The Long Game 36: The Problems with Research Papers, 'Oumuamua, Color Wheel, the Dark Side of Smart
1️⃣ One Billion Americans, High Potential ideas, Are Experts Real, Backfiring Rules, and Much More!
Greetings from Paris!
In this episode, we explore:
The Problems with Research Papers
A color wheel explains humanity
The dark side of smart
High potential ideas to work on
One Billion Americans
Let’s dive in!
📝 The Problems with Research Papers
To understand the effects of some health interventions on our health, we need to research to validate the hypotheses. The problem is that many research papers are actually wrong for multiple reasons, which makes the whole process of improving health at scale even more difficult.
For example, here’s a thought-provoking paper about how individual choices influence scientific results:
In many health domains, we are concerned that observed links - for example, between “healthy” behaviors and good outcomes - are driven by selection into behavior. This paper considers the additional factor that these selection patterns may vary over time. When a particular health behavior becomes more recommended, the take-up of the behavior may be larger among people with other positive health behaviors. Such changes in selection would make it even more difficult to learn about causal effects. I formalize this change in selection in a simple model. I test for evidence of these patterns in the context of diet and vitamin supplementation. Using both microdata and evidence from published results I show that selection varies over time with recommendations about behavior and that estimates of the relationship between health outcomes and health behaviors vary over time in the same way. I show that adjustment for selection on observables is insufficient to address the bias. I suggest a possible robustness approach relying on assumptions about proportional selection of observed and unobserved variables.
This isn’t the only paper raising awareness of this problem. John P. A. Ioannidis wrote a famous paper back in 2005, arguing that most published research findings are false. Here’s the summary:
There is increasing concern that most current published research findings are false. The probability that a research claim is true may depend on study power and bias, the number of other studies on the same question, and, importantly, the ratio of true to no relationships among the relationships probed in each scientific field. In this framework, a research finding is less likely to be true when the studies conducted in a field are smaller; when effect sizes are smaller; when there is a greater number and lesser preselection of tested relationships; where there is greater flexibility in designs, definitions, outcomes, and analytical modes; when there is greater financial and other interest and prejudice; and when more teams are involved in a scientific field in chase of statistical significance. Simulations show that for most study designs and settings, it is more likely for a research claim to be false than true. Moreover, for many current scientific fields, claimed research findings may often be simply accurate measures of the prevailing bias. In this essay, I discuss the implications of these problems for the conduct and interpretation of research.
The problem of fake news and bad reporting that society is going through right now is also present in science. It’s very hard to solve because if you centralize the truth and give the power to decide just to a few “experts,” you can easily end up with an echo chamber that doesn’t explore things outside of its comfort zone. And if you let anyone publish anything, it becomes a nightmare because you basically have a very low rate of papers that are right, which can erode the trust we put in science (that’s what’s happening in nutritional epidemiology—for example, this paper shows that almost everything is associated with cancer.)
To keep progressing, we need science, and we greatly need a science of science to make sure scientists remain trustworthy and to minimize “identity science,” where scientists become one with their ideas and pursue them before pursuing the truth.
For more, this conversation with John P. A. Ioannidis explains everything that’s wrong with science right now.
🎨 A Color Wheel Explains Humanity
I’m not a big fan of personality tests, but this article is excellent and can help you better understand yourself.
Personalities, organizations, goals, and means can all be thought of in terms of the Magic colors they typify, allowing you to draw interesting connections, make surprisingly useful predictions, identify deficits and growth areas, and increase empathy.
Below are the five colors of Magic: white, blue, black, red, and green. Each color has a central goal and a default strategy.
The key recognition is that all of these ways of being are okay. They’re all good, they’re all evolved and refined, they’re all adaptive and workable.
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🧠 Better Thinking
🏴 The Dark Side of Smart
I like to see the positive in people and our species, but I also like to read materials that go against what I like or what I believe. This article from an evolutionary psychologist delivers a dark message: “Intelligence is associated with coming up with more convincing bullshit and with being a better liar, but not associated with a better ability to recognize one’s own bias.”
It turns out this was also the thesis of an influential paper by biologist Richard Dawkins:
Because of the way natural selection works, it is reasonable for us to picture an animal as a machine designed to preserve and propagate the genes that ride inside it (Dawkins 1976). As a means to this end it will often manipulate objects in its world, pushing them around to its own advantage. Some of these objects will themselves be living creatures -mates, parents, prey, rivals-each one a machine designed to propagate its own genes in similar ways.
I’m sure most people would prefer the explanation that intelligence is selected to favor collaboration between humans, but it seems to be the opposite:
Hold on a minute you might be saying to yourself—you evolutionary people are so cynical—didn’t we also get smart to cooperate? Perhaps, to some degree. But research suggests intelligence has been a lot more important, especially for theory of mind for competition, than for cooperation. Evolutionary models, for example, have shown that competition promotes the ability to think about other minds more strongly than cooperation. And studies have shown that areas of the brain related to thinking about other minds are activated more by competition than cooperation.
I don’t know what to do with this new information, but still, I prefer to know it:
The dark side of smart is that whenever we do good works, and cooperate, we draw from our manipulative past. The even darker side of smart is that competition doesn’t just select an ability to manipulate but also an adaptive ability to be unpredictable. And one of the best ways to be unpredictable is to not know yourself. So we have evolution to thank for shielding us from complete self-knowledge. As a result, most of our own minds are shrouded in darkness. Perhaps that’s for the best. We might not like what we’d see.
⚡️ Startup Stuff
🎯 High Potential Ideas to Work on
Are you thinking of starting a company or investing in early-stage startups? Here are some themes and ideas you don’t want to miss out on:
And here are the other ideas mentioned by Balaji:
VR as office replacement
AR for productivity (e.g., Google Glass Enterprise)
Self-hosting makes a comeback
Telemedicine, personal genomics, health tracking v2 (👋 lifetizr)
3D printing of metal
Pseudonymity, aided by crypto and AI voices & faces
Most of these themes were covered in The Long Game Podcast, with some of the best founders and thinkers working on these promising projects.
In 2021, I want to talk to more founders from Africa, as I believe Africa will play a major role in the 21st century. Let me know if you know people doing great things in Africa. 👇
📚 What I Read
1️⃣ One Billion Americans
This week, I enjoyed reading One Billion Americans: The Case for Thinking Bigger by Matthew Yglesias. The author explains why the US should aim at a population of one billion Americans to remain the world’s strongest power. In the first chapter, Yglesias shows that China will catch up relatively soon if nothing is done, leaving the US behind. Then, the author covers the classic objections to having a bigger population. For example, with one billion people, the US would have a population density of… France!
Here are some of the other topics the book covers:
Being big gives an edge on a global scale. For example, you can impose legislation on other countries.
People have fewer children than they’d like.
The problem with Workism (here’s a great article on Workism by Derek Thompson) and why reorienting the meaning of American lives from work to the family could be a solution.
Immigration: on the classic immigration debate where the left is for more immigration and the right is against, the author is for immigration, arguing that it will be needed to reach one billion Americans and more people bring more ideas.
On the housing scarcity, Yglesias explains that it’s caused by bad legislation that is, once again, subject to identity politics, where the single-family home is essential for the right and linked to racism (as a concept) for the left. It leads to legislations that are not pragmatic but only based on identity.
🥼 Are Experts Real?
The pandemic has shaken many things in our society. The place and legitimacy of experts are one of them. This article by Alvaro de Menard is great at explaining why the situation is very complex:
I vacillate between two modes: sometimes I think every scientific and professional field is genuinely complex, requiring years if not decades of specialization to truly understand even a small sliver of it, and the experts at the apex of these fields have deep insights about their subject matter. The evidence in favor of this view seems pretty good, a quick look at the technology, health, and wealth around us ought to convince anyone.
But sometimes one of these masters at the top of the mountain will say something so obviously incorrect, something even an amateur can see is false, that the only possible explanation is that they understand very little about their field. Sometimes vaguely smart generalists with some basic stats knowledge objectively outperform these experts. And if the masters at the top of the mountain aren't real, then that undermines the entire hierarchy of expertise.
🎙 Podcast Episode of the Week
Here are some great episodes I listened to recently:
🍭 Brain Food
The story of ‘Oumuamua is fascinating:
ʻOumuamua is a small object estimated to be between 100 and 1,000 metres (300 and 3,000 ft) long, with its width and thickness both estimated to range between 35 and 167 metres (115 and 548 ft). It has a dark red color, similar to objects in the outer Solar System. Despite its close approach to the Sun, ʻOumuamua showed no signs of having a coma, but did exhibit non‑gravitational acceleration.
What’s really fascinating is the discussion about what ‘Oumuamua truly is. On one side, scientists explain that it can only be the result of a natural phenomenon. On the other side, other scientists, led by Avi Loeb, believe it could be coming from another civilization.
For more on the mystery of ‘Oumuamua, I greatly enjoyed this long conversation.
👾 Just for Fun
🚷 What's a Rule That Was Implemented Somewhere That Massively Backfired?
We try to make the world legible, and we consider that humans are rational beings, but we’re not. This is particularly interesting in the context of legislation. New rules can often backfire, and this Reddit thread gives a lot of examples. Here are the best ones:
In 209 BC, 900 Chinese soldiers were marching to defend a city in Northern China. A flood stopped them halfway. Qin law dictated that the government would execute anyone that was "late" for government orders. Rather than face execution, the soldiers rebelled.
In Athens (late 80s), the government tried to limit pollution by having odd-numbered, and even-numbered license plates drive on alternating days. Result: everyone bought a second (low-cost and with worse emission) car as their backup. Streets stayed clogged, pollution worse.
One city had an issue with loud bikes, so they installed decibel readers as a deterrent. People started driving up to the machine and revved up their vehicles to see who could “win” by being the loudest. The city took the reader down.
🎥 What I’m Watching
📦 The Logistics of Amazon Deliveries
The logistics behind Amazon’s two-day delivery is truly incredible. Here’s a great video to understand how it works. In a few words: ‘it depends.’
🔧 The tool of the Week
There’s a lot of great informative content on Youtube, but for longer videos, it can be hard to find the time to listen to them. Huffduffer is great because it makes it possible to download the video's audio and makes it available on your podcast reader on your phone. This way, you can listen to it anywhere.
🪐 Quote I'm Pondering
Every society honors its live conformists and its dead troublemakers.
― Marshall McLuhan
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