Book review: Synthetic Worlds: The Business and Culture of Online Games by Edward Castronova
Castranova is one of the first intellectuals to notice the importance of new societies that are being created in cyberspace. Much of this book is devoted to (sometimes redundant) explanations of why they are more than just games.
Around the middle of the book, he switches from describing a typical world for the benefit of those who doubt the importance of virtual worlds to describing how to design good worlds. This is where I started to find the book interesting and the questions thought-provoking, but the answers often unconvincing.
His most important discussion is about the near-anarchy that prevails in most virtual societies. He attributes this partly to the “Customer Service State” of for-profit world builders who are too cheap to pay for as much government as he assumes citizens want. But he seems to believe this is too inevitable to be worth much analysis. His more interesting question is why don’t the world’s citizens organize a government of their own? His answer is that citizens don’t have enough power over each other to enforce laws they might create. But he doesn’t convince me this is true (are boycotts useless? is repeatedly killing an outlaw not punishment?), nor does he explain why the designer face little pressure to change the design of the world to make it easier to enforce laws (what would happen if the world were designed to enable one person to effectively banish a person she doesn’t like from her view of the world?). I suspect part of the answer is that there’s less demand for government than he expects. I see some hints that his desire for government in cyberspace is a simple reflection of his desire for government in the real world. Yet I’d expect the analysis of whether government is desirable to be nontrivially affected by such differences as whether poverty and death cause much harm.
He claims “A fun economy should have property, theft, and jail too”, but only gives a few cryptic hints about what theft and jail add to an economy.
He claims “there should be no goods which never depreciate”, and partly justifies that by pointing to some benefits of a continuing need to produce new goods, but leaves me wondering why the rule should be universal or even close to universal.
He hints at the desirability of creating p2p virtual societies so that control over them can be decentralized instead of being determined by a corporate owner, but I’m disappointed that he fails to analyze whether this is practical.
One insight I liked was this description of how to deal with the desire for everyone to have high status: “How do you make a world in which everyone is in the top 10 percent? The answer: AI.”
He has a disturbing idea about the military uses of virtual worlds – an aggressor need not be hampered by unfamiliarity with the land he’s invading if he has unlimited ability to practice the invasion in simulation.
He has some ideas about how virtual worlds might help deal with threats such as grey goo, but doesn’t develop them as well as I would like. His ideas on using virtual worlds to make AIs more friendly appear to anthropomorphise AI in a rather naive and dangerous manner.
Book Review: Knowledge and the Wealth Of Nations: A Story of Economic Discovery by David Warsh
This book is an entertaining (but sometimes long-winded) history of economic thought that focuses on the role of technological knowledge, showing how sporadic attempts starting with Adam Smith to incorporate it into the mainstream of economic thought kept getting marginalized until a paper by Paul Romer in 1990 finally appears to have convinced the profession to include it in their models as a nonrival, partly excludable good.
Warsh writes in a style intended to be appropriate for laymen, but I find this rather frustrating, as it leaves out a fair amount of technical detail that I would like to understand, but probably fails to satisfy laymen since the subject of the book will only seem important to readers who already have enough familiarity with economics to handle a more technical discussion.
I liked an analogy that the book reports of the history of maps of Africa, where improved standards of accuracy sometimes caused mapmakers to produce less informative maps as they removed unverified reports of features from interior parts of Africa well before they were able to replace them with something more reliable. The book shows how similar processes in economic models have resulted in similar blank spots in economic thought.
He claims that Romer’s theory amounts to an argument against free markets and in favor of some poorly specified state management of some aspects of the economy. But I saw no analysis to support that conclusion. All I see are arguments that classical economic theory is too simplistic, that we probably need to study lots of messy empirical evidence before deciding what Romer’s theory says about state action.
His analysis of the Microsoft antitrust case provides a better argument than I’d previously heard for breaking up Microsoft into an OS company and an Apps company, but still leaves me wondering why it would make much difference – most of the causes of Microsoft’s OS monopoly power would remain unchanged. His claim (apparently reporting Romer’s remarks) that Microsoft solved the double marginalization problem in a way that a breakup wouldn’t alter seems confused. He is right to point out that those pricing effects weren’t the main issue, although he doesn’t seem to understand why (see Lessig’s The Future of Ideas for a good explanation of how monopolies stifle innovation).
He has a chapter titled “How the Dismal Science Got Its Name” which says nothing about the actual origin of that term (which was coined by a racist who hated Mill’s belief that blacks could be productive without being slaves).
Book Review: Happiness: The Science behind Your Smile by Daniel Nettle
This book provides a fairly good, but not very novel, description of what does and does not influence happiness, the problems with measuring it, and some bits of evolutionary theory that hint at why it is hard to achieve lasting increases in happiness.
The claim I found most important is that “If you control for social class, there is almost no relationship between income and life satisfaction.” This seems to have important implications for what kind of social equality we ought to be encouraging. I’m disappointed that he doesn’t say enough about this for me to determine how robust this conclusion is to the way it’s measured.
I’m disappointed that he ends with some misleading arguments for an alarming trend of increased distress among the least happy. He reports that suicides have increased among the young in recent decades, but fails to note that overall suicide rates in the U.S. have declined over that period. He claims “People are as hard as they ever have”, but cites no references for that, and Robert Fogel has reported research that reached the opposite conclusion in The Escape from Hunger and Premature Death.
Book review: No Two Alike: Human Nature and Human Individuality by Judith Rich Harris
This book provides a clear theory of what causes the personality differences between people that can’t be explained by genetic differences. She focuses a fair amount on identical twins, because the evidence that their environmentally caused personality differences are the same as ordinary siblings, and the same whether they’re reared together or apart, rules out many tempting theories.
Amazon reviewer Sioran points out an inconsistency – she claims early on that random chance can’t explain all of the variation, but her explanation ends up amounts to saying the causes are ultimately random. I find her early arguments against randomness unconvincing. And her explanation’s reliance on randomness doesn’t imply that her explanation is useless – she rules out most kinds of randomness as a cause, narrowing down the class of random causes to those which affect the person’s view of her status in society (e.g. differences in who outside family the person interacts with, and physical differences such as being tall due to better nutrition).
The most surprising prediction she makes is that mindblind (i.e. most) animals won’t have persistent personality differences that can’t be explained by genetic differences. I’m unsure whether to believe this – it seems that animals should only need to remember differences in how others treat them (rather than have a theory of mind) in order to produce the results we see. She would probably predict that autistic people have no persistent environmentally caused personality differences, but she isn’t clear about that (it may depend on the degree of autism).
One interesting result that she mentions is that autistic children are unable to use the fusiform face area (which in most people is specialized to do good face recognition), and instead seem to recognize faces the same way they recognize ordinary objects. I’m wondering how much this explains about why autism impairs many parts of the mind that deal with relationships.
I’m annoyed by how many pages she spends recounting the reaction to her prior book (The Nurture Assumption, a better book than this). If you’ve read that, most of the first half of this book will be a waste of time.
One interesting piece of evidence she mentions is this paper from the Journal of Political Economy which says that one’s height as a teenager is a better predictor of wages as an adult than adult height.
One small quibble: she says being a firstborn is unimportant (often not even known) outside the home in “contemporary societies — at least those not ruled by monarchies”. Korean society appears to be a clear exception to that claim.
At the recent AGI workshop, Michael Anissimov concisely summarized one of the reasons to worry about AI: the greatest risk is that there won’t be small risks leading up to it.
Book review: Expert Political Judgment: How Good Is It? How Can We Know? by Philip E. Tetlock
This book is a rather dry description of good research into the forecasting abilities of people who are regarded as political experts. It is unusually fair and unbiased.
His most important finding about what distinguishes the worst from the not-so-bad is that those on the hedgehog end of Isaiah Berlin’s spectrum (who derive predictions from a single grand vision) are wrong more often than those near the fox end (who use many different ideas). He convinced me that that finding is approximately right, but leaves me with questions.
Does the correlation persist at the fox end of the spectrum, or do the most fox-like subjects show some diminished accuracy?
How do we reconcile his evidence that humans with more complex thinking do better than simplistic humans, but simple autoregressive models beat all humans? That seems to suggest there’s something imperfect in using the hedgehog-fox spectrum. Maybe a better spectrum would use evidence on how much data influences their worldviews?
Another interesting finding is that optimists tend to be more accurate than pessimists. I’d like to know how broad a set of domains this applies to. It certainly doesn’t apply to predicting software shipment dates. Does it apply mainly to domains where experts depend on media attention?
To what extent can different ways of selecting experts change the results? Tetlock probably chose subjects that resemble those who most people regard as experts, but there must be ways of selecting experts which produce better forecasts. It seems unlikely they can match prediction markets, but there are situations where we probably can’t avoid relying on experts.
He doesn’t document his results as thoroughly as I would like (even though he’s thorough enough to be tedious in places):
I can’t find his definition of extremists. Is it those who predict the most change from the status quo? Or the farthest from the average forecast?
His description of how he measured the hedgehog-fox spectrum has a good deal of quantitative evidence, but not quite enough for me check where I would be on that spectrum.
How does he produce a numerical timeseries for his autoregressive models? It’s not hard to guess for inflation, but for the end of apartheid I’m rather uncertain.
Here’s one quote that says a lot about his results:
Beyond a stark minimum, subject matter expertise in world politics translates less into forecasting accuracy than it does into overconfidence
Book review: Evolution’s Rainbow: Diversity, Gender, and Sexuality in Nature and People by Joan Roughgarden
This book provides some good descriptions of sexual and gender diversity in nature and in a variety of human cultures, and makes a number of valid criticisms of biases against diversity in the scientific community and in society at large.
Many of her attempts to criticize sexual selection theory are plausible criticisms of beliefs that don’t have much connection to sexual selection theory (e.g. the belief that all sexually reproducing organisms fall into one of two gender stereotypes).
Her more direct attacks on the theory amount to claiming that “almost all diversity is good” and ignoring the arguments of sexual selection theorists who describe traits that appear to indicate reduced evolutionary fitness (see Geoffrey Miller’s book The Mating Mind). She practically defines genetic defects out of existence. She tries to imply that biologists agree on her criteria for a “genetic defect”, but her criteria require that a “trait be deleterious under all conditions” (I suspect most biologists would say “average” instead of “all”), and that it reduce fitness by at least 5 percent.
Her “alternative” theory, social selection, may have some value as a supplement to sexual selection theory, but I see no sign that it explains enough to replace sexual selection theory.
She sometimes talks as if she were trying to explain the evolution of homosexuality, but when doing so she is referring to bisexuality, and doesn’t attempt to explain why an animal would be exclusively homosexual.
Her obsession with discrediting sexual selection comes from an exaggerated fear that the theory implies that most diversity is bad. This misrepresents sexual selection theory (which only says that some diversity represents a mix of traits with different fitnesses). It’s also a symptom of her desire to treat natural as almost a synonym for good (she seems willing to hate diversity if it’s created via genetic engineering).
She tries to imply that a number of traits (e.g. transsexualism) are more common than would be the case if they significantly reduced reproductive fitness, but her reasoning seems to depend on the assumption that those traits can only be caused by one possible mutation. But if there are multiple places in the genome where a mutation could produce the same trait, there’s no obvious limit to how common a low-fitness trait could be.
Her policy recommendations are of very mixed quality. She wants the FDA to regulate surgical and behavioral therapies the way it regulates drugs, and claims that would stop doctors from “curing” nondiseases such as gender dysphoria. But she doesn’t explain why she expects the FDA to be more tolerant of diversity than doctors. Instead, why not let the patient decide as much as possible whether to consider something a disease?
This book is a colorful explanation of why we are less successful at finding happiness than we expect. It shows many similarities between mistakes we make in foreseeing how happy we will be and mistakes we make in perceiving the present or remembering the past. That makes it easy to see that those errors are natural results of shortcuts our minds take to minimize the amount of data that our imagination needs to process (e.g. filling in our imagination with guesses as our mind does with the blind spot in our eye).
One of the most important types of biases is what he calls presentism (a term he borrows from historians and extends to deal with forecasting). When we imagine the past or future, our minds often employ mental mechanisms that were originally adapted to perceive the present, and we retain biases to give more weight to immediate perceptions than to what we imagine. That leads to mistakes such as letting our opinions of how much food we should buy be overly influenced by how hungry we are now, or Wilbur Wright’s claim in 1901 that “Man will not fly for 50 years.”
This is more than just a book about happiness. It gives me a broad understanding of human biases that I hope to apply to other areas (e.g. it has given me some clues about how I might improve my approach to stock market speculation).
But it’s more likely that the book’s style will make you happy than that the knowledge in it will cause you to use the best evidence available (i.e. observations of what makes others happy) when choosing actions to make yourself happy. Instead, you will probably continue to overestimate your ability to predict what will make you happy and overestimate the uniqueness that you think makes the experience of others irrelevant to your own pursuit of happiness.
I highly recommend the book.
Some drawbacks:
His analysis of memetic pressures that cause false beliefs about happiness to propagate is unconvincing. He seems to want a very simple theory, but I doubt the result is powerful enough to explain the extent of the myths. A full explanation would probably require the same kind of detailed analysis of biases that the rest of the book contains.
He leaves the impression that he thinks he’s explained most of the problems with achieving happiness, when he probably hasn’t done that (it’s unlikely any single book could).
He presents lots of experimental results, but he doesn’t present the kind of evidence needed to prove that presentism is a consistent problem across a wide range of domains.
He fails to indicate how well he follows his own advice. For instance, does he have any evidence that writing a book like this makes the author happy?
While browsing through charts of various stocks, I came across a company (Manchester Inc., symbol MNCS) with a chart that’s unusual enough that I had to check around to reassure myself that my primary source for stock market prices wasn’t playing tricks on me.
It has a history of unusually steady increases with few signs of the randomness that I normally see in stock prices. If you had bought at the closing price any day this year and held for ten trading days, it would have closed higher than your purchase price (your average gain would have been over 3 percent), and it was almost as predictable the prior year.
A paragraph in the middle of this Forbes story explains why its market value looks strange.
The only guess I have as to what might cause this is an unusual form of manipulation where the manipulators produce this phenomenon until traders who buy purely on price trends provide enough liquidity for the manipulators to cash out. But even that is pretty implausible – if that’s what’s happening, why wouldn’t they create a bit more day to day randomness to disguise it a bit? And how could they afford to risk as large an investment as I suspect that would take on an approach which seems different enough from anything tried before that it ought to be hard to predict whether it will work?
Book review: The Undercover Economist: Exposing Why the Rich Are Rich, the Poor Are Poor–and Why You Can Never Buy a Decent Used Car! by Tim Harford
This book does an excellent job of describing economics in a way that laymen can understand, although experts won’t find much that is new in it.
Harford’s description of price discrimination is the best I’ve seen, and the first to describe how to tell the extent to which an instance of price discrimination has good effects (the extent to which it expands the number of sales).
His arguments that globalization reduces pollution are impressive for most types of pollution, but for carbon dioxide emissions I’m very disappointed. He hopes that energy use has peaked in the richest countries because he’s failed to imagine what will cause enough increased demand to offset increases in efficiency. For those of modest imagination, I suggest thinking about more realistic virtual reality (I want my Holodeck), personal robots, and increased air conditioning due to people moving to bigger houses in warmer climates. For those with more imagination, add in spacecraft and utility fog.
Some small complaints:
He refers to Howard Schultz as the owner of Starbucks, but he only owns about 2 percent of Starbucks’ stock.
His comment that Amazon stock price dropped below its IPO price fails to adjust for stock splits – a share bought at $18 in 1997 would have become 12 shares worth $8 each in the summer of 2001.
His claim that “Google is the living proof that moving first counts for nothing on the Internet” is a big exaggeration. It’s quite possible that Google success was primarily due to being the first to reach some key threshold of quality, and that many small competitors have matched its quality without taking measurable business away from it.