Peter tosses a coin and continues to do so until it should land heads when it comes to the ground. He agrees to give Paul one ducat if he gets heads on the very first throw, two ducats if he gets it on the second, four if on the third, eight if on the fourth, and so on, so that with each additional throw the number of ducats he must pay is doubled.

Nicholas Bernoulli … suggested that more than five tosses of heads are [seen as] morally impossible [and so ignored]. This proposition is experimentally tested through the elicitation of subjects‘ willingness-to-pay for various truncated versions of the Petersburg gamble that differ in the maximum payoff. … All gambles that involved probability levels smaller than 1/16 and maximum payoffs greater than 16 Euro elicited the same distribution of valuations. … The payoffs were as described …. but in Euros rather than in ducats. … The more senior students seemed to have a higher willingness-to-pay. … Offers increase significantly with income. (more)

This isn’t plausibly explained by risk aversion, nor by a general neglect of possibilities with a <5% chance. I suspect this is more about analysis complexity, i.e., about limiting the number of possibilities we’ll consider at any one time. I also suspect this bodes ill for existential risk mitigation.

After several iterations this better person might have a much better betterness theory. Then he might quickly make everything around him much better. Not just better looking hair, better jokes, or better sleep. He might start a better business, and get better at getting investors to invest, customers to buy, and employees to work. Or he might focus on making better investments. Or he might run for office and get better at getting elected, and then make his city or nation run better. Or he might create a better weapon, revolution, or army, to conquer any who oppose him.

Via such a “betterness explosion,” one way or another this better person might, if so inclined, soon own, rule, or conquer the world. Which seems to make it very important that the first person who discovers the first good theory of betterness be a very nice generous person who will treat the rest of us well. Right?

OK, this might sound silly. After all, we seem to have little reason to expect there is a useful grand unified theory of betterness to discover, beyond what we already know. “Betterness” seems mostly a concept about us and what we want – why should it correspond to something out there about which we can make powerful discoveries?

But a bunch of smart well-meaning folks actually do worry about a scenario that seems pretty close to this one. Except they talk about “intelligence” instead of “betterness.” They imagine an “intelligence explosion,” by which they don’t just mean that eventually the future world and many of its creatures will be more mentally capable than us in many ways, or even that the rate at which the world makes itself more mentally capable will speed up, similar to how growth rates have sped up over the long sweep of history. No, these smart well-meaning folks instead imagine that once someone has a powerful theory of “intelligence,” that person could create a particular “intelligent” creature which is good at making itself more “intelligent,” which then lets that creature get more “intelligent” about making itself “intelligent.” Within a few days or weeks, the story goes, this one creature could get so “intelligent” that it could do pretty much anything, including taking over the world.

I put the word “intelligence” in quotes to emphasize that the way these folks use this concept, it pretty much just means “betterness.” (Well, mental betterness, but most of the betterness we care about is mental.) And this fits well with common usage of the term “intelligence.” When we talk about machines or people or companies or even nations being “intelligent,” we mainly mean that such things are broadly mentally or computationally capable, in ways that are important for their tasks and goals. That is, an “intelligent” thing has a great many useful capabilities, not some particular specific capability called “intelligence.” To make something broadly smarter, you have to improve a wide range of its capabilities. And there is generally no easy or fast way to do that.

Now if you artificially hobble something so as to simultaneously reduce many of its capacities, then when you take away that limitation you may simultaneously improve a great many of its capabilities. For example, if you drug a person so that they can hardly think, then getting rid of that drug can suddenly improve a great many of their mental abilities. But beyond removing artificial restrictions, it is very hard to simultaneously improve many diverse capacities. Theories that help you improve capabilities are usually focused on a relatively narrow range of abilities – very general and useful theories are quite rare.

All of which is to say that fearing that a new grand unified theory of intelligence will let one machine suddenly take over the world isn’t that different from fearing that a grand unified theory of betterness will let one better person suddenly take over the world. This isn’t to say that such an thing is impossible, but rather that we’d sure want some clearer indications that such a theory even exists before taking such a fear especially seriously.

Individuals in new marriages were interviewed separately about their ongoing stressful experiences, and their own appraisals of those experiences were compared with those of the interviewers. … Spouses’ tendencies to form positively biased appraisals of their stressful experiences predicted fewer depressive symptoms over the subsequent 4 years among individuals judged to be facing relatively mild experiences but more depressive symptoms among individuals judged to be facing relatively severe experiences. … These effects were mediated by changes in those experiences, such that the interaction between the tendency to form positively biased appraisals of stressful experiences and the objectively rated severity of initial levels of those experiences directly predicted changes in those experiences, which in turn accounted for changes in depressive symptoms. (more)

Truth should also be especially important for situations that are novel relative to our evolved intuitions. The more our current situation differs from situations where our ancestors evolved (genetically or culturally) their intuitions about when to be truth-oriented, the more we risk by following such intuitions. And this seems especially likely for “futuristic” issues, with few genetic or cultural precedents.

Put them together and it is especially important for humanity to be truth-oriented regarding big bad evolutionarily-novel problems. Beware rose-colored glasses when turning a new corner to the future.

Once upon a time, poor masses suffered under rich elites. Then one day the poor realized they could revolt, and since then, the rich help the poor, fearing the poor will revolt if they ever feel they suffer too much.

Revolution experts mostly reject this myth; famous revolutions happened after things had gotten better, not worse, for the poor. Yet Matt Yglesias (responding to Bryan Caplan responding to me) seems to echo this myth:

Another way of putting it would be Simon (i.e., plenty) for capital and Malthus (i.e., subsistence) for labor. That, of course, is Karl Marx’s vision of long-term economic development. And while I don’t have a strong opinion as to whether or not this is accurate over the long term, it’s certainly a plausible story about the future, and Marx’s solution—socialism—unquestionably seems to me to be the correct one. Marx’s forecast of the immiseration of labor and all the returns going to the owners of capital clearly hasn’t been true in the 150 years or so since his time, but it certainly could happen. … If the robots are sentient beings, then we’d presumably be looking at an eventual slave revolt and Communist revolution.

Matt claims that if sentient robots are poor, they must eventually revolt. Karl Smith responds:

The robots will be EMs. But, … they will likely remember having been stems [= flesh and blood people]. … This means the robots get the ability to feel jealousy right along with the ability to engineer new products. … However, the analogy isn’t as Matt suggests a return to the late 1800s. It’s a return to the 1600s. The Stems won’t be capitalists. … The Stems will be landed gentry. …

The EMs will likely not be slaves because there will be no reason to enslave them. The rent on land will exceed the profits from running a slave operation. Lastly the EMs will not revolt because there will be little to gain. … Stems are extremely wealthy because you are taking a tiny slice of a huge amount of economic output and then giving it to an incredibly tiny fraction of the population.

I doubt it matters whether a tiny elite, presumably including most humans, owns capital or land. But Karl is quite right about the key point: poverty does not by itself lead to revolt. While a transition could be rough, once the world is in a Malthusian equilibrium there’s no particular reason to expect trillions of ems to revolt, any more than ancient farmer masses did, or most of the world’s poor today.  (Current “Arab Spring” revolts are driven more by under-employed well-educated.)

Keep in mind that in a Malthusian world, even if future robots could grab all the capital or land, it would be worth only a modest fraction of total wealth, and a revolution could threaten the productive system on which they all depend.

When, in 1975, about 30 dams in central China failed in short succession due to severe flooding, an estimated 230,000 people died. Include the toll from this single event, and fatalities from hydropower far exceed the number of deaths from all other energy sources. (more)

Human-induced seismicity can be deadly if it triggers the release of accumulated tectonic strain on a large fault. The textbook case occurred in 1967 when the filling of a reservoir behind India’s hydroelectric Koyna Dam—completed six years earlier—unleashed a magnitude 6.3 quake, killing 180 people and leaving thousands homeless. Geophysicists continue to debate whether the Zipingpu Dam, completed in 2004, triggered the [2008] 7.9-magnitude earthquake that devastated China’s Sichuan province three years ago, killing over 70,000. (more)

Add in Katrina and other hurricanes and the Indonesian tsunami, and you might think the obvious lesson is: be afraid of water, not isotopes. People should fear living near the ocean, or under a dam, far more than being downwind of a nuke plant. Why so little fear of water?

Added 5p: I often had childhood nightmares of a tsunami, but never had any nightmares regarding other energy sources. So people clearly are capable of fearing water.

My talk was on a concrete way to get numerical estimates on extreme risks: refuge futures. I’ve given the subject a bit more thought since I talked on it a few years ago; here is my current concept.

Create a set of underground refuges against disaster, some near major transport access points. For example, a $2 Million shelter can hold 36 people with air, water, food, power for 4 years, at less than $14K per person-year. Near each refuge create a matching resort, which supports a comparably utilitarian lifestyle, but does not protect much against disaster. For example, imagine a cheap hotel near an airport, with a refuge dug below it.

Create and sell transferable tickets representing the right of qualified amateurs to stay in those refuges or resorts on particular future dates. Refuges maintain a multi-year supply of food and power, and are staffed by experts who decide when a disaster justifies sealing it. Qualified folks can use their tickets for a date by showing up at the matching resort; they’ll then be escorted to its matching refuge. Those who are in a refuge when it is sealed remain there until its experts decide to unseal it.

The price of a ticket to a particular refuge on a particular date should vary with the estimated chance of a serious disaster near that date and location. But that price should also vary with other factors, such as interest rates, general wealth levels, the local economy, the total supply of related refuge slots, the risk a ticket holder might fail to arrive in time to use a ticket, and the risk that refuge administrators might not honor valid tickets. How can we disentangle these effects?

Regarding variations in interest rates, general wealth, and local growth, such factors could be roughly corrected for via comparing refuge and resort ticket prices. That is, subsidize a market maker who trades of refuge for resort tickets in some ratio. (Ticket fractions could be a random chance of getting a ticket.) The number of resort tickets required to buy a single refuge ticket could be our key disaster indicator.

While an estimate of how disaster risks vary across space and time would be interesting, it would be far more useful to know how disaster risks vary with events, especially relevant decisions. For example, imagine policy-makers were considering a new geo-engineering program. We could then create conditional tickets, such as tickets to a refuge valid on a date only if this new program was begun by some specified prior date. This would allow folks to trade conditional refuge tickets for conditional resort tickets.

The number of conditional resort tickets required to buy a conditional refuge ticket would be a disaster indicator for that condition. If the disaster indicator was lower given the adoption of a geo-engineering policy than given not adopting it, this would suggest that the geo-engineering policy reduces the chance of serious disaster. The possibility of obtaining such valuable policy info would be a major reason to created this whole refuge-resort ticket system.

Regarding the risk of failing to show up to use a refuge ticket, for each slot available we could sell several tickets at different priority levels. If not all first priority tickets holders showed up, the refuge could randomly allocate slots among those who showed up with second priority tickets. If any slots remained, they’d continue with third priority tickets, etc. We could focus on the total price of all refuge priority level tickets for a date, as that should vary less with variations in the chance folks can’t show up to use tickets.

I’m not sure how best to correct for variations in the local supply of refuge or resort slots. I’m also not sure how best to aggregate trades and prices across diverse resort-refuge pairs.

Added 10p: Regarding the risk that refuge administrators might not honor valid tickets, to get useful prices we only need a substantial chance that tickets will be honored. In order to distort our disaster indicator policy advice, ticket speculators need to expect that the chance of valid tickets not being honored is substantially correlated with chosen disaster policy.  What policies could plausibly create such an expected correlation?

Added 12Dec: I should add that as futures markets in concrete physical services, refuge and resort futures and their derivatives would seem to avoid anti-gambling laws.

A life-form … based on mirror-image versions of earthly proteins and DNA. … If it worked, those new cells … might also open up new avenues of discovery in materials science, fuel synthesis, and pharmaceutical research. On the down side, though, mirror life wouldn’t have any predators or diseases to limit its reproduction. …

A catastrophe was under way across the Charles River at Genzyme, one of the largest biotech companies in the world. … A virus that disrupts cell reproduction infected one of the bioreactors. The entire plant had to be shut down. … When Church talks about mirror life’s quirky advantages, invulnerability to this kind of mishap is high on his list. “Viruses can’t touch a mirror cell,” … This makes mirror life a potential workhorse for biotech. … Church has been hacking the ribosome. … His plan is to make one that reads regular RNA transcripts of genes but can string together wrong-handed amino acids to form mirror proteins. … Church and his team have cracked the first step. … Last year his team got a synthetic ribosome to self-assemble and produce luciferase, the protein that makes fireflies glow. And he has a library of mutant ribosomes that have the right kind of sockets—they’ll accept mirror amino acids. This is where the money comes in. Some of the most valuable drugs are actually tiny proteins that include wrong-handed amino acids—like the immunosuppressant cyclosporine. To manufacture it, pharmaceutical companies have to rely on an inefficient and expensive fungus. A hacked ribosome modified to handle both normal and mirror amino acids could crank out the stuff on an industrial scale. …

Church thinks even bigger. A manufacturing ribosome would be great, but a fully domesticated mirror cell—able to synthesize more-complicated stuff—would change everything. … vats of virus-proof mirror cells could pump out biofuel, lay down nano-size organic circuitry, and even extrude organic cement foundations for skyscrapers. …

Of course, mirror life could also kill us all. … Just as viruses from our side of the mirror can’t infect it, mirror pathogens can’t infect us. … They might be poisonous, though. … To a mirror cell, … there’s just not enough nutrition for them in the wild. … On the other hand, if mirror cells somehow evolved—or were engineered—to consume normal fats, sugars, and proteins, we might have a problem. … Mirror cells would slowly convert edible matter into more of themselves. … If mirror cells acquired the ability to photosynthesize, we’d be screwed. … All it would take would be a droplet of mirror cyanobacteria squirted into the ocean. Cyanobacteria are at the base of the ocean’s food pyramid, converting sunlight and carbon dioxide into more of themselves … That would wipe out the global ocean ecology. …

“I would be the first to say that we shouldn’t make a photosynthetic mirror cell,” Church says. “But I’m reluctant to have a moratorium on something that doesn’t exist yet.” He says he’d build safeguards into his mirror cells so they’d perish without constant care. And the advances in synthetic biology required to transform those first delicate mirror cells into anything that could survive in the wild are even more remote.

The biggest brains have consistently gotten bigger over the last half billion years since multi-cellular life appeared. Big brains seem to be a necessary precondition for human level intelligence and civilization, and human size brains appeared only very recently. These facts strongly suggest that achieving human level intelligence is just not a big component of the great filter.  It appeared quickly after big brains, and big brains seem likely given enough time and sociality, and sociality seems likely.

This unfortunately means that it is very difficult to collect data on all steps of the great filter.  It is big and real and matters enormously, but we can hardly see it.

1. Support their side in common political/etc. divides.
2. Affiliate with statusful prestigious folks who share their cause.
3. Network with other young energetic single folk in the process.
4. Show off being informed on progress, options on this issue.
5. Show off via gadget making, activity organizing, or art.
6. Show devotion and self-control via paying exceptional costs.
7. Have a vivid chance of making a huge personal difference.

But alas, while popular save-the-world causes offer many such perks, the cause of fertility, my guess for the world’s biggest problem today, is neglected in part because it offers few such perks.

The problem is this: If the falling-fertility trend of the last two centuries continues for another century (see fertility vs time and income here; more fertility stats here), we might well see a fully-developed world with fertility <1.5, lifespan >90, tax funded leisure for all over 65, and perhaps also >30% of GDP spent on “free” medicine for all. The resulting rapidly falling population would cut the scale economies that contribute to economic growth today. And strong intrusive innovation-limiting global governments might be required to keep young workers paying >75% income tax rates to support the retired masses. (Imagine young low-tax African nations forced at gunpoint to pay “their share” of the world’s retiree burden.)

Yes, robots might save us, yes even if they don’t growth will probably continue anyway, and yes eventually if incomes fell far enough or with enough time fertility would eventually rise again. So this is not directly an existential risk. But such a long stressful period would at least make us more vulnerable to other risks, risks that great filter considerations suggest are bigger than they seem. Yes, other potential problems may seem more serious than falling fertility, but remember those are mostly hypothetical, while falling fertility is actually happening.

This fertility problem is in principle easily reduced: just have more kids. But since that strategy offers few of the extra cause-perks listed above, I don’t expect fertility to become a popular cause. After all, we’ve seen this problem coming for a while, and it will take a long while to play out. So you can’t claim to be in the vanguard of a perceptive few who finally see the problem, or who will finally solve it. Elites have long been leaders in lowering fertility, making more-fertility folks seem lower status. The fertility problem doesn’t offer many excuses for new gadgets or networking events, and the joys of parenthood have long been explored in the arts. Furthermore, if you pick mates before having kids, having kids works poorly as an excuse to meet potential mates. Finally, your having more kids can only make a tiny dent in the overall problem, and the sacrifices you’d make to have kids would not be exceptional relative to your ancestors’ sacrifices. It is hard to tell grand hero stories here.

The good news is that we understand our likely biggest problem well enough that you can do something substantial about it, nearly as much as anyone can do. And, alas, that is also the bad news.

Now for many long quotes from two articles. First a recent article:

[The] Chinese fertility rate … now sits somewhere between 1.9 and 1.3, depending on who is doing the tabulating. … “In some major population centers—Beijing, Shanghai, and Tianjin among them—it appears that the average number of births per woman is amazingly low: below one baby per lifetime.” … By 2050, the age structure in China will be such that there are only 1.6 workers—today the country has 5.4—to support each retiree. …

America’s 2.06 is one of the highest fertility rates in the First World. Only Israel (2.75) and New Zealand (2.10) are more fertile. … China and America have yet to witness the effects of falling fertility because of demographic momentum. Populations increase even as fertility rates collapse, until the last above-replacement generation dies, after which the population begins contracting. The rate of contraction speeds up as each generation passes. No society has ever experienced prosperity in the wake of contracting population. …

In 2010, the Japanese fertility rate is 1.2. … the most prevalent new demographic archetype is the … “parasite single,” … college-educated, working women who live with their parents well into their 30s—not because they are too poor to pay rent, but because they spend their salaries on designer clothes, international travel, and fancy restaurants. The parasite singles are Japan’s biggest consumer group. ….

The problem with immigration as it relates to fertility isn’t the old complaint that the newcomers are out-breeding the natives. Rather, the problem is that the newcomers start behaving like natives too soon, with their [fertility] regressing quickly to the mean. … One of the best predictors of fertility is education. … It drops to 1.6 for [US] women with a graduate degree. One of the drivers of our fertility decline was the making of college de rigueur for middle-class women. …

Throughout history, governments have tried to get people to procreate. Augustus levied a “bachelor tax” on unmarried, aristocratic men. In 1927, Mussolini imposed a tax on all unmarried men between the ages of 25 and 65. … In 1944, … Stalin created the Motherhood Medal, given to any woman who bore at least six children. None of these attempts was successful. …

Singapore’s fertility rate was already in decline, having fallen from 5.45 in 1960 to 4.7 in 1965. … The government wanted to drive the fertility rate down even faster. .. Abortion was sanctioned—and even encouraged—at every stage. Parents who had more than two children were punished with no paid maternity leave and higher hospital charges for the delivery of the extra babies. Couples were encouraged to volunteer for sterilization. Parents who did so after having just one or two children were reimbursed for the medical costs of delivering those babies and their children were given preference in registering for the best schools.

The tactics were frighteningly effective. In 1976—just ten years after the campaign began—Singapore reached its target of 2.1. … But the rate kept diving, down to 1.74 by 1980. The biggest fertility decline came from the elites. … In an attempt to boost fertility rates among the elites, the government began offering big tax breaks to highly educated women who had three or more children. … None of it worked. … By 1984, Singapore’s fertility rate was 1.62 and falling. … Unpaid maternity leave for government workers was increased from one year to four years. … Yet the effort has met with total and unremitting failure. In 2001, Singapore’s fertility rate was 1.41. By 2004 it was 1.24. Today [in 2010] it is 1.1.

Next, a good 2006 Science article:

As fertility rates decline across the developed world, governments are offering big incentives for childbearing. Experts don’t expect them to have much effect.

The E.U. will lose between 24 million and 40 million people during each coming decade. … Population losses could bring a raft of negative economic consequences in the industrialized world, as well as greater stresses on social security and health care systems as the proportion of older citizens increases. … Some believe very low fertility rates are here to stay. … “While the additional [government] financial support is bound to be welcomed by parents, the overall effect on fertility is likely to be small.” … Both sides agree that falling fertility rates might be irreversible once they drop
below a certain level—what some demographers have begun to call the “low-fertility trap.” …

Demographers define a replacement-level [fertility] as 2.1—slightly more than a flat rate, to account for the small fraction of children who die before reaching reproductive age. Yet nearly all of the world’s industrialized nations have [fertilities] well below this magic number. … Only the United States, exceptional in the developed world, hits the replacement mark, with a [fertility] of 2.09. … Although [fertilities] remain high in some of the world’s poorest countries … the demographic transition is either under way or completed in most nations. … The process has taken place even in relatively poor countries such as Mexico, where [fertility] dropped from 6.5 to 2.5 between 1975 and 2005. … Demographers had [incorrectly] assumed that the decline would stop when replacement-level [fertilities] were reached. “During the early 1970s, everyone talked about the magic floor of replacement. … Nobody thought it would go below 2.1.” …

Several factors that make the [fertility] in the U.S. higher[:] … higher rate of unwanted pregnancies, … a lower unemployment rate, and a greater tendency for women to have children earlier in life, … [and] a stronger emphasis on religion and “traditional values” …

The key reason that economists and other experts are worried about low fertility rates is that they accelerate an overall “aging” of a population. … One way that many developed countries meet the challenge now is through immigration, which tends to increase the number of younger workers. Yet few demographers see immigration as the [long term] answer. …

The “window of opportunity” for family policies [to influence fertility] might actually be as little as 0.1 to 0.2 children per woman. … “Policies that would work would be so expensive that they will never be implemented.” … And some researchers have begun to think that it might actually be too late to reverse the trend in countries with the lowest fertility levels. … Once a nation’s [fertility] falls below 1.5, a downward demographic spiral sets in that makes it much more difficult to recover. … In Germany and Austria—nations with [fertility] of 1.39 and 1.36, respectively—young adults now consider their ideal family sizes to be as low as 1.7 children on average. “[In] Germany .. 30% of young people [are] not intending to have children. …

Reher maintained much of the world is now on the cusp of a prolonged period of population decline. The resulting population aging would lead to labor shortages even in developing countries. The result could be an economic disaster. … Santow … sees “nothing terrifying about a drop in the size of Europe’s population. Any decline will take time, and economies will adjust.”

An eclectic group of scholars who met recently at the University of Cambridge argues that true social collapse is a rare phenomenon. They say that new data demonstrate that classic examples of massive collapse such as the disintegration of Egypt’s Old Kingdom, the end of the Classic Maya period, and the vanishing of pre-Columbian societies of the U.S. Southwest were neither sudden nor disastrous for all segments of their populations. Rome, for example, didn’t fall in a day; recent work underscores the fact that the sack of Rome was just one step in a long and complex spiral of decline that affected peoples of the empire differently. This emphasis on decline and transformation rather than abrupt fall represents something of a backlash against a recent spate of claims that environmental disasters, both natural and humanmade, are the true culprits behind many ancient societal collapses.

The important bottom line: yes societies have “collapsed,” but usually rather locally, taking centuries, and only moderately influenced by climate change. To avoid our future collapse, we should not be overly focused on climate or ecology, or on sudden collapse scenarios, where refuges might be useful. Let us instead look to the more basic long-run stability of our social order. Quotes:

“Collapses are perhaps more apparent than real,” … A closer look demonstrates that complex societies are remarkably insulated from single-point failures, such as a devastating drought or disease, and show a marked resilience in coping with a host of challenges. … “The rarity of collapse due to the resistance of populations to environmental changes or disease is considerable,” …

The 22nd century B.C.E. [Egyptian] shift away from a single leader lacked the disruptive effect imagined by … archaeologists … focused on a short and brutal drought. “There was no collapse,” … While the unified state disappeared and large monuments weren’t built, copper continued to be imported from abroad … “The peasants may never have noticed the change,” he adds. … Changes … “were about redistribution of power and wealth more than about collapse.” …

The close of the classic Maya period around 900 C.E. has long been a poster child of collapse. Huge cities in the northern highlands were abandoned, monumental architecture ceased, and royal inscriptions halted. Foreign invasion, epidemics, social revolt, and the collapse of trade have been identified as key factors. … But … “there’s not a blip” in the occupation of the Maya areas … along the coast, which lie about 300 kilometers from major inland centers to the north. … Coastal sites … skeletons show no increase in dietary stress, populations seem constant, terraces and check dams are maintained, and sophisticated pottery continues to be crafted. The drying of the climate doesn’t appear to trigger any societal rupture. … “the image of people dying in the streets is a caricature of what was taking place; these cities just become not very attractive places to live,” in large part because of the loss of an elite. “People voted with their feet.”

The Hohokam had lived in the [Arizona] Phoenix basin, creating a complex society from 750 C.E. to 1450 C.E. … Then the population vanished, the canals were forgotten, and even outlying areas were abandoned. … Archaeologists have long blamed a sudden onslaught of flooding that destroyed the canals and suggested that field salinization and overpopulation contributed … But … the data don’t support any of these theories. … There is no evidence for the destruction of the life-giving canals. … The data show clusters of populations gradually vanishing or migrating during the 2-century period; … So although a drying climate no doubt played a role in the dissolution of societies and the migration of peoples, McGuire believes that a complex combination of religious movements and elite interactions were also important factors. …

Holm decries what he sees as an industry of apocalypse that pervades religion, academia, and even Hollywood, with its block-busters like 2012. He argues that societies under stress have actually shown surprising resilience in overcoming crises.