It is long past time the US eliminated gerrymandering, the drawing of district lines specifically for the purpose of favoring one political party, across the board. This requires either a 50 state agreement, or action at the federal level. This has been a problem since near the beginning of our democracy, and seems to be getting worse. We are now in the middle of a mid-decade tit-for-tat rash of gerrymandering that is extremely anti-democratic, so it’s a good time to raise this as an issue voters should definitely understand and prioritize.

As a quick aside – this is not a “political” blog, which does not mean that I never discuss political issues or topics with a political dimension. It partly means that I try my best to by non-partisan, and to avoid purely political value-judgements. I recognize this is an impossible ideal – we all have our biases and perspectives that color our thinking on topics in subtle ways. But we can try. Also, this is not a strictly science blog, it covers science, critical thinking, and media savvy, which are part of what we call scientific skepticism. Recently I started a video podcast, Political Reality, with co-host Andrea Jones Roy, who is a political scientist, for the purpose of applying scientific skepticism to political topics. This is also not a partisan show, and is mostly part civics lesson and part fact-checking. With that in mind, I thought I would write about what science and critical thinking have to say about gerrymandering, given that this is a topic in the news recently, although not as much as I think it should be. We also did cover this topic on Political Reality.

The term gerrymander dates back to 1812 when Massachusetts Governor Elbridge Gerry redistricted his state’s representative districts in order to favor his party, the Democratic Republicans. One of the districts looked like a salamander, leading the Boston Gazette to quip that it was really a “Gerry-mander”, and the name stuck. (Ironically, the two parts of that term, gerry and mander, both kinda sound like they mean “rig”, but the word has nothing to do with that.) Since then all political parties have used gerrymandering to gain unfair advantage. This stems from some features of US politics.

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Remember The Last Starfighter from 1984? In that movie a trailer-park kid with limited prospects spends his time on an arcade-style video game, Starfighter. He plays the game so much that he beats the final level, and it turns out he is the first person to ever do so. He is heavily criticized for spending so much time playing a game, which is seen as a sign of boredom and lack of ambition – a waste of time. The twist (42 year old spoiler incoming) is that the game was actually a test (the Excalibur test – a deliberate reference to King Arthur) to find a skilled pilot for an actual real-life starfighter. He goes on to save the galaxy from invasion.

The interesting premise of the movie is that playing a video game is not only a test of real-life skill, but can be used to train such skill. In 1984 this was  kind of a new idea, and appealing to a generation of kids newly hooked on video games. Video games have been significantly mainstreamed over the last half century, but there is still a bit of a cultural stigma attached to them – they are seen as the realm of dorks and geeks, with inevitable jokes about how avid video gamers with “never get laid” (or something to that effect). Since the beginning of their popularity parents have worried, with such worry being fed by a sensationalist media, that video games were going to “rot” their kids’ brains, turn them into losers who can never get a skilled job, and might even cause violent behavior. Every mass shooting someone brings up violent video games.

But the evidence simply does not support these concerns. One big problem with the research is that it shows correlation only, not causation. Sure, people who play aggressive video games tend to be more aggressive, but that doesn’t mean the game is the cause. Further, there are many confounding factors, and more recent research shows that violence in the game is not the key feature. It has more to do with the level of difficulty and the resulting frustration that seems to raise aggression, not violence in the game. More competitive and difficult games tend to be more stimulating, regardless of the level of violence. The bottom line – after decades of research, systematic reviews conclude: “There is insufficient scientific evidence to support a causal link between violent video games and violent behavior.”

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Many people might find this to be an easy question and simple concept – what is your favorite color? In fact it was used as the quintessential easy question by the bridge guardian in Monty Python and the Holy Grail. But it is a good rule of thumb that everything is much more complicated than you think or than it may at first appear, and this is no exception. We recently had a casual discussion about this topic on the SGU, and it left me unsatisfied, so I thought I would do a deeper dive. Perhaps there is a neuroscientific answer to this question.

The panel differed in their reactions to the question of favorite color (we were just giving our subjective feelings, not discussing research or evidence). Cara felt that “favorite color” is largely arbitrary. Kids are asked to pick a favorite color, which they do (under pressure) and then often just stick with that answer as they get older. She also felt the question was meaningless without context – are you referring to clothes, cars, house color, or something else? Jay was at the other end of the spectrum – he has a strong affiliation for the color orange which gives him a pleasant feeling. The rest were somewhere in between these two extremes.

I knew there had to be a science of “favorite color”, which I thought might be interesting. Indeed there is – and it is interesting.

First, what is the distribution of favorite color, across the world and demographically? Blue is, far and away, the most favorite color, in most countries across the world, so it seems to be very cross-cultural. It is also the favorite across age groups and gender. The second-most favorite color is either green, red, or purple. Brown is almost universally the least favorite color. Gender has an effect on favorite color, with more women favoring pink, and reds in general (but still preferring blue overall). Republicans still prefer blue over red, but more Republicans prefer red than Democrats. There are country-specific differences as well. Red is a higher preference in China than many other countries, for example.

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Many teachers are panicking over AI (artificial intelligence), and for good reason. This goes beyond students using AI to cheat on their homework or write their essays for them. If you have AI essentially think for you, then you will not learn to think. On the other hand optimists point out that AI can be a powerful tool to aid in learning. It all comes down to how we use, regulate, and manage our AI tools.

The cautionary approach was captured well, I think, by Mark Crislip in this SBM commentary, in which worries about the effects of AI on doctor education. How will a new generation of physicians learn how to think like expert clinicians if they can have AIs do all their clinical thinking for them? My question is – is AI fundamentally different than all the other technological advances that have come before. Did calculators take away our ability to do math? The answer appears to be no. Students still gain basic math skills at the same rate with or without access to calculators. But there are lots of confounding factors here, and so some teachers still warn of allowing kids access to calculators too soon. Others point out that access to calculators has simply shifted our math abilities, away from basic operations toward more modeling, problem solving, and complex concepts. It seems we are in the middle of the same exact conversation about AI.

We can also think about things like GPS. My ability to navigate from point A to point B without GPS, or to navigate with maps, has definitely declined. But using GPS has also made my navigating to unfamiliar locations easier and more efficient. I would not want to go back to a world without it.

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A recent study shows pretty clearly that highschoolers benefit from a little extra sleep. We will get to the study in a bit, but first I want to note that this information is not new. Teenagers tend to stay up late, and yet we make them get up super early to be at class, often by 7:00 AM. This is not good for their health or their learning. So why do we do it?

The primary reason is logistical, which is tied to cost. School systems have tiered start times for elementary, middle school, and high school because this allows them to use the same fleet of buses and drivers for all three. Starting high school later, at the same time as middle school, would mean increasing the size of the fleet. There are other stated reasons, but honestly I think this is the real reason and everything else is a backend justification. The other reasons are more tradeoffs, that benefit some people but not others. For example, a parent with a long commute could drop off their highschooler on the way to work. There is more time for after school clubs, sports, and jobs. While some older teens may get home early to watch their younger siblings until their parents get home.

This all points to a main reason our civilization is frustratingly sub-optimal (to be polite). The default is to follow the pathway of least resistance  – everyone just does what’s best for themselves, with people in power doing their best to solidify more power, with vested interests putting the most consistent effort into making the system work for their narrow interest. What is often lacking is any kind of systemic planning, and when that does occur (even with the best intentions) the law of unintended consequences often results in a net wash or even detriment. The world is complex, and we are just not very good at managing that level of complexity. What we need are institutions that can accumulate evidence-based institutional knowledge to incrementally make things work better. But that’s a lot of work, and it’s too easy for vested interests to sabotage such efforts.

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I would not be surprised if the period of time roughly between 2000 and 2050 looms large in the collective mind of humanity for centuries to come – and not in a good way. It is increasingly seeming like our behavior during this period is locking in a certain about of climate change, including sea level rises and loss of ice sheets, for centuries. Some climate changes are likely to be irreversible on human time scales.

A recent study adds to the mountain of evidence that this is the case. They find that under current climate policies emissions through 2050 lock in 0.3 meters of sea level rise through 2300. If current policy continues through 2090 then the locked in sea level rise will be about 0.8 meters. If, on the other hand, we make significant efforts to reduce emissions, we can reduce this locked in sea level rise by 0.6 meters.  The point is, what we do now will impact global coastlines for centuries. And while 0.8 meters may not sound like a lot, that is an average with some areas experiencing much more. That is also enough to cause significant displacement of coastal populations.

Meanwhile, it is during this time period (the first half of the 21st century) that the consensus of climate experts was pretty solid – the evidence is clear that greenhouse gas emissions are trapping heat and causing average global warming. You could argue that this consensus existed earlier, but 2000 is a convenient round number – by then there was no reasonable denial of that consensus. And of course, I am talking about the big picture, not all the tiny details. It was clear we needed to think of ways to move our civilization away from burning more and more fossil fuel. In 2016 the Paris Accords were signed, formalizing global recognition that we need to collectively address this issue. This makes it difficult to deny that we did not recognize there was a problem and that we urgently need to do something about it.

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I wasn’t planning on doing a follow up to my recent post on AI so quickly, but a published commentary on the issue makes a good point of discussion. I know it can get tiring to see so much news and commentary about AI, but we are in the middle of a rapidly evolving and potentially disruptive technology, so an active and dynamic conversation is needed. Also, yesterday I was giving a seminar to high school STEM students on critical thinking and media savvy, and the students were eager to raise the question of AI and what impact it has. They very much wanted to know how to navigate this world they are inheriting overwhelmed with AI-generated misinformation and deep fakes. How bad is it going to get, and what should they do?

Like many such questions we can focus on two levels – individual and societal. This question very much needs to be addressed issue by issue, but generally speaking I am a proponent of dealing with societal issues with societal solutions, and not just dumping all the burden and responsibility on individual citizens. This does not mean individuals should not take responsibility for themselves, only that this should not be the only solution. Let’s take crime as an example. There are steps that individuals can take to make themselves less vulnerable to crime, but that is not the ultimate solution to a society overwhelmed by crime. We also need police, social programs, good street lighting, and other measures to reduce overall crime.

The same is true with AI-generated deep fakes, misinformation, disinformation, and just low quality slop. Since my talk yesterday was on critical thinking, I focused on what we can do as individuals. This is basically scientific skepticism 101. Evaluate the source of any information and always try to track back any claim to its original source. Do not accept someone else’s narrative about that information – if it’s important, take the time to find out for yourself. Also, do not let other people curate information for you, because that let’s them control your information ecosystem and create the narrative for you. Do not rely on any one source for information. Seek out different sources and different perspectives, and specifically seek out information that contradicts or falsifies any claim you are facing (especially if it’s something you want to be true).

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There is a lot of talk concerning the growing plastic waste problem in the world, and that’s because it is a real and serious problem. The world produces about 430 million tons of plastic waste per year, and this is steadily increasing over time. About 6 millions tons of this will end up in water environments – rivers, lakes, shorelines, and the ocean. This plastic can be disruptive to marine life through entanglement, ingestion, and contamination.

There is a bit of semi-good news – a recent study finds that policies aimed at reducing single-use plastic bags are somewhat effective. They used data from apps used by people who clean up waste along the shoreline. The apps track how much and what kind of waste they pick up. This was a convenient source of study data. It is also true that in the US there is no Federal ban on plastic bags, so the researchers were able to compares local, county, and state-wide policies limiting plastic bag use. They could also compare the two main types of policies, bans and fees.

They found that areas with plastic bag policies had 25 – 47% reduced plastic bag waste compared to areas without such policies. The effect was greater over time, and was greater in areas with higher initial plastic bag waste. They also found that plastic bag fees were more effective than bans, which I found interesting. Partial bans were the least effective. Policies at all governmental scales were effective, with state level policies being the most effective.

But – and here is the reason for my caveats above – in all areas the absolute amount of plastic waste increased over the study period. There was only a decrease in the relative amount of increase in areas with a policy compared to those without. So policies decrease the rate of increase of plastic waste. How can this data inform policies going forward?

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As a scientific concept – does race exist? Is it a useful construct, or is it more misleading than useful? I wrote about this question in 2016, and my thinking has evolved a bit since then. My bottom line conclusion has not changed – the answer is, it depends. There is no fully objective answer because this is ultimately a matter of categorization which involves arbitrary choices, such as how to weight different features, how much difference is meaningful, and where to draw lines. People can also agree on all the relevant facts, but disagree simply on emphasis. (If all of this is sounding familiar it’s because the same issues exist surrounding biological sex.)

Here are some relevant facts. Humans – Homo sapiens – are a single species. While we are an outbred species with a lot of genetic diversity, we have passed through several fairly recent genetic bottlenecks (most recently around 900k years ago) and the genetic disparity (amount of difference) among humans is relatively small (about 0.1%). It is also true that genetic variation is not evenly distributed among human populations but tend to cluster geographically. However, genetic variation within these clusters is greater than genetic variation between these clusters. Further, obvious morphological differences between identifiable groups tend to be superficial and not a good reflection of underlying genetic diversity. But at the same time, genetic background can be meaningful – predicting the risk of developing certain diseases or responding to certain medications, for example. Genetic variation is also not evenly distributed. Most genetic variations within humans is among Africans, because all non-Africans are derived from a recent genetic bottleneck population about 50-70k years ago.

How should we summarize all of these non-controversial and generally agreed upon facts? You can emphasize the clustering and say that something akin to race exists and is meaningful, or you can emphasize the genetic  similarity of all humans and lack of discrete groups to say that race is not a meaningful or helpful concept. So, as a purely scientific question we have to recognize that there is no completely objective answer here. There are just different perspectives. However, that does not mean that every perspective is equally strong or that our choice of emphasis cannot be determined by other factors, such as their utility in specific contexts.

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It is generally accepted that the transition from hunter-gatherer communities to agriculture was the single most important event in human history, ultimately giving rise to all of civilization. The transition started to take place around 12,000 years ago in the Middle East, China, and Mesoamerica, leading to the domestication of plants and animals, a stable food supply, permanent settlements, and the ability to support people not engaged full time in food production. But why, exactly, did this transition occur when and where it did?

Existing theories focus on external factors. The changing climate lead to fertile areas of land with lots of rainfall, at the same time food sources for hunting and gathering were scarce. This occurred at the end of the last glacial period. This climate also favored the thriving of cereals, providing lots of raw material for domestication. There was therefore the opportunity and the drive to find another reliable food source. There also, however, needs to be the means. Humanity at that time had the requisite technology to begin farming, and agricultural technology advanced steadily.

A new study looks at another aspect of the rise of agriculture, demographic interactions. How were these new agricultural communities interacting with hunter-gather communities, and with each other? The study is mainly about developing and testing an inferential model to look at these questions. Here is a quick summary from the paper:

“We illustrate the opportunities offered by this approach by investigating three archaeological case studies on the diffusion of farming, shedding light on the role played by population growth rates, cultural assimilation, and competition in shaping the demographic trajectories during the transition to agriculture.”

In part the transition to agriculture occurred through increased population growth of agricultural communities, and cultural assimilation of hunter-gatherer groups who were competing for the same physical space. Mostly they were validating the model by looking at test cases to see if the model matched empirical data, which apparently it does.

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