I’ve moved to Berkeley, partly to be closer to the Center for Applied Rationality and interesting people who are associated with it.
Also, please stop using my old rahul.net email address, and use Peter.C.McCluskey@gmail.com instead.
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Book review: The Origins of Political Order: From Prehuman Times to the French Revolution, by Francis Fukuyama.
This ambitious attempt to explain the rise of civilization (especially the rule of law) is partly successful.
The most important idea in the book is that the Catholic church (in the Gregorian Reforms) played a critical role in creating important institutions.
The church differed from religions in other cultures in that it was sufficiently organized to influence political policy, but not strong enough to become a state. This lead it to acquire resources by creating rules that enabled people to leave property to the church (often via wills, which hardly existed before then). This turned what had been resources belonging to some abstract group (families or ancestors) into things owned by individuals, and created rules for transferring those resources.
In the process, it also weakened the extended family, which was essential to having a state that impartially promoted the welfare of a society that was larger than a family.
He also provides a moderately good description of China’s earlier partial adoption of something similar in its merit-selected bureaucracy.
I recommend reading the first 7 chapters plus chapter 16. The rest of the book contains more ordinary history, including some not-too-convincing explanations of why northwest Europe did better than the rest of Christianity.
There was a large shift in our ancestors diet about 3.5 million years ago to food derived from grasses and/or sedges. This has potentially important implications for what diet we’re adapted to. Unfortunately, the evidence isn’t specific enough to be very useful:
The isotope method cannot distinguish what parts of grasses and sedges human ancestors ate – leaves, stems, seeds and-or underground storage organs such as roots or rhizomes. The method also can’t determine when human ancestors began getting much of their grass by eating grass-eating insects or meat from grazing animals.
Book review: Radical Abundance: How a Revolution in Nanotechnology Will Change Civilization, by K. Eric Drexler.
Radical Abundance is more cautious than his prior books, and targeted at a very nontechnical audience. It accurately describes many likely ways in which technology will create orders of magnitude more material wealth.
Much of it repackages old ideas, and it focuses too much on the history of nanotechnology.
He defines the subject of the book to be atomically precise manufacturing (APM), and doesn’t consider nanobots to have much relevance to the book.
One new idea that I liked is that rare elements will become unimportant to manufacturing. In particular, solar energy can be made entirely out of relatively common elements (unlike current photovoltaics). Alas, he doesn’t provide enough detail for me to figure out how confident I should be about that.
He predicts that progress toward APM will accelerate someday, but doesn’t provide convincing arguments. I don’t recall him pointing out the likelihood that investment in APM companies will increase dramatically when VCs realize that a few years of effort will produce commercial products.
He doesn’t do a good jobs of documenting his claims that APM has advanced far. I’m pretty sure that the million atom DNA scaffolds he mentions have as much programmable complexity as he hints, but if I only relied on this book to analyze that I’d suspect that those structures were simpler and filled with redundancy.
He wants us to believe that APM will largely eliminate pollution, and that waste heat will “have little adverse impact”. I’m disappointed that he doesn’t quantify the global impact of increasing waste heat. Why does he seem to disagree with Rob Freitas about this?
Book review: The Motivation Hacker, by Nick Winter.
This is a productivity book that might improve some peoples’ motivation.
It provides an entertaining summary (with clear examples) of how to use tools such as precommitment to accomplish an absurd number of goals.
But it mostly fails at explaining how to feel enthusiastic about doing so.
The section on Goal Picking Exercises exemplifies the problems I have with the book. The most realistic sounding exercise had me rank a bunch of goals by how much the goal excites me times the probability of success divided by the time required. I found that the variations in the last two terms overwhelmed the excitement term, leaving me with the advice that I should focus on the least exciting goals. (Modest changes to the arbitrary scale of excitement might change that conclusion).
Which leaves me wondering whether I should focus on goals that I’m likely to achieve soon but which I have trouble caring about, or whether I should focus on longer term goals such as mind uploading (where I might spend years on subgoals which turn out to be mistaken).
The author doesn’t seem to have gotten enough out of his experience to motivate me to imitate the way he picks goals.
Automated market-making software agents have been used in many prediction markets to deal with problems of low liquidity.
The simplest versions provide a fixed amount of liquidity. This either causes excessive liquidity when trading starts, or too little later.
For instance, in the first year that I participated in the Good Judgment Project, the market maker provided enough liquidity that there was lots of money to be made pushing the market maker price from its initial setting in a somewhat obvious direction toward the market consensus. That meant much of the reward provided by the market maker went to low-value information.
The next year, the market maker provided less liquidity, so the prices moved more readily to a crude estimate of the traders’ beliefs. But then there wasn’t enough liquidity for traders to have an incentive to refine that estimate.
One suggested improvement is to have liquidity increase with increasing trading volume.
I present some sample Python code below (inspired by equation 18.44 in E.T. Jaynes’ Probability Theory) which uses the prices at which traders have traded against the market maker to generate probability-like estimates of how likely a price is to reflect the current consensus of traders.
This works more like human market makers, in that it provides the most liquidity near prices where there’s been the most trading. If the market settles near one price, liquidity rises. When the market is not trading near prices of prior trades (due to lack of trading or news that causes a significant price change), liquidity is low and prices can change more easily.
I assume that the possible prices a market maker can trade at are integers from 1 through 99 (percent).
When traders are pushing the price in one direction, this is taken as evidence that increases the weight assigned to the most recent price and all others farther in that direction. When traders reverse the direction, that is taken as evidence that increases the weight of the two most recent trade prices.
The resulting weights (p_px in the code) are fractions which should be multiplied by the maximum number of contracts the market maker is willing to offer when liquidity ought to be highest (one weight for each price at which the market maker might position itself (yes there will actually be two prices; maybe two weight ought to be averaged)).
There is still room for improvement in this approach, such as giving less weight to old trades after the market acts like it has responded to news. But implementers should test simple improvements before worrying about finding the optimal rules.
trades = [(1, 51), (1, 52), (1, 53), (-1, 52), (1, 53), (-1, 52), (1, 53), (-1, 52), (1, 53), (-1, 52),]
p_px = {}
num_agree = {}
probability_list = range(1, 100)
num_probabilities = len(probability_list)
for i in probability_list:
p_px[i] = 1.0/num_probabilities
num_agree[i] = 0
num_trades = 0
last_trade = 0
for (buy, price) in trades: # test on a set of made-up trades
num_trades += 1
for i in probability_list:
if last_trade * buy < 0: # change of direction
if buy < 0 and (i == price or i == price+1):
num_agree[i] += 1
if buy > 0 and (i == price or i == price-1):
num_agree[i] += 1
else:
if buy < 0 and i <= price:
num_agree[i] += 1
if buy > 0 and i >= price:
num_agree[i] += 1
p_px[i] = (num_agree[i] + 1.0)/(num_trades + num_probabilities)
last_trade = buy
for i in probability_list:
print i, num_agree[i], '%.3f' % p_px[i]
Book review: Paleofantasy: What Evolution Really Tells Us about Sex, Diet, and How We Live, by Marlene Zuk
This book refutes some myths about what would happen if we adopted the lifestyle of some imaginary hunter-gather ancestor who some imagine was perfectly adapted to his environment.
I’m a bit disappointed that it isn’t as provocative as the hype around it suggested. It mostly just points out that there’s no single environment that we’re adapted to, plus uncertainty about what our ancestors’ lifestyle was.
She spends a good deal of the book demonstrating what ought to be the well-known fact that we’re still evolving and have partly adapted to an agricultural lifestyle. A more surprising point is that we still have problems stemming from not yet having fully evolved to be land animals rather than fish (e.g. hiccups).
She provides a reference to a study disputing the widely held belief that the transition from hunter-gatherer to farmer made people less healthy.
She cites evidence that humans haven’t evolved much adaptation to specific diets, and do about equally well on a wide variety of diets involving wild foods, so that looking at plant to animal ratios in hunter-gather diets isn’t useful.
Her practical lifestyle advice is mostly consistent with an informed guess about how we can imitate our ancestors’ lifestyle (e.g. eat less processed food), and mainly serves to counteract some of the overconfident claims of the less thoughtful paleo lifestyle promoters.
In his talk last week, Robin Hanson mentioned an apparently suboptimal level of charitable donations to for-profit companies.
My impression is that some of the money raised on Kickstarter and Indiegogo is motivated by charity.
Venture capitalists occasionally bias their investments towards more “worthy” causes.
I wonder whether there’s also some charitable component to people accepting lower salaries in order to work at jobs that sound like they produce positive externalities.
Charity for profitable companies isn’t likely to become a popular concept anytime soon, but that doesn’t keep subsets of it from becoming acceptable if framed differently.
I tried O2Amp glasses to correct for my colorblindness. They’re very effective at enabling me to notice some shades of red that I’ve found hard to see. In particular, two species of wildflowers (Indian Paintbrush and Cardinal Larkspur) look bright orange through the glasses, whereas without the glasses my vision usually fills in their color by guessing it’s similar to the surrounding colors unless I look very close.
But this comes at the cost of having green look much duller. The net effect causes vegetation to be less scenic.
The glasses are supposed to have some benefits for observing emotions via better recognition of blood concentration and oxygenation near the skin. But this effect seems too small to help me.
O2Amp is a small step toward enhanced sensory processing that is likely to become valuable someday, but for now it seems mainly valuable for a few special medical uses.
Book review: Why Nations Fail: The Origins of Power, Prosperity, and Poverty, by Daron Acemoglu and James Robinson.
This book claims that “extractive institutions” prevent nations from becoming wealthy, and “inclusive institutions” favor wealth creation. It is full of anecdotes that occasionally have some relevance to their thesis. (The footnotes hint that they’ve written something more rigorous elsewhere).
The stereotypical extractive institutions certainly do harm that the stereotypical inclusive institutions don’t. But they describe those concepts in ways that do a mediocre job of generalizing to non-stereotypical governments.
They define “extractive institutions” broadly to include regions that don’t have “sufficiently centralized and pluralistic” political institutions. That enables them to classify regions such as Somalia as extractive without having to identify anything that would fit the normal meaning of extractive.
Their description of Somalia as having an “almost constant state of warfare” is strange. Their only attempt to quantify this warfare is a reference to a 1955 incident where 74 people were killed (if that’s a memorable incident, it would suggest war kills few people there; do they ignore the early 90’s because it was an aberration?). Wikipedia lists Somalia’s most recently reported homicide rate as 1.5 per 100,000 (compare to 14.5 for their favorite African nation Botswana, and 4.2 for the U.S.).
They don’t discuss the success of Dubai and Hong Kong because those governments don’t come very close to fitting their stereotype of a pluralistic and centralized nation.
They describe Mao’s China as “highly extractive”, but it looks to me more like ignorant destruction than an attempt at extracting anything. They say China’s current growth is unsustainable, somewhat like the Soviet Union (but they hedge and say it might succeed by becoming inclusive as South Korea did). Whereas I predict that China’s relatively decentralized planning will be enough to sustain modest growth, but it will be held back somewhat by the limits to the rule of law.
They do a good (but hardly novel) job of explaining why elites often fear that increased prosperity would threaten their position.
They correctly criticize some weak alternative explanations of poverty such as laziness. But they say little about explanations that partly overlap with theirs, such as Fukuyama’s Trust (a bit odd given that the book contains a blurb from Fukuyama). Fukuyama doesn’t seem to discuss Africa much, but the effects of slave trade seem to have large long-lasting consequences on social capital.
For a good introduction to some more thoughtful explanations of national growth such as the rule of law and the scientific method, see William Bernstein’s The Birth of Plenty.
Why Nations Fail may be useful for correcting myths among people who are averse to math, but for people who are already familiar with this subject, it will just add a few anecdotes without adding much insight.