Wednesday, May 24, 2017

Science, Uncertainty, and "The Hunt for Vulcan"

Today, I'm listening to a science podcast from earlier this month, "How the Planet Vulcan Changed Science Forever":
In the podcast, which runs in FiveThirtyEight’s What’s The Point feed, senior science writer Maggie Koerth-Baker, lead science writer Christie Aschwanden and senior editor Blythe Terrell talk through how science ideas evolve over time — and how challenging that process can be.

The second part of this month’s podcast features Christie interviewing [Thomas] Levenson about [his] book, [The Hunt for Vulcan].
I'll have to add Levenson's book to my reading list. And if you want to read ahead for next month's podcast, they'll be talking about "Flavor" by Bob Holmes.

Tuesday, May 23, 2017

Trump's Budget

Trump released his first budget, which FiveThirtyEight observes is built on fantasy:
President Trump’s first budget, released Tuesday, is not going to become law. First, because presidents’ budgets never become law, not the way they’re initially proposed. And second, because the specifics of Trump’s fiscal 2018 budget — enormous cuts to nearly every significant government program other than defense, Social Security and Medicare in order to pay for huge tax cuts that would go disproportionately to the wealthy — seem designed to alienate not just Democrats (at least a few of whom Trump needs to get his budget through the Senate) but also moderate Republicans and the public at large. Trump likely knows this; the White House released the budget while he is thousands of miles away on his first foreign trip as president.
Hmmm...

The fantastical part of his budget? He's basing it accelerating economic growth, up to 3% by 2021. This is much higher than estimates from the Congressional Budget Office (1.9%), the Federal Reserve (1.8%), and what was observed last year (1.6%). There are also countless threats to economic growth, including limits on immigration and the retirements of baby boomers. Productivity is also slowing down and no one knows why, making it difficult to predict what the economy will look like.

The response from the White House is basically chiding the Obama administration for being so pessimistic about the nation's economic growth, and faith that we can obtain 3% growth. Hope and faith is important, but not to build a budget upon.

Monday, May 22, 2017

Would You Like Fryes With That?: A Psychological Analysis of Fraud Victims

What started as an over-the-top music festival in the Bahamas ended up as a social media joke. The Frye Festival, which was supposed to take place in late April, was canceled - after guests had already started arriving:
On social media, where Fyre Festival had been sold as a selfie-taker’s paradise, accounts showed none of the aspirational A-lister excesses, with only sad sandwiches and free alcohol to placate the restless crowds. General disappointment soon turned to near-panic as the festival was canceled and attendees attempted to flee back to the mainland of Florida.

“Not one thing that was promised on the website was delivered,” said Shivi Kumar, 33, who works in technology sales in New York, and came with a handful of friends expecting the deluxe “lodge” package for which they had paid $3,500: four king size beds and a chic living room lounge. Instead Ms. Kumar and her crew were directed to a tent encampment. Some tents had beds, but some were still unfurnished. Directed by a festival employee to “grab a tent,” attendees started running, she said.

Now, they're under federal investigation for fraud. In hindsight, the whole thing is clearly a scam. Websites disappeared because designers weren't getting paid. Past customers of previous services complained that special offers never materialized. Not to mention hearing from disgruntled past employees and contractors. In fact, it's so clearly a scam, it's surprising anyone fell for it.

It's very easy for us to look at all of this information now, and come to the conclusion that it was a scam. The problem with hindsight is that it's always 20/20. The same cannot be said for foresight but that doesn't stop people from saying they would have known all along. This is called hindsight bias.

There's probably also some victim blaming going on here. How could these people not know any better? Had we been in the same situation, of course we would have known. We distance ourselves from the victims of this fraud, because it helps us feel more safe, more in control of our world. The same thing could never happen to use because we wouldn't let it.

It's easy to understand reactions after-the-fact. What's more interesting, I think, is to try to figure out what got the attendees and contractors to buy into this fraud to begin with. We ask incredulously, "What were they thinking?" But seriously - what were they thinking?

Human beings are social creatures. We have to be. In order for our species to survive in a hostile environment, it was necessary for us to band together. We formed groups, which became tribes, which become whole societies. And in order to survive in these social structures, it was necessary for to have some trust in the people around us. You could argue that trust is an evolutionarily selected trait in humans. Let's face it, if you don't trust anyone else, it's really unlikely that you're going to reproduce. You have to at least trust one person to do that (at least, if you're reproducing on purpose).

So now we have a species pre-disposed toward trusting others. But we don't give our trust to just anyone - rather, to people we perceive as having certain traits. The more charismatic the leader, the more likely we are to trust them. And if everyone else in our social group trusts a certain person, we're more likely to trust that person too, at least externally.

Internally we may be more skeptical. If we look at the results of the Milgram study, we find that many people reported after the fact feeling very uncomfortable with what they were doing. They even had doubts as to whether they were doing the right thing. But they continued shocking the learner nonetheless. Why? Because somebody in the lab coat, somebody they perceived as having expertise, told them to. This person knows better than me, so I'm just going to keep doing what they say. It doesn't matter whether they actually have any expertise. It's the perception of expertise. And that is something charismatic leaders can do. They can convince you that they know more than they actually do, that they are an expert in something that you are not an expert in. Mc Farland had people believing that he was an expert in entertainment, technology, and rubbing elbows with celebrities. He had people convinced that he could help them to do the same thing.

I'm sure there are some people who didn't trust him. But they went along with him anyway, because there were people who did believe him, who believed that he could do exactly what he said he was going to do, despite instances in the past where he had simply wasted other people's money. But that's the nature of conformity. At the very least, if everyone else is doing it, that makes us more likely to question why we aren't doing it too. Maybe the rest of the group knows something that we don't. Maybe we're misreading the situation.

In the 1950s, Solomon Ash conducted what he said was a study on perception, that was actually a study of conformity.  Actors who pretended to be fellow participants publicly selected what was clearly the wrong answer, to see if the true participant would do the same; 32% of participants conformed with the wrong answer every time across multiple trials, and 75% conformed at least once.

Obviously, there are some other cognitive fallacies occurring here and in similar scams. The sunk cost fallacy, for instance, would explain why people held onto the idea of the festival, especially if they kept paying into it over time. It's the same principle that keeps people pumping money into slot machines or staying in bad relationships - if I keep this up, eventually it will be worth it, and I've put in too much time, money, and/or effort to walk away now. That's what happens when something has a variable schedule of reinforcement. We learn from variable schedules that if you just keep it up, the reward will eventually come.

Combine the sunk cost fallacy with a charismatic leader, the promise of rubbing elbows with people we admire, and other members of our social group going along with it, and it's not surprising at all that people fell for this scam. The problem is that people are going to keep falling for it. The people who were hurt in this particular scam will probably learn their lesson and stay far away from McFarland and his endeavors. But there will always be others will fall for it. And they're unlikely to learn anything from the negative experience of their peers - they'll blame the victims, they'll insist they would have known all along, and they'll distance themselves from those who have been hurt. They'll think of them as the outgroup - people who aren't like them in the ways that matter - and ascribe negative characteristics to them.

There will always be people like McFarland. And there will always be people who fall for his song and dance.

Sunday, May 21, 2017

Statistics Sunday: The Analysis of Variance

In the t-test post and bonus post, I talked about how to use the t-test to compare two sample means and see if they are more different than we would expect by chance alone. This statistic is great when you have two means, but what if you have more than 2?

Here's a more concrete example. Imagine you're going to a movie with three friends. You buy your tickets, get your popcorn and sodas, and go into the theatre. You turn to your friends to ask where they'd like to sit.

The first says, "The back. You don't have anyone behind you kicking your seat and you can see the whole screen no matter what."

"No," the second friend says, "that's way too far away. I want to sit in the front row. No one tall in front of you to block your view, and you can look up and see the actors larger-than-life."
And you can be like these guys

"You're kidding, right?" asks the third friend. "And deal with the pain in neck from looking up the whole time? No, thank you. I want to sit farther back, but not all the way in the back row. The middle is the best place to sit."

How do you solve this dilemma? With research, of course! (Why? How do you solve arguments between friends?) You could pass out a survey to movie goers to see who has the best experience of the movie based on where they sit - front, middle, or back. But now you want to see which group, on average, has the best experience. You know a t-test will let you compare two groups, but how do you compare three groups?

Yes, you could do three t-tests: front v. middle, front v. back, and middle v. back. But remember that you inflate your Type I error with each statistical test you conduct. You could correct your alpha for multiple comparisons, but you also increase your probability of Type II error doing that. As with so many issues in statistics, there's a better way.

Enter the analysis of variance, also known as ANOVA. This lets you test more than two means. And it does it, much like the t-test, by examining deviation from the mean. In any statistical situation, the expected value is the mean - in this case, it's what we call the grand mean, the mean across all 3+ groups. If seating location makes no difference, we would expect all three groups to share the same mean; that is, the grand mean would be the best descriptor for everyone. We're testing the statistical hypothesis that the grand mean is not the best descriptor for everyone. So we need to see how far these groups are from the grand mean and if it's more than we expect by chance alone.

But the mean is a balancing point; some groups will be above the grand mean, and some below it. If I took my grand mean, and subtracted each group mean from it, then added those deviations together, they would add up to 0 or close to it. What do we do when we want to add together deviations and not have them cancel each other out? We square them! Remember - this is how we get variance: the average squared deviation from the mean. So, to conduct an ANOVA, we look at the squared deviations from the grand mean. Analysis of variance - get it? Good.

Once you have your squared deviations from the grand mean - your between group variance - you compare those values to the pooled variance across the three groups - your within group variance, or how much variance you expect by chance alone. If your between group variance is a lot more than your within group variance, the result will be significant. Just like the t-test, there's a table of critical values, based on sample size as well as the number of comparisons you're making; if your ANOVA (also known as a F test - here's why) is that large or larger, you conclude that at least two of the group means are different from each other.

You would need to probe further to find out exactly which comparison is different - it could be only two are significantly different or it could be all three. You have to do what's called post hoc tests to find out for certain. Except now, you're not fishing - like you would be with multiple t-tests. You know there's a significant difference in there somewhere; you're just hunting to find out which one it is. (Look for a future post about post hoc tests.)

The cool thing about ANOVA is you can use it with more than one variable. Remember there is a difference between levels and variables. A level is one of the "settings" of a variable. For our caffeine study, the levels are "experimental: receives caffeine" and "control: no caffeine." In the movie theatre example, the variable is seating location, and the levels are front, middle, and back. But what if you wanted to throw in another variable you think might effect the outcome? For instance, you might think gender also has an impact on movie enjoyment.

There's an ANOVA for that, called factorial ANOVA. You need to have a mean for each combination of the two variables: male gender-front seat, female gender-front seat, male gender-middle seat, female gender-middle seat, male gender-back seat, and female gender-back seat. Your ANOVA does the same kind of comparison as above, but it also looks at each variable separately (male v. female collapsed across seating location, and front v. middle v. back collapsed across gender) to tell you the effect of each (what's called a main effect). Then, it can also tell you if the combination of gender and seating location changes the relationship. That is, maybe the effect of seating location differs depending on whether you are a man or a woman. This is called an interaction effect.

On one of these Statistics Sundays, I might have to show an ANOVA in action. Stay tuned!

Saturday, May 20, 2017

Long Road Ahead

So a special counsel has been appointed to continue the Russia investigation. People on both sides of the political continuum are pretty happy about this (after all, 78% of Americans in a recent poll were in support of this) - people who believe Trump is innocent of any wrongdoing can depend on the investigation to exonerate him, while people who believe Trump is guilty can finally move one step closer to removal from office.

But it's liable to be years before anything definitive comes out of this investigation. Clare Malone at FiveThirtyEight sat down with political scientist, Brandon Rottinghaus, to discuss the history of political scandals and when this particular investigation might end:
Number one, the president is insulated politically so that it’s hard to get the president’s staff and counsels to turn on the president.

Number two, presidents are often insulated legally; they have the ability to do a lot of things that staff or Cabinet members aren’t able to do.

The third thing is that independent counsels, special counsels and any other investigatory bodies are reluctant to challenge the president in a way that might lead to impeachment for fear that it looks like a non-democratic outcome to the legal process. Although obviously these things run into partisanship very quickly, people are less willing to remove a president unless the crisis is severe and the implications are egregious.

[Y]our standard investigation, even of a person who’s a Cabinet member or staff member, is probably between two and three years. For a president in particular, it tends to be longer because the amount of care to be taken is greater.
Regardless of how long the process will take, the fact that an investigation is underway will still have an impact on the current administration, long before any results are shared:
[T]hese kind of events often lead to legislative paralysis, and if you’re not producing legislation, the public tends to take it out on the incumbent party, especially the president. So it’s a kind of double whammy for presidents looking to keep those approval ratings above water.
It's a long road ahead:

This gorgeous photo is by photographer Glenn Nagel

Friday, May 19, 2017

Meanwhile, In the Mind of AI

Researcher and lover of neural networks, Janelle Shane, was thinking about the strange and usual paint names and wondered what a neural network would name different paint shades. So she fed in 7,500 Sherwin Williams paint names and RGB values, and let the network go to work. The results is not only an interesting insight into neural networks and a higher-level view of how they work; it's pretty hilarious what the network came up with:
One way I have of checking on the neural network’s progress during training is to ask it to produce some output using the lowest-creativity setting. Then the neural network plays it safe, and we can get an idea of what it has learned for sure.

By the first checkpoint, the neural network has learned to produce valid RGB values - these are colors, all right, and you could technically paint your walls with them. It’s a little farther behind the curve on the names, although it does seem to be attempting a combination of the colors brown, blue, and gray.
Later in the training process, the neural network is about as well-trained as it’s going to be (perhaps with different parameters, it could have done a bit better - a lot of neural network training involves choosing the right training parameters). By this point, it’s able to figure out some of the basic colors, like white, red, and grey, although not reliably.

In fact, looking at the neural network’s output as a whole, it is evident that:
  1. The neural network really likes brown, beige, and grey.
  2. The neural network has really really bad ideas for paint names.

Stoner Blue is pretty nice. Think I'll skip Sindis Poop.

In Your Mind, The Future is Now

In an article published today by the New York Times, journalist John Tierney teams up with psychologist and founder of the positive psychology movement, Marty Seligman, for a great article about anticipation and the importance of the future in our approach to the present:
But it is increasingly clear that the mind is mainly drawn to the future, not driven by the past. Behavior, memory and perception can’t be understood without appreciating the central role of prospection. We learn not by storing static records but by continually retouching memories and imagining future possibilities. Our brain sees the world not by processing every pixel in a scene but by focusing on the unexpected.

Our emotions are less reactions to the present than guides to future behavior. Therapists are exploring new ways to treat depression now that they see it as primarily not because of past traumas and present stresses but because of skewed visions of what lies ahead.

Prospection enables us to become wise not just from our own experiences but also by learning from others. We are social animals like no others, living and working in very large groups of strangers, because we have jointly constructed the future. Human culture — our language, our division of labor, our knowledge, our laws and technology — is possible only because we can anticipate what fellow humans will do in the distant future. We make sacrifices today to earn rewards tomorrow, whether in this life or in the afterlife promised by so many religions.

The central role of prospection has emerged in recent studies of both conscious and unconscious mental processes, like one in Chicago that pinged nearly 500 adults during the day to record their immediate thoughts and moods. If traditional psychological theory had been correct, these people would have spent a lot of time ruminating. But they actually thought about the future three times more often than the past, and even those few thoughts about a past event typically involved consideration of its future implications.

When making plans, they reported higher levels of happiness and lower levels of stress than at other times, presumably because planning turns a chaotic mass of concerns into an organized sequence. Although they sometimes feared what might go wrong, on average there were twice as many thoughts of what they hoped would happen.
The article is based on a book Marty wrote with fellow psychologist Roy Baumeister, philosopher Peter Railton, and psychiatrist Chandra Sripada, called Homo Prospectus.