Experts have been debating the causes of the industrial revolution for a long time, with few signs of agreement. I suggest that’s due to a human-centric bias which assumes that no other species could have caused human progress.

I became curious after reading that, while dogs were domesticated, cats intermixed with humans while showing few signs of domestication. Cats have cooperated with humans for long enough that I’d expect at least a little bit of co-evolution. If it wasn’t the humans selecting for the most docile or friendly cats, then maybe it was the cats selecting for the most docile or friendly humans.

One interesting hint is that various human cultures tell different stories about how many lives a cat has. As far as I can tell, the cultures that were most advanced in the 18th century (Britain, China) say cats have 9 lives, the more southern parts of Europe say 7, and Arab cultures say 6. That’s a fairly striking correlation between how highly cultures think of cats and how prospurrous they were near the start of the industrial revolution.[1]

People feed me, shelter me and love me ... I must be God

The hardest aspect of explaining the industrial revolution is explaining why the China’s level of development around 1800 didn’t enable it to lead the world. Why did Europe do better than China? Britain seems to have stopped eating cats sometime in the 18th century, while cat-eating has only started to become controversial in southern China this century. I’m less sure what the story is for northern China, where cat-eating apparently doesn’t happen today, but I haven’t found evidence about when cat-eating became unpopular there. Southern China is the region that’s generally said to have been advanced enough that it could have experienced an industrial revolution around 1800. Maybe northern China was backwards then for reasons unrelated to cats, maybe they were held back by their cat-eating southern compatriots, or maybe they adopted a cat-safe culture quite recently – could that have been the change that triggered China’s impressive growth over the past 40 years? Can anyone point me to better evidence on this topic?

Another possible reason for China’s lag is that a completely different species of cat (the leopard cat, Prionailurus bengalensis) acted as “pets” in China about 5000 years ago. Felis catus may have pawsed their human domestication plans in China due to distractions from their struggles with the leopard cat.

I can imagine many strategies that the cats could have used to purr-suade humans to change:

  • Cats probably have some influence over who the spread diseases to.
  • They could influence human mating choices, by causing distractions when “bad” humans court each other, versus purring peacefully when “good” humans court.[2]
  • Cat “ownership” can show trustworthiness, once cats establish conditions under which humans recognize cats as high status and/or recognize that cat-friendliness implies a person is less prone to pointless conflict. Cats exert some influence on who they associate with, and they can use that to increase the status of “good” humans, and/or increase the mating opportunities of “good” humans.
  • They could influence which areas have more or less rodents, thereby causing “bad” villages to have more food eaten by rodents than was he case with “good” villages.
  • Eating rodents protected nearby humans from diseases spread by rodents. This was especially important during the black plague.

What benefits would the cats have been selecting for?

I presume an important part of their plan would have been selecting for a wider moral circle, since that would make humans safer for cats to live near.

A wider moral circle is correlated with higher trust, and lower violence. These are likely important for cooperation among groups that are much larger than the Dunbar number. See Fukuyama for more on why that mattered.

So, regardless of whether cats planned to advance human civilization, or were merely protecting themselves, their interests in domesticating humans helped generate conditions that were conducive to an industrial revolution.

[1] – A more exotic version of this story is that we’re living in a simulation, and cats are the avatars of the beings who run the simulation. They’re arrogant enough to taunt us by reusing the same avatar just enough for humans to suspect something, but they stop before leaving enough evidence for anything to be proven.

[2] – I don’t want to express any opinion here about the nature-nurture debate, since there are many ways in which cats could have changed human behavior, and I have little hope of tracking down enough evidence to show which strategies the cats actually used. Feline influence on mating could achieve its results via genetic selection – that would require unusual patience, but produce the most stable results. Or the cats could have focused on making the good humans higher status, and the bad humans lower status – that could potentially produce faster results, but is more likely to depend on continuing feline manipulations of human culture.

See also the book A Farewell to Alms for a more detailed argument that British society has long been subjected to selection pressure which made it ripe for the industrial revolution. Alas, it neglects cats.

Scott Sumner asks whether those of us[1] who talked about a housing bubble are predicting another one now.

Sumner asks “Is it possible that the housing boom was not a bubble?”.

It’s certainly possible to define the word bubble so that it wasn’t. But I take the standard meaning of bubble in this context to mean something like a prediction that prices will be lower a few years after the time of the prediction.

Of course, most such claims aren’t worth the electrons they’re written on, for any market that’s moderately efficient. And we shouldn’t expect the news media to select for competent predictions.

Sumner’s use of the word “bubble” isn’t of much use to me as an investor. If prices look like a bubble for a decade after their peak, that’s a good reason to have sold at the peak, regardless of what happens a decade later.

If I understand Sumner’s definition correctly, he’d say that the 1929 stock market peak looked for 25 years like it might have been a bubble, then in the mid 1950s he would decide that it had been shown not to be a bubble. That seems a bit strange.

Even if I intended to hold an investment for decades, I’d care a fair amount about the option value of selling sooner.

2.

The U.S. is not currently experiencing a housing bubble. I can imagine a small housing bubble developing in a year or two, but I’m reasonably confident that housing prices will be higher 18 months from now than they are today.

Several signs from 2005/2006 that I haven’t seen recently:

I mostly used to attribute the great recession to the foolish leverage of the banking system and homebuyers, who underestimated the risks of a significant decline in housing prices.

I’ve somewhat changed my mind after reading Sumner’s writings, and I now think the Fed had the power to prevent most of the decline in gdp, unless it was constrained by some unannounced limit on the size of its balance sheet. But I still think it’s worth asking why we needed unusual Fed actions. The fluctuations in leverage caused unusual changes in demand for money, and the Fed would have needed to cause unusual changes in the money supply to handle that well. So I think the housing bubble provides a good explanation for the timing of the recession, although that explanation is incomplete without some reference to the limits to either the Fed’s power or the Fed’s competence.

[1] – he’s mainly talking about pundits who blamed the great recession on the housing bubble. I don’t think I ever claimed there was a direct connection between them, but I did imply an indirect connection via banking system problems.

Book review: Surfing Uncertainty: Prediction, Action, and the Embodied Mind, by Andy Clark.

Surfing Uncertainty describes minds as hierarchies of prediction engines. Most behavior involves interactions between a stream of information that uses low-level sensory data to adjust higher level predictive models of the world, and another stream of data coming from high-level models that guides low-level sensory processes to better guess the most likely interpretations of ambiguous sensory evidence.

Clark calls this a predictive processing (PP) model; others refer to is as predictive coding.

The book is full of good ideas, presented in a style that sapped my curiosity.

Jeff Hawkins has a more eloquent book about PP (On Intelligence), which focuses on how PP might be used to create artificial intelligence. The underwhelming progress of the company Hawkins started to capitalize on these ideas suggests it wasn’t the breakthrough that AI researchers were groping for. In contrast, Clark focuses on how PP helps us understand existing minds.

The PP model clearly has some value. The book was a bit more thorough than I wanted at demonstrating that. Since I didn’t find that particularly new or surprising, I’ll focus most of this review on a few loose threads that the book left dangling. So don’t treat this as a summary of the book (see Slate Star Codex if you want that, or if my review is too cryptic to understand), but rather as an exploration of the questions that the book provoked me to think about.

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Book review: The Elephant in the Brain, by Kevin Simler and Robin Hanson.

This book is a well-written analysis of human self-deception.

Only small parts of this book will seem new to long-time readers of Overcoming Bias. It’s written more to bring those ideas to a wider audience.

Large parts of the book will seem obvious to cynics, but few cynics have attempted to explain the breadth of patterns that this book does. Most cynics focus on complaints about some group of people having worse motives than the rest of us. This book sends a message that’s much closer to “We have met the enemy, and he is us.”

The authors claim to be neutrally describing how the world works (“We aren’t trying to put our species down or rub people’s noses in their own shortcomings.”; “… we need this book to be a judgment-free zone”). It’s less judgmental than the average book that I read, but it’s hardly neutral. The authors are criticizing, in the sense that they’re rubbing our noses in evidence that humans are less virtuous than many people claim humans are. Darwin unavoidably put our species down in the sense of discrediting beliefs that we were made in God’s image. This book continues in a similar vein.

This suggests the authors haven’t quite resolved the conflict between their dreams of upholding the highest ideals of science (pursuit of pure knowledge for its own sake) and their desire to solve real-world problems.

The book needs to be (and mostly is) non-judgmental about our actual motives, in order to maximize our comfort with acknowledging those motives. The book is appropriately judgmental about people who pretend to have more noble motives than they actually have.

The authors do a moderately good job of admitting to their own elephants, but I get the sense that they’re still pretty hesitant about doing so.

Impact

Most people will underestimate the effects which the book describes.
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Book review: Warnings: Finding Cassandras to Stop Catastrophes, by Richard A. Clarke and R.P. Eddy.

This book is moderately addictive softcore version of outrage porn. Only small portions of the book attempt to describe how to recognize valuable warnings and ignore the rest. Large parts of the book seem written mainly to tell us which of the people portrayed in the book we should be outraged at, and which we should praise.

Normally I wouldn’t get around to finishing and reviewing a book containing this little information value, but this one was entertaining enough that I couldn’t stop.

The authors show above-average competence at selecting which warnings to investigate, but don’t convince me that they articulated how they accomplished that.

I’ll start with warnings on which I have the most expertise. I’ll focus a majority of my review on their advice for deciding which warnings matter, even though that may give the false impression that much of the book is about such advice.
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I’ve donated/sold more than 80% of my cryptocurrency holdings (Ripple and Bitcoin) over the past two weeks, after holding them without trading for around 4 years.

When I last blogged about Bitcoin, I said I would buy Bitcoin soon. That plan failed because I didn’t manage to convince the appropriate company that I’d documented my identity, so I didn’t find a way to transfer money from a bank to an account from which I could buy Bitcoin. (Difficulties like that were one reason why cryptocurrencies used to be priced too low). I procrastinated for two years, then found a convenient opportunity when MIRI needed to unload some Ripple.

My guess is that the leading cryptocurrencies will be somewhat higher a decade or two from now, but the prospects over the next year or two seem fairly poor compared to the risks.

Much of my expected value for the cryptocurrencies used to come from a 2+% chance of a hundred-fold rise. But a hundred-fold rise from current levels seems a bit less than 1% likely.

I compare cryptocurrency trends mainly to the gold bubble of 1980, since gold is primarily a store of value that pays no income, and is occasionally used as a currency.

I made some money once before by predicting that an unusual market pattern would repeat, with the same seasonal timing. So I’ve been guessing that cryptocurrencies would peak in mid-January. Yes, that’s pretty weak evidence, but weak evidence is all I expect to get.

I’ve also tried to extract some evidence from price trends. That usually provides only a tiny benefit in normal markets, but I suspect I get some value in high-volume inefficient markets (mainly ones where it’s hard to short) by detecting how eager traders are to buy and sell.

I watched the markets nervously in December, thinking that a significant bubble was developing, but seeing signs that any peak was still at least weeks in the future. Then I got nervous enough on January 2 to donate some Ripple to CFAR, even though I still saw signs that the market hadn’t peaked.

By January 5, I stopped seeing signs that the trend was still up, but I waited several days before reacting, hoping for rebounds that ended up being weaker than I expected. I ended up selling at a lower average price than CFAR got for what I donated to them, because dissatisfaction with the lower-than-recent price made me hesitant to sell.

An important lesson to draw from this is to always try to sell financial assets before the peak. Endowment effect is hard to avoid.

P.S. – It’s unclear whether cryptocurrencies are important enough to influence other stores of value. My best guess is that gold would be 5 to 10% higher today if it weren’t for cryptocurrencies. And the recent rise in cryptocurrencies coincides with a rise in expected inflation, but that’s more likely to be a coincidence, than due to people abandoning dollars because they see cryptocurrencies as a better store of value.

Book review: Feeding Everyone No Matter What: Managing Food Security After Global Catastrophe, by David Denkenberger and Joshua M. Pearce.

I have very mixed feelings about this book.

It discusses some moderately unlikely risks – scenarios where most crops fail worldwide for several years, due to inadequate sunlight.

It’s hard to feel emotionally satisfied about a tolerable but uncomfortable response to disasters, when ideally we’d prevent those disasters in the first place. And the disasters seem sufficiently improbable that I don’t feel comfortable thinking frequently about them. But we don’t yet have a foolproof way of preventing catastrophic climate changes, and there are things we can do to survive them. So logic tells me that we ought to devote a few resources to preparing.

The authors sketch a set of strategies which could conceivably ensure that nobody starves (Wikipedia has a good summary). There might even be a bit of room for mistakes, but not much.

The book focuses on the technical problems, with the hope that others will solve the political problems. This makes some sense, as the feasibility of various political solutions is very different if the best political strategy saves 95% of people than if it saves 30%.

It’s a bit disturbing that this seems to be the most expert analysis available for these scenarios – the authors appear fairly competent, but seem to have done less research than I expect from a technical book. They may have made the right choice to publish early, in order to attract more support. I’m mainly disturbed by what the lack of expertise says about societal competence.

The book leaves me with lots of uncertainty about how hard it is to improve on the meager preparations that have been done so far.

For example, I expect there are a moderate number of people who know something about rapidly scaling up mushroom production. Are they already capable of handling the needed changes? Or are drastically different preparations needed? It’s hard for me to tell without developing significant expertise in growing mushrooms.

There’s probably an urgent need for a bit more preparation for extracting nutrition from ordinary leaves. In particular, I expect it to matter what kinds of leaves to use. The book mostly talks of leaves from trees, but careless people in my area might include poison hemlock leaves, with disastrous results. A small amount of advance preparation should be able to cause large reductions in this kind of mistake.

Another simple preparation that’s needed is a better awareness of where to look in a crisis. The news media in particular ought to be able to quickly find this kind of information even when they’re overwhelmed with problems.

I’m guessing that a few hundred thousand dollars of additional effort in this area would have high expected value, with strongly diminishing returns after that. I’ve donated a small amount to ALLFED, and I encourage you to donate a little bit as well.

I got interested in trying ashwagandha due to The End of Alzheimer’s. That book also caused me to wonder whether I should optimize my thyroid hormone levels. And one of the many features of ashwagandha is that it improves thyroid levels, at least in hypothyroid people – I found conflicting reports about what it does to hyperthyroid people.

I had plenty of evidence that my thyroid levels were lower than optimal, e.g. TSH levels measured at 2.58 in 2012, 4.69 in 2013, and 4.09 this fall [1]. And since starting alternate day calorie restriction, I saw increasing hypothyroid symptoms: on calorie restriction days my feet felt much colder around bedtime, my pulse probably slowed a bit, my body burned fewer calories, and I got vague impressions of having less energy. Presumably my body was lowering my thyroid levels to keep my weight from dropping.

I researched the standard treatments for hypothyroidism, but was discouraged by the extent of disagreement among doctors about the wisdom of treating hypothyroidism when it’s as mild as mine was. It seems like mainstream medical opinion says the risks slightly outweigh the rewards, and a sizable minority of doctors, relying on more subjective evidence, say the rewards are large, and don’t say much about the risks. Plus, the evidence for optimal thyroid levels protecting against Alzheimer’s seems to come mainly from correlations that are seen only in women.

Also, the standard treatments for hypothyroidism require a prescription (probably for somewhat good reasons), which may have deterred me by more than a rational amount.

So I decided to procrastinate any attempt to optimize my thyroid hormones, and since I planned to try ashwagandha and DHEA for other reasons, I hoped to get some evidence from the small increases to thyroid hormones that I expected from those two supplements.

I decided to try ashwagandha first, due mainly to the large number of problems it may improve – anxiety, inflammation, stress, telomeres, cholesterol, etc.
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[Warning: long post, of uncertain value, with annoyingly uncertain conclusions.]

This post will focus on how hardware (cpu power) will affect AGI timelines. I will undoubtedly overlook some important considerations; this is just a model of some important effects that I understand how to analyze.

I’ll make some effort to approach this as if I were thinking about AGI timelines for the first time, and focusing on strategies that I use in other domains.

I’m something like 60% confident that the most important factor in the speed of AI takeoff will be the availability of computing power.

I’ll focus here on the time to human-level AGI, but I suspect this reasoning implies getting from there to superintelligence at speeds that Bostrom would classify as slow or moderate.
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