Allan Cheyne, Nathaniel Barr, Derek J. Koehler and Jonathan A. Fugelsang  On the reception and detection of pseudo-profound bullshit: “The Oxford English Dictionary defines bullshit as, simply, ‘rubbish’ and ‘nonsense,’ which unfortunately does not get to the core of bullshit…
“What might cause someone to erroneously rate pseudo-profound bullshit as profound? In our view, there are two candidate mechanisms that might explain a general ‘receptivity’ to bullshit. The first mechanism relates to the possibility that some people may have a stronger bias toward accepting things as true or meaningful from the outset…
“The second mechanism relates to a potential inability to detect bullshit, which may cause one to confuse vagueness for profundity.”
In an article entitled “See the Threads of Conspiracy Thinking” (reproduced in the November 14th citing the above work, Dr. Ken Broda-Bahm, a psychologist who helps trial lawyers delve into the psyches of potential jurors, recently focused on those who held firm conspiracy theory beliefs, tracking four variables that push (pull?) individuals into such strange theories:
Distrust and Eccentricity
The strongest psychological factor that accompanies conspiracy thinking is something called “Schizotypy.” It means interpersonal suspiciousness, social anxiety, and isolation. It characterizes those who distrust what they’re told, believe that events are controlled by others, feel ill at ease with the world, and see themselves as “having special insight into the machinations of these malevolent actors.”…
Belief in a Dangerous World
A second psychological factor characterizing conspiracy thinking is a belief in a lack of safety. Conspiracy thinkers are committed to the view that “the world is a dangerous place full of bad people.” As the authors point out, that is a relatively strange worldview to adopt because, typically, people will chose a worldview that makes them feel more comfortable (e.g., a “belief in a just world” would be at the other end of the spectrum). Belief in a dangerous world, however, might also add comfort in providing a ready explanation for everything…
Gullibility… and the Best-Named Psychological Scale Ever
The scale is called “Receptivity to Bullshit” (Pennycook et al., 2015 [see above quotes]). Yes, that is the actual, official, academically acknowledged and used name for this psychological scale, And it is exactly what it sounds like: A measure of how readily a person will accept something that is meaningless or unsupported. It is, the authors write, a “tendency to perceive profundity in nonsensical but superficially meaningful ideas,” or an “eagerness to seek or find meaning or patterns in ambiguous or random information.” It is measured by getting reactions to statements that seem to have come from a random-quote generator: “Wholeness quiets infinite phenomena,” or “Imagination is inside exponential space time events.”…
Belief in Agency
The final factor, belief in agency, was found to lack a strong independent connection to conspiracy thinking, with most of the associated variation being explained by “Receptivity to Bullshit.” … A belief in agency refers to the psychological tendency to see intention behind actions and events. In other words, instead of saying “shit happens,” they are more likely to see purpose, reason, and motives even when, potentially, there aren’t any.
Fake news is the currency of conspiracy theorists. They manufacture that disinformation in droves. They reinforce each other with fabricated-“facts”-cited-as-reality, often vindicated by a quote or a tweet from the President himself based on such self-reinforcing “fabricated facts” to prove the premise. Circular reasoning that starts with a manufactured statement that virally achieves “legitimacy” the more often it is repeated. The sheer volume of viral messages, some repeaters adding their names to the false statement as if they vetted their “facts,” continues to validate the false underlying premise. The Russians have mastered the use of political disinformation, particularly preying on the gullibility of conspiracy theorists, to destabilize our electoral process, and they are hardly alone.
Without fake news, conspiracy theory taken as fact, there is no Donald Trump. But in the tug of war between the free speech provisions of the First Amendment and the ability to spread falsehood as fact though social media and ultimately into traditional media, is American democracy itself threatened when so many voters no longer vote based on what is real? How do we balance the dictates of free speech, essential to a modern democracy, with speech that threatens to disable democracy at its core?
I’m Peter Dekom, and unless we can begin to solve this conundrum in the immediate future, democracy may in fact fade into the oblivion of history books forcing all of us to live in a dark future of plutocracy or autocracy where the few control the rest… with an iron hand.