Category Archives: Life

“The Book of Why” – Judea Pearl

Source: Boston Review, Sep 2019

“We live in an era that presumes Big Data to be the solution to all our problems,” he says, “but I hope with this book to convince you that data are profoundly dumb.” Data may help us predict what will happen—so well, in fact, that computers can drive cars and beat humans at very sophisticated games of strategy, from chess and Go to Jeopardy!—but even today’s most sophisticated techniques of statistical machine learning can’t make the data tell us why.

For Pearl, the missing ingredient is a “model of reality,” which crucially depends on causes. Modern machines, he contends against a chorus of enthusiasts, are nothing like our minds.

Causation really cannot be reduced to correlation, even in large data sets, Pearl came to see. Throwing more computational resources at the problem, as Pearl did in his early work (on “Bayes nets,” which apply Thomas Bayes’s basic rule for updating probabilities in light of new evidence to large sets of interconnected data), will never yield a solution. In short, you will never get causal information out without beginning by putting causal hypotheses in.

he developed simple but powerful techniques using what he calls “causal graphs” to answer questions about causation, or to determine when such questions cannot be answered from the data at all.

the main innovation that Pearl is advertising—the use of causal hypotheses—gets couched not so much in algebra-laden statistics as in visually intuitive pictures: “directed graphs” that illustrate possible causal structures, with arrows pointing from postulated causes to effects. A good deal of the book’s argument can be grasped simply by attending only to these diagrams and the various paths through them.

Consider two basic building blocks of such graphs. If two arrows emerge from a single node, then we have a “common-causal fork,” which can produce statistical correlations between properties that are not, themselves, causally related (such as car color and accident rate on the reckless-drivers-tend-to-like-the-color-red hypothesis). In this scenario, A may cause both B and C, but B and C are not causally related.

On the other hand, if two different arrows go into the same node then we have a “collider,” and that raises an entirely different set of methodological issues. In this case, A and B may jointly cause C, but A and B are not causally related. The distinction between these two structures has important consequences for causal reasoning. While controlling for a common cause can eliminate misleading correlations, for example, controlling for a collider can create them. As Pearl shows, the general analytic approach, given a certain causal model, is to identify both “back door” (common cause) and “front door” (collider) paths that connect nodes and take appropriate cautions in each case.

The method of causal graphs allows us to test the hypotheses, both by themselves and against each other, by appeal to the data; it does not tell us which hypotheses to test.

(“We collect data only after we posit the causal model,” Pearl insists, “after we state the scientific query we wish to answer. . . . This contrasts with the traditional statistical approach . . . which does not even have a causal model.”)

Sometimes the data may refute a theory. Sometimes we find that none of the data we have at hand can decide between a pair of competing causal hypotheses, but new data we could acquire would allow us to do so. And sometimes we find that no data at all can serve to distinguish the hypotheses.

why care about causes? One reason is pure scientific curiosity: we want to understand the world, and part of that requires figuring out its hidden causal structure. But just as important, we are not mere passive observers of the world: we are also agents. We want to know how to effectively intervene in the world to prevent disaster and promote well-being. Good intentions alone are not enough.

We also need insight into how the springs and forces of nature are interconnected. So ultimately, the why of the world must be deciphered if we are to understand the how of successful action.

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Optimists Live Longer

Source:  The Star,  Sep 2019

New American research has found that people who are more optimistic are more likely to live longer, possibly even achieving “exceptional longevity”, which is living to age 85 or older.

The large-scale study by researchers from the Boston University School of Medicine, National Center for PTSD at VA Boston Healthcare System and Harvard T.H. Chan School of Public Health looked at 69,744 women and 1,429 men.

They were asked to complete surveys to assess their level of optimism, as well as their overall health and health habits such as diet, smoking status and alcohol use.

After following the women for ten years and the men for 30 years, the researchers found that the most optimistic men and women seemed to benefit from an 11% to 15% longer lifespan and had a 50% to 70% greater chance of living to the age of 85 and older, compared to the least optimistic participants.

he researchers explained that optimism is characterised as the general expectation that good things will happen or the belief that the future will be favourable because one can control important outcomes.

“While research has identified many risk factors for diseases and premature death, we know relatively less about positive psychosocial factors that can promote healthy ageing,” explained study author Dr Lewina Lee.

“This study has strong public health relevance because it suggests that optimism is one such psychosocial asset that has the potential to extend the human lifespan.

“Interestingly, optimism may be modifiable using relatively simple techniques or therapies.”

How optimism may help people live longer is still unclear, although senior author Dr Laura Kubzansky noted that, “Other research suggests that more optimistic people may be able to regulate emotions and behaviour, as well as bounce back from stressors and difficulties more effectively.”

LiveAid Didn’t Pan Out

Source: Spin, Jul 2015

The truth is shocking in its clarity. “People are dying because of their government,” says Jason Clay, an anthropologist studying famine in Ethiopia. “And what groups like Live Aid are doing is helping the government set up a system that is going to cause people to die for decades to come.”

“Western governments and humanitarian groups like Live Aid are fueling an operation that will be described with hindsight in a few years time as one of the greatest slaughters in the history of the twentieth century,” says Dr. Claude Malhuret, whose relief agency, Medicins sans Frontieres (Doctors Without Borders), has been kicked out of Ethiopia for speaking up against “the most massive violations of human rights we have seen in recent ?ears.”

HKG Protests: Creating New Chinese Characters

Source: LanguageLog/UPenn, Sep 2019

Among the new polysyllabic characters (called hétǐ zì 合體字 [“compound / synthesized characters”] in Chinese) created by the Hong Kong protesters is this one (see below in the “Readings” [especially the first item] for other examples).  It is preceded by this note: “Hongkongers will remember 721 & 831”, which are references to the extreme brutality wreaked on the people of Hong Kong by hired gangsters on July 21 and by “police” on August 31, for which see 721 Yuen Long Nightmare and #831terroristattack (also here).  This new polysyllabic character is widely circulating on the internet and has come to me from many sources (here’s one).

This composite character consists of elements of the following three Sinographs:

ging2 警 of ging2caat3 警察 (lit., “alert / vigilant observe / examine / inspect”, i.e., “police”)

hak1 黑 of hak1 se5wui6*2 黑社會 (lit., “black society”, i.e., “organized crime; the triads; gangsters”)

tit3 鐵 of tit3lou6 鐵路 (lit., “iron road”, i.e., “railway; railroad”)

It alludes to the collusion of police, hired gangsters, and railway authorities in the notorious beating of passengers described here:

The 2019 Yuen Long attack was a mob attack that occurred on 21 July 2019, in Yuen Long, Hong Kong. A mob of over 100 armed men dressed in white indiscriminately attacked civilians on the streets and passengers in the Yuen Long MTR station including the elderly, children, black-clad protesters, journalists and lawmakers. At least 45 people were injured in the incident, including a pregnant woman. The attack happened following an anti-extradition bill protest in Sheung Wan, Hong Kong and was an act threatening the pro-democracy protesters who were returning home to Yuen Long.

Despite thousands of reports made to the 999 emergency hotline, the police did not arrive for more than 30 minutes and finally arrived one minute after the mob had left the station. No arrests were made that night. Many accused the police of failing to protect citizens from being attacked, with some even alleging that the police colluded with the mobs.

One of the strongest weapons of the Hong Kong protesters against the armed might of the police, thugs, and increasingly military infiltrators from the north is language, both spoken and written, as described in this post and in the following earlier posts.

From the Comments:

SP said,

September 1, 2019 @ 3:16 pm

There’s yet another thing that’s hidden. In 黑, you can see the logo of the MTR, Hong Kong’s subway system. The attacks of 721 and 831 took place in the MTR

Seek First to Understand … (by listening)

Source: Medium, Aug 2019

If you are serious about knowing others, and making real connections, invest time in your relationships. Understanding someone on a deep level requires a completely different mindset — an open mind to listen instead of judgement.

In his book, Emotional Intelligence, Daniel Goleman listed ‘understanding others’ as the first element of empathy. He also suggested that understanding others is more than just sensing other people’s feelings and emotions. It also means taking a genuine interest in them and their concerns.

Listen past your blind spots

Listening is by far one of the best ways to understand others. You can easily be good at speaking and bad at listening because we naturally want to make our case in many situations. We seek first to be understood.

Scott Young, author of Ultralearning: Master Hard Skills, Outsmart the Competition, and Accelerate Your Career, argues, “My guess is that listening — meaning all the effort taken to correctly understand the social context and minds of other people — is probably more like 80% of social skills, with the last 20% being delivery of your message.”

Understand others’ is a kind of mindfulness practice. It’s about sustaining attention to someone else’s inner world. The most influential people strive for a genuine connection with others.

Show that you get “it” by keeping and maintain an open mind to “get” what they mean. Show that you understand the challenges your conversational counterpart is facing. To understand someone, we should not imagine their point of view but make the effort to “get” their perspective.

Listening is an active skill.

It requires you to clear your mind of personal biases. When you are fully present with people and listen instead of judge, it’ll likely blow your mind how much effort it really does require.

Understanding others does not mean that you have to necessarily agree with their point of view, or feelings. Instead, it means you recognise their point of view and accept that it is different from yours.

Remember what Dr Stephen Covey once said, “If I were to summarize in one sentence the single most important principle I have learned in the field of interpersonal relations, it would be this: Seek first to understand, then to be understood.”

Become a Billionaire in 5 Easy Steps (with OPM)

Source: ZeroHedge, Aug 2019

Step 1:

Find a product that people love… then make a slightly better version of it, and price it WAY BELOW your cost so that you lose money on every unit sold.

Step 2:

Create a ridiculous mission statement.

It doesn’t matter what you’re selling– your real mission is things like consciousness, happiness, and community. And use the word ‘technology’ a lot. No matter what you’re producing, always pretend that you’re a tech company.

Step 3:

Raise money from investors at an obscene valuation on the basis that you’re a visionary tech company.

Don’t bother forecasting profits and creating conservative pro-forma statements, from which investors can derive a sensible valuation of your business. Instead, let the investors imagine how profitable your company can eventually become.

Step 4: 

At a minimum, double your losses every year. And, as you continue to burn through investor capital, raise even more money at progressively higher valuations.

Step 5: 

At the peak of the stock market bubble, take your company public at twice your last valuation.  Reward these gullible investors with limited voting rights, and consolidate your power over the company as you steer it towards greater and greater losses while showering yourself with gigantic compensation packages.

What happens between Flights

Source: The Points Guy, Aug 2019
<see source for details>

 

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