The machine learning president

For the past four years, I have been unable to post with any regularity. I have dozens of unfinished posts sitting in my drafts folder. I would start with a thought but then get stuck, which had previously been somewhat unusual for me. Now on this first hopeful day I have had for the past four trying years, I am hoping I will be able to post more regularly again.

Prior to what I will now call the Dark Years, I viewed all of history through an economic lens. I bought into the standard twentieth century leftist academic notion that wars, conflicts, social movements, and cultural changes all have economic underpinnings. But I now realize that this is incorrect or at least incomplete. Economics surely plays a role in history but what really motivates people are stories and stories are what led us to the Dark Years and perhaps to get us out.

Trump became president because he had a story. The insurrectionists who stormed the Capitol had a story. It was a bat shit crazy lunatic story but it was still a story. However, the tragic thing about the Trump story (or rather my story of the Trump story) is that it is an unintentional algorithmically generated story. Trump is the first (and probably not last) purely machine learning president (although he may not consciously know that). Everything he did was based on the feedback he got from his Twitter Tweets and Fox News. His objective function was attention and he would do anything to get more attention. Of the many lessons we will take from the Dark Years, one should be how machine learning and artificial intelligence can go so very wrong. Trump’s candidacy and presidency was based on a simple stochastic greedy algorithm for attention. He would Tweet randomly and follow up on the Tweets that got the most attention. However, the problem with a greedy algorithm (and yes that is a technical term that just happens to coincidentally be apropos) is that once you follow a path it is hard to make a correction. I actually believe that if some of Trump’s earliest Tweets from say 2009-2014 had gone another way, he could have been a different president. Unfortunately, one of his early Tweet themes that garnered a lot of attention was on the Obama birther conspiracy. This lit up both racist Twitter and a counter reaction from liberal Twitter, which led him further to the right and ultimately to the presidency. His innate prejudices biased him towards a darker path and he did go completely unhinged after he lost the election but he is unprincipled and immature enough to change course if he had enough incentive to do so.

Unlike standard machine learning for categorizing images or translating languages, the Trump machine learning algorithm changes the data. Every Tweet alters the audience and the reinforcing feedback between Trump’s Tweets and its reaction can manufacture discontent out of nothing. A person could just happen to follow Trump because they like The Apprentice reality show Trump starred in and be having a bad day because they missed the bus or didn’t get a promotion. Then they see a Trump Tweet, follow the link in it and suddenly they find a conspiracy theory that “explains” why they feel disenchanted. They retweet and this repeats. Trump sees what goes viral and Tweets more on the same topic. This positive feedback loop just generated something out of random noise. The conspiracy theorizing then starts it’s own reinforcing feedback loop and before you know it we have a crazed mob bashing down the Capitol doors with impunity.

Ironically Trump, who craved and idolized power, failed to understand the power he actually had and if he had a better algorithm (or just any strategy at all), he would have been reelected in a landslide. Even before he was elected, Trump had already won over the far right and he could have started moving in any direction he wished. He could have moderated on many issues. Even maintaining his absolute ignorance of how govening actually works, he could have had his wall by having it be part of actual infrastructure and immigration bills. He could have directly addressed the COVID-19 pandemic. He would not have lost much of his base and would have easily gained an extra 10 million votes. Maybe, just maybe if liberal Twitter simply ignored the early incendiary Tweets and only responded to the more productive ones, they could have moved him a bit too. Positive reinforcement is how they train animals after all.

Now that Trump has shown how machine learning can win a presidency, it is only a matter of time before someone harnesses it again and more effectively. I just hope that person is not another narcissistic sociopath.

The tragedy of low probability events

We live in an age of fear and yet life (in the US at least) is the safest it has ever been. Megan McArdle blames coddling parents and the media in a Washington Post column. She argues that cars and swimming pools are much more dangerous than school shootings and kidnappings yet we mostly ignore the former and obsess about the latter. However, to me dying from an improbable event is just so much more tragic than dying from an expected one. I would be much less despondent meeting St. Peter at the Pearly Gates if I happened to expire from cancer or heart disease than if I were to be hit by an asteroid while weeding my garden. We are so scared now because we have never been safer. We would fear terrorist attacks less if they were more frequent. This is the reason that I would never want a major increase in lifespan. I most certainly would like to last long enough to see my children become independent but anything beyond that is bonus time. Nothing could be worse to me than immortality. The pain of any tragedy would be unbearable. Life would consist of an endless accumulation of sad memories. The way out is to forget but that to me is no different from death. What would be the point of living forever if you were to erase much of it. What would a life be if you forgot the people and things that you loved? To me that is no life at all.

Fake news and beliefs

Much has been written of the role of fake news in the US presidential election. While we will never know how much it actually contributed to the outcome, as I will show below, it could certainly affect people’s beliefs. Psychology experiments have found that humans often follow Bayesian inference – the probability we assign to an event or action is updated according to Bayes rule. For example, suppose is the probability we assign to whether climate change is real; is our probability that climate change is false. In the Bayesian interpretation of probability, this would represent our level of belief in climate change. Given new data (e.g. news), we will update our beliefs according to

What this means is that our posterior probability or belief that climate change is true given the new data, , is equal to the probability that the new data came from our internal model of a world with climate change (i.e. our likelihood), multiplied by our prior probability that climate change is real, divided by the probability of obtaining such data in all possible worlds, . According to the rules of probability, the latter is given by , which is the sum of the probability the data came from a world with climate change and that from one without.

This update rule can reveal what will happen in the presence of new data including fake news. The first thing to notice is that if is zero, then there is no update. In this binary case, this means that if we believe that climate change is absolutely false or true then no data will change our mind. In the case of multiple outcomes, any outcome with zero prior (has no support) will never change. So if we have very specific priors, fake news is not having an impact because no news is having an impact. If we have nonzero priors for both true and false then if the data is more likely from our true model then our posterior for true will increase and vice versa. Our posteriors will tend towards the direction of the data and thus fake news could have a real impact.

For example, suppose we have an internal model where we expect the mean annual temperature to be 10 degrees Celsius with a standard deviation of 3 degrees if there is no climate change and a mean of 13 degrees with climate change. Thus if the reported data is mostly centered around 13 degrees then our belief of climate change will increase and if it is mostly centered around 10 degrees then it will decrease. However, if we get data that is spread uniformly over a wide range then both models could be equally likely and we would get no update. Mathematically, this is expressed as – if  then . From the Bayesian update rule, the posterior will be identical to the prior. In a world of lots of misleading data, there is no update. Thus, obfuscation and sowing confusion is a very good strategy for preventing updates of priors. You don’t need to refute data, just provide fake examples and bury the data in a sea of noise.

 

In praise of MSG

When I go to a Chinese restaurant, I am always disappointed when the menu says “No MSG.” I used to be on the “MSG is bad” bandwagon too until I learned some neuroscience. Glutamate is the primary excitatory neurotransmitter in the brain and now I’m always hoping to get extra glutamate into my system and brain. I don’t really know if MSG is going to supercharge my brain but hey the placebo effect is real. Research has never found any bad effects of MSG. See this article for details. This is also an interesting case where other people’s beliefs do directly affect me. Because, the public is hugely biased against MSG, I will be deprived of it at my local Chinese take out place. I don’t know why you have a headache after you eat at a Chinese restaurant. It might be from drinking too much tea or the salt but it’s probably not because of the MSG.

Probability of gun death

The tragedy in Oregon has reignited the gun debate. Gun control advocates argue that fewer guns mean fewer deaths while gun supporters argue that if citizens were armed then shooters could be stopped through vigilante action. These arguments can be quantified in a simple model of the probability of gun death, :

where is the probability of having a gun,  is the probability of being a criminal or  mentally unstable enough to become a shooter,  is the probability of effective vigilante action, and is the probability of accidental death or suicide.  The probability of being killed by a gun is given by the probability of someone having a gun times the probability that they are unstable enough to use it. This is reduced by the probability of a potential victim having a gun times the probability of acting effectively to stop the shooter. Finally, there is also a probability of dying through an accident.

The first derivative of with respect to is and the second derivative is negative. Thus, the minimum of cannot be in the interior  and must be at the boundary. Given that when and when , the absolute minimum is found when no one has a gun. Even if vigilante action was 100% effective, there would still be gun deaths due to accidents. Now, some would argue that zero guns is not possible so we can examine if it is better to have fewer guns or more guns.  is maximal at . Thus, unless is greater than one half then even in the absence of accidents there is no situation where increasing the number of guns makes us safer. The bottom line is that if we want to reduce gun deaths we should either reduce the number of guns or make sure everyone is armed and has military training.

 

 

 

Implicit bias

The most dangerous form of bias is when you are unaware of it. Most people are not overtly racist but many have implicit biases that can affect their decisions.  In this week’s New York Times, Claudia Dreifus has a conversation with Stanford psychologist Jennifer Eberhardt, who has been studying implicit biases in people experimentally.  Among her many eye opening studies, she has found that convicted criminals whose faces people deem more “black” are more likely to be executed than those that are not. Chris Mooney has a longer article on the same topic in Mother Jones.  I highly recommend reading both articles.

Race against the machine

One of my favourite museums is the National Palace Museum (Gu Gong) in Taipei, Taiwan. It houses part of the Chinese imperial collection, which was taken to Taiwan in 1948 during the Chinese civil war by Chiang Kai-shek. Beijing has its own version but Chiang took the good stuff. He wasn’t much of a leader or military mind but he did know good art. When I view the incredible objects in that museum and others, I am somewhat saddened that the skill and know-how required to make such beautiful things either no longer exists or is rapidly vanishing. This loss of skill is apparent just walking around American cities much less those of Europe and Asia. The stone masons that carved the wonderful details on the Wrigley Building in Chicago are all gone, which brings me to this moving story about passing the exceedingly stringent test to be a London cabbie (story here).

In order to be an official London black cab driver, you must know how to get between any two points in London in as efficient a manner as possible. Aspiring cabbies often take years to attain the mastery required to pass their test. Neural imaging has found that their hippocampus, where memories are thought to be formed, is larger than normal and it even gets larger as they study. The man profiled in the story quit his job and studied full-time for three years to pass! They’ll ride around London on a scooter memorizing every possible landmark that a person may ask to be dropped off at. Currently, cabbies can outperform GPS and Google Maps (I’ve been led astray many a time by Google Maps) but it’s only a matter of time. I hope that the cabbie tradition lives on after that day just as I hope that stone masons make a comeback.

Discounting the obvious

The main events in the history of science have involved new ideas overthrowing conventional wisdom. The notion that the earth was the center of the universe was upended by Copernicus. Species were thought to be permanent and fixed until Darwin. Physics was thought to be completely understood at the end of the nineteenth century and then came relativity theory and quantum mechanics to mess everything up. Godel overthrew the notion that mathematics was infallible. This story has been repeated so many times that people now seem to instinctively look for the counterintuitive answer to every problem. There are countless books on thinking outside of the box.  However, I think that the supplanting of “linear” thinking with “nonlinear” thinking is not always a good idea and sometimes it can have dire consequences.

A salient example is the current idea that fiscal austerity will lead to greater economic growth. GDP is defined as the sum of  consumption, investment, government spending and exports minus imports. If consumption or investment were to decline in an economic contraction, as in the Great Recession, then the simple linear idea would be that GDP and growth can be bolstered by increased government spending. This was the standard government response immediately after the financial crisis of 2008. However, starting in about 2010 when the recovery wasn’t deemed fast enough instead of considering the simple idea that the stimulus wasn’t big enough, the idea that policy makers, especially in Europe, adopted was that government spending was crowding out private spending so that a decrease in government spending would lead to a net increase in GDP and growth. This is very nonlinear thinking because it requires a decrease in GDP to induce an increase in GDP. Thus far this idea is not working and austerity has led to lower GDP growth in all countries that have tried it.  This idea was reinforced by a famous, now infamous, paper by Reinhart and Rogoff, which claimed that when government debt reaches 90% of GDP, growth is severely curtailed. This result has been taken as undisputed truth by governments and the press even though there were many economists who questioned it.  However, it turns out that the paper has major errors (including an Excel coding error). See here for a summary.  This is case where the nonlinear idea (as well as conflating correlation with causation) is probably wrong and has inflicted immense hardship on a large number of people.

 

Cognitive dissonance

The New York Times has a story today describing how the American middle class are becoming more reliant on government aid, much to their chagrin.  However, the reaction of many of the people interviewed  is animosity towards government programs and support for culling them, even though that would hurt themselves economically.

New York Times: One of the oldest criticisms of democracy is that the people will inevitably drain the treasury by demanding more spending than taxes. The theory is that citizens who get more than they pay for will vote for politicians who promise to increase spending.

But Dean P. Lacy, a professor of political science at Dartmouth College, has identified a twist on that theme in American politics over the last generation. Support for Republican candidates, who generally promise to cut government spending, has increased since 1980 in states where the federal government spends more than it collects. The greater the dependence, the greater the support for Republican candidates.

Conversely, states that pay more in taxes than they receive in benefits tend to support Democratic candidates. And Professor Lacy found that the pattern could not be explained by demographics or social issues.

Cognitive dissonance is a term in psychology that describes the uncomfortable feeling when two conflicting thoughts are simultaneously held and the attempts to rationalize the inconsistency. The political dynamics currently playing out in the United States may be a giant manifestation of this phenomenon.  A telling aspect of the article was that many of the people interviewed acknowledged that they could not survive without government assistance but felt that they did not deserve such help and preferred that it be reduced rather than subjecting others to higher taxes to pay for it.   This rather honorable attitude serves as a stark contrast to the premise of the heavily debated new book of Charles Murray, Coming Apart (see New York Times review here) that argues that the economic travails of the white working class is due largely to a lapse in moral values.  What was also striking in the article was that there was no sense that the dire economic situation these people were facing was due to the fact that the economic game was stacked against them.  There was just a silent resignation that this is the way things are.  The American mythos of the self-reliant and self-made individual is a powerful metaphor that is firmly implanted in a large fraction of the population.  People will not always support policies that are in their economic interests.  This facility for self-denial is a large part of what makes us human.  How we obtained it is still an unresolved problem in evolutionary biology.