Tag Archives: History

The Long Term Future of History

Assuming that dark energy continues to make the universe expand at an accelerating rate, in about 150 billion years all galaxies outside the Local Supercluster will pass behind the cosmological horizon. It will then be impossible for events in the Local Group to affect other galaxies. Similarly it will be impossible for events after 150 billion years, as seen by observers in distant galaxies, to affect events in the Local Group. (More)

My last two posts suggested that the average spacing between independently-originating aggressive alien civilizations is roughly 1-4 billion light years. If we can eventually get light signals from galaxies that are roughly 100 billion light years away today, this suggests that we’ll be able to dimly see the first billion or so years of the history of a few tens to hundreds of such alien civilizations. But just seeing them dimly won’t really tell us that much about them, and we may be terribly curious to know much more.

Aliens who aspire to win in the great universal meme evolution contest should seek to take advantage of this curiosity, by sending out messages about themselves and their memes. There are two obvious ways to do this. They can either sent data in light (or other fast particle) signal messages, or they can send physical emissaries that carry lots of data with them.

Over these distances, data sent by physical emissaries goes slower, and is sent directly to fewer locations, but its quantity can be far more. However, it will be harder to believe that the emissary data you receive is actually the data that was originally sent. Especially if it is passed on via several intermediary civilizations. In contrast, while less data can be sent in light signals, not only does it go faster, but one can have stronger confidence that the signal received was actually the signal sent.

The possibility that history may be rewritten is a problem not only for emissaries, but also for ourselves. In fact, the most trustworthy data on our own history might be the signals that we sent out long ago to others, which they then simply reflect back to us. By mixing up the signals that you send out with the signals you reflect back to others, you give them a modestly stronger incentive to read what you send.

To believe our reflected signals, we’d need to encrypt what we send our outgoing signals in some way to make it very hard for them to change them without corrupting them. However, if there are cryptographic hash scheme that can’t be cracked over billions of years by civilizations eager to change history, we could use this not only to trust our reflected signals, but also to let distant aliens verify that the large data they get via emissaries was actually the data that we sent out long long ago.

As with all cosmic beacons, there’s be an advantage to coordinating on where to look when to see them. Such as sending a signal right after seeing a gamma ray burst, and in the exact opposite direction so your signal follows the burst. Then listeners look for your message right after seeing a burst, and in that same direction.

Added 24Dec: As I’ve discussed before, humans cultures had separated diversity for ~1Myr, and now have much stronger  integration, but will again diversify in 1Kyr+ as we spread out among the stars. It seems a similar pattern will play out among alien civs later. They go from separated diversity for 1st ~1Byr, to much stronger integration at ~1-100Byr, but then they diversify again as they lose contact w/ each other after that.

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Try-Try or Try-Once Great Filter?

Here’s a simple and pretty standard theory of the origin and history of life and intelligence. Life can exist in a supporting oasis (e.g., Earth’s surface) that has a volume V and metabolism M per unit volume, and which lasts for a time window W between forming and then later ending. This oasis makes discrete “advances” between levels over time, and at any one time the entire oasis is at the same level. For example, an oasis may start at the level of simple dead chemical activity, may later rise to a level that counts as “life”, then rise to a level that includes “intelligence”, and finally to a level where civilization makes a big loud noises that are visible as clearly artificial from far away in the universe.

There can be different kinds of levels, each with a different process for stepping to the next level. For example, at a “delay” level, the oasis takes a fixed time delay D to move to the next level. At a “try once” level, the oasis has a particular probability of immediately stepping to the next level, and if it fails at that it stays forever “stuck”, which is equivalent to a level with an infinite delay. And at a “try try” level, the oasis stays at a level while it searches for an “innovation” to allow it to step to the next level. This search produces a constant rate per unit time of jumping. As an oasis exists for only a limited window W, it may never reach high levels, and in fact may never get beyond its first try-try level.

If we consider a high level above many hard try-try levels, and with small enough values of V,M,W, then any one oasis may have a very small chance of “succeeding” at reaching that high level before its window ends. In this case, there is a “great filter” that stands between the initial state of the oasis and a final success state. Such a success would then only tend to happen somewhere if there are enough similar oases going through this process, to overcome these small odds at each oasis. And if we know that very few of many similar such oases actually succeed, then we know that each must face a great filter. For example, knowing that we humans now can see no big loud artificial activity for a very long distance from us tells us that planets out there face a great filter between their starting level and that big loud level.

Each try-try type level has an expected time E to step to the next level, a time that goes inversely as V*M. After all, the more volume there is of stuff that tries, and faster its local activity, the more chances it has to find an innovation. A key division between such random levels is between ones in which this expected time E is much less than, or much greater than, the oasis window W. When E << W, these jumps are fast and “easy”, and so levels change relatively steadily over time, at a rate proportional to V*M. And when E >> W, then these jumps are so “hard” that most oases never succeed at them.

Let us focus for now on oases that face a great filter, have no try-once steps, and yet succeed against the odds. There are some useful patterns to note here. First, let’s set aside S, the sum of the delays D for delay steps, and of the expected times E for easy try-try steps, for all such steps between the initial level and the success level. Such an oasis then really only has a time duration of about W-S to do all its required hard try-try steps.

The first pattern to note is that the chance that an oasis does all these hard steps within its window W is proportional to (V*M*(W-S))N, where N is the number of these hard steps needed to reach its success level. So if we are trying to predict which of many differing oases is mostly likely to succeed, this is the formula to use.

The second pattern to note is that if an oasis succeeds in doing all its required hard steps within its W-S duration, then the time durations required to do each of the hard steps are all drawn from the same (roughly exponential) distribution, regardless of the value of E for those steps! Also, the time remaining in the oasis after the success level has been reached is also drawn from this same distribution. This makes concrete predictions about the pattern of times in the historical record of a successful oasis.

Now let’s try to compare this theory to the history of life on Earth. The first known fossils of cells seems to be from 0.1-0.5 Ga (billion years) after life would be possible on Earth, which happened about 4.2 Gya (billion years ago), which was about 9.6 Ga after the universe formed. The window remaining for (eukaryotic) life to remain on Earth seems 0.8-1.5 Ga. The relatively steady growth in max brain sizes since multi-cellular life arose 0.5 Gya suggests that during this period there were many easy, but no hard, try-try steps. Multi-celluar life seems to require sufficient oxygen in the atmosphere, but the process of collecting enough oxygen seems to have started about 2.4 Gya, implying a long 1.9 Ga delay step. Prokaryotes started exchanging genes about 2.0 Gya, eukaryotes appeared about 1.7 Gya, and modern sex appeared about 1.2 Gya. These events may or may not have been the result of successful try-try steps.

Can we test this history against the predictions that try-try hard step durations, and the window time remaining, should all be drawn from the same roughly exponential distribution? Prokaryote sex, eukaryotes, and modern sex all appeared within 0.8 Ga, which seems rather close together, and leaving a long uneventful period of almost ~2 Ga before them. The clearest hard step duration candidates are before the first life, which took 0.0-0.5 Ga, and the window remaining of 0.8-1.5 Ga, which could be pretty different durations. Overall I’d say that while this data isn’t a clear refutation of the same hard step distribution hypothesis, it also isn’t that much of a confirmation.

What about the prediction that the chance of oasis success is proportional to (V*M*(W-S))N? The prediction about Earth is that it will tend to score high on this metric, as Earth is the only example of success that we know.

Let’s consider some predictions in turn, starting with metabolism M. Life of the sort that we know seems to allow only a limited range of temperatures, and near a star that requires a limited range of distances from the star, which then implies a limited range of metabolisms M. As a result of this limited range of possible M, our prediction that oases with larger M will have higher chances of success doesn’t have much room to show itself. But for what its worth, Earth seems to be nearer to the inner than outer edge of the Sun’s allowable zone, giving it a higher value of M. So that’s a weak confirmation of the theory, though it would be stronger if the allowed zone range were larger than most authors now estimate.

What about volume V? The radii of non-gas-giant planets seems to be lognormally distributed, with Earth at the low end of the distribution (at a value of 1 on this axis):

So there are many planets out there (at r=4) with 16 times Earth’s surface area, and with 64 times the volume, ratios that must be raised to the power of N to give their advantage over Earth. And these larger planets are made much more of water than is Earth. This seems to be a substantial, if perhaps not overwhelming, disconfirmation of the prediction that Earth would score high on VN. The higher is the number of hard steps N, the stronger is this disconfirmation.

Regarding the time window W, I see three relevant parameters: when a planet’s star formed, how long that star lasts, and how often there are supernova nearby that destroy all life on the planet. Regarding star lifetimes, main sequence star luminosity goes as mass to the ~3.5-4.0 power, which implies that star lifetimes go inversely as mass to the ~2.5-3.0 power. And as the smallest viable stars have 0.08 of our sun’s mass, that implies that there are stars with ~500-2000 times the Sun’s lifetime, an advantage that must again be raised to the power N. And there are actually a lot more such stars, 10-100 times more than of the Sun’s size:

However, the higher metabolism of larger mass stars gives them a spatially wider habitable zone for planets nearby, and planets near small stars are said to face other problems; how much does that compensate? And double stars should also offer wider habitable zones; so why is our Sun single?

Now what if life that appears near small long-lived stars would appear too late, as life that appeared earlier would spread and take over? In this case, we are talking about a race to see which oases can achieve intelligence or big loud civilizations before others. In which case, the prediction is that winning oases are the ones that appeared first in time, as well has having good metrics of V,M,W.

Regarding that, here are estimates of where the habitable stars appear in time and galactic radii, taking into account both star formation rates and local supernovae rates (with the Sun’s position shown via a yellow star):

As you can see, our Sun is far from the earliest, and its quite a bit closer to galactic center than is ideal for its time. And if the game isn’t a race to be first, our Sun seems much earlier than is ideal (these estimates are arbitrarily stopped at 10Ga).

Taken together, all this seems to me to give a substantial disconfirmation of the theory that chance of oasis success is proportional to (V*M*(W-S))N, a disconfirmation that gets stronger the larger is N. So depending on N, maybe not an overwhelming disconfirmation, but at least substantial and worrisome. Yes, we might yet discover more constraints on habitability to explain all these, but until we find them, we must worry about the implications of our analysis of the situation as we best understand it.

So what alternative theories do we have to consider? In this post, I’d like to suggest replacing try-try steps with try-once steps in the great filter. These might, for example, be due to evolution’s choices of key standards, such as the genetic code, choices that tend to lock in and get entrenched, preventing competing standards from being tried. The overall chance of success with try-once steps goes as the number of oases, and is independent of oasis lifetime, volume, or metabolism, favoring many small oases relative to a few big ones. With more try-once steps, we need fewer try-try steps in the great filter, and thus N gets slower, weakening our prediction conflicts. In addition, many try-once steps could unproblematically happen close to each other in time.

This seems attractive to me because I estimate there to be in fact a great many rather hard steps. Say at least ten. This is because the design of even “simple” single cell organisms seems to me amazingly complex and well-integrated. (Just look at it.) “Recent” life innovations like eukaryotes, different kinds of sex, and multicellular organisms do involved substantial complexity, but the total complexity of life seems to me far larger than these. And while incremental evolution is capable of generating a lot of complexity and integration, I expect that what we see in even the simplest cells must have involved a lot of hard steps, of either the try-once or the try-try type. And if they are all try-try steps, that makes for a huge N, which makes the prediction conflicts above very difficult to overcome.

Well that’s enough for this post, but I expect to have more to say on the subject soon.

Added 19Jan: Turns out we also seem to be in the wrong kind of galaxy; each giant elliptical with a low star formation rate hosts 100-10K times more habitable Earth-like planets, and a million times as many habitable gas giants, than does our Milky Way.

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Yay Parliaments

Voters may like the idea of direct democracy, but as Garett Jones mentions in 10% Less Democracy, most scholars agree that representative democracy produces better outcomes. Similarly, while voters may thrill more to directly choose their top leader, better outcomes come from having voters pick legislators who then pick, and can remove, the top leader.

Here’s Arend Lijphart with some simple theory:

In parliamentary systems, only the legislature is popularly elected and is the clear and legitimate representative of the people, but in presidential systems both president and legislature are popularly elected and are both legitimate representatives of the people—but it is quite possible and even likely that the president and the majority of legislators have divergent political preferences. … There is no democratic principle to resolve such disagreements. … second problem is “rigidity”: presidents are elected for fixed periods of time. … third serious problem is the “winner take all” nature of presidential elections. … The fourth serious drawback of presidentialism is that presidential election campaigns encourage the politics of personality … instead of … competing parties and … programs.

In his new book Why Not Parliamentarism? Tiago Ribeiro Dos Santos collects much evidence favoring that option: Continue reading "Yay Parliaments" »

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Lost Advanced Civilizations

Did life on Earth start on Earth, or did it start on Mars and move to Earth? If you frame such panspermia as an “extraordinary claim” for which you demand “extraordinary evidence”, you will of course conclude that this should be treated “skeptically” as unlikely and sloppy unscientific “speculation”. To be disdained and not treated as serious by respectable academics and science journalists. But that’s not really fair.

You see the early Mars environment is, a priori, about as likely a place for life to start as the Earth environment. So if the rate at which life is transferred between the planets were high enough, then equal chances of life starting first in both places would result in equal chances for Earth life to have started in either place. We should take the expected time difference between life starting in the two places, and ask how high is the chance that life would move from one planet to the next during that period. The more often rocks are thrown from one place to the other, and the more easily life could survive for the travel period within those rocks, then the more likely it is that Earth life started on Mars.

In addition, Mars, being further from the Sun, would have cooled first, and had a head start in its window for life. Making it more likely that life would start there and spread to Earth than vice versa. Of course life starting first on Mars would have implications for what we might see when we look at Mars. If we had expected Mars life to continue strong until today, then the fact that we see no life on Mars now would be a big strike against this hypothesis. But if we expected Mars life to have died out or at least gone dormant by now, then the issue is what we will see when we dig on Mars. With enough data on such digs, we may come to reject to Mars first hypothesis even given its initial plausibility.

A similar analysis applies to panspermia from other stars. You might think it obvious that the rate at which life-filled rocks from a star make it to seed other stars is very low, but most stars are born in large groups close together in stellar nurseries. So if life arose early enough within our star’s nursery, there might have been high rates of moving that life between stars in that nursery. In which case the chance that Earth life came from another star could also be high, and the best place to look for life outside our star would be the other stars from our stellar nursery.

Now consider the possibility of lost advanced civilizations. Not just civilizations at a similar level of development to those around them in space and time; that’s quite likely given that we keep finding new previously-unknown settlements and developed places. No, the more interesting claims are about substantial (but not crazy extreme) decreases in the peak or median level of civilizations across wide areas. Such as what happened late in the late Mediterranean Bronze Age, or at the fall of the Roman Empire. Could there have been “higher” civilizations before the “first” ones that we now know about in each region, such as the Sumerians, Egyptians, and Chinese Shang dynasty? (I’m talking human civs, not others.) Continue reading "Lost Advanced Civilizations" »

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Science 2.0

Skepticism … is generally a questioning attitude or doubt towards one or more items of putative knowledge or belief or dogma. It is often directed at domains, such as the supernatural, morality (moral skepticism), theism (skepticism about the existence of God), or knowledge (skepticism about the possibility of knowledge, or of certainty). (More)

Humans have long had many possible sources for our beliefs about the physical world. These include intuitive folk physics, sacred scriptures, inherited traditions, traveler stories, drug-induced experiences, gadget sales pitches, and expert beliefs within various professions. And for a very long time, we paid the most attention to the highest status sources, even if they were less reliable. This encouraged gullibility; we often believed pretty crazy stuff, endorsed by the high status.

One ancient high status group was astronomers, whose status was high because their topic was high – the sky above. It so happened that astronomers naturally focused on a small number of very standard parameters of wide interest: the sky positions of planets and comets (anything that moved relative to the stars). Astronomers often gained status by being better able to predict these positions, and for this purpose they found it useful to: (1) collect and share careful records on past positions, (2) master sufficient math to precisely describe past patterns, and (3) use those patterns to predict future parameter values.

For a long time astronomy seemed quite exceptional. Most other domains of interest seemed to have too much fuzziness, change, and variety to support a similar approach. What can you usefully measure while walking through a jungle? What useful general patterns can simple math describe there? But slowly and painfully, humans learned to identify a few relatively stable focal parameters of wide interest in other domains as well. First in physics: velocity, weight, density, temperature, pressure, toughness, heat of reaction, etc. Then in dozens of practical domains.

With such standard focal parameters in hand, domain experts also gained status by being able to predict future parameter values. As a result, they also learned that it helped to carefully collect shared systematic data, and to master sufficient math to capture their patterns.

And thus beget the scientific revolution, which helped beget the industrial revolution. A measurement revolution starting in astronomy, moving to physics, and then invading dozens of industrial domains. As domains acquired better stable focal parameters to observe, and better predictions, many such domains acquired industrial power. That is, those who had mastered such things could create devices and plans of greater social value. This raised the status of such domain experts, so that eventually this “scientific” process acquired high status: carefully collecting stable focal parameters, systematically collecting and sharing data on them, and making math models to describe their patterns. “Science” was high status.

One way to think about all this is in terms of the rise of skepticism. If you allow yourself to doubt if you can believe what your sources tell you about the physical world, your main doubt will be “who can I trust?” To overcome such doubt, you’ll want to focus on a small number of focal parameters, and for those seek shared data and explicit math models. That is, data where everyone can check how the data is collected, or collect it themselves, with redundant records to protect against tampering, and explicit shared math models describing their patterns. That is, you will turn to the methods to which those astronomers first turned.

Which is all to say that the skeptics turned out to be right. Not the extreme skeptics who doubted their own eyes, but the more moderate ones, who doubted holy scriptures and inherited traditions. Our distant ancestors were wrong (factually, if not strategically) to too eagerly trust their high status sources, and skeptics were right to focus on the few sources that they could most trust, when inclined toward great doubt. Slow methodical collection and study of the sort of data of which skeptics could most approve turned out to be a big key to enabling humanity’s current levels of wealth and power.

For a while now, I’ve been exploring the following thesis: this same sort of skepticism, if extended to our social relations, can similarly allow a great extension of our “scientific” and “industrial” revolutions, making our social systems far more effective and efficient. Today, we mainly use prestige markers to select and reward the many agents who serve us, instead of more directly paying for results or following track records. If asked, many say we do this because we can’t measure results well. But as with the first scientific revolution, with work we can find ways to coordinate to measure more stable focal parameters, sufficient to let us pay for results. Let me explain.

In civilization, we don’t do everything for ourselves. We instead rely on a great many expert agents to advise us and act for us. Plumbers, cooks, bankers, fund managers, manufacturers, politicians, contractors, reporters, teachers, researchers, police, regulators, priests, doctors, lawyers, therapists, and so on. They all claim to work on our behalf. But if you will allow yourself to doubt such claims, you will find plenty of room for skepticism. Instead of being as useful as they can, why don’t they just do what is easy, or what benefits them?

We don’t pay experts like doctors or lawyers directly for results in improving our cases, and we don’t even know their track records in previous cases. But aside from a few “bad apples”, we are told that we can trust them. They are loyal to us, coming from our nation, city, neighborhood, ethnicity, gender, or political faction. Or they follow proper procedures, required by authorities.

Or, most important, they are prestigious. They went to respected schools, are affiliated with respected institutions, and satisfied demanding licensing criteria. Gossip shows us that others choose and respect them. If they misbehave then we can sue them, or regulators may punish them. (Though such events are rare.) What more could we want?

But of course prestige doesn’t obviously induce a lawyer to win our case or promote justice, nor a doctor to make us well. Or a reporter to tell us the truth. Yes, it is logically possible that selecting them on prestige happens to also max gains for us. But we rarely hear any supporting argument for such common but remarkable claims; we are just supposed to accept them because, well, prestigious people say so.

Just as our distant ancestors were too gullible (factually, if not strategically) about their sources of knowledge on the physical world around them, we today are too gullible on how much we can trust the many experts on which we rely. Oh we are quite capable of skepticism about our rivals, such as rival governments and their laws and officials. Or rival professions and their experts. Or rival suppliers within our profession. But without such rivalry, we revert to gullibility, at least regarding “our” prestigious experts who follow proper procedures.

Yes, it will take work to develop better ways to measure results, and to collect track records. (And supporting math.) But progress here also requires removing many legal obstacles. For example, trial lawyers all win or lose in public proceedings, records of which are public. Yet it is very hard to actually collect such records into a shared database; many sit in filing cabinets in dusty county courthouse basements.

Contingency fees are a way to pay lawyers for results, but they are illegal in many places. Bounty hunters are paid for results in catching fugitives, but are illegal in many places. Bail bonds give results incentives to those who choose jail versus freedom, but they are being made illegal now. And so on. Similarly, medical records are more often stored electronically, but medical ethics rules make it very hard to aggregate them, and also to use creative ways to pay doctors based on results.

I’ve written many posts on how we could work to pay more for results, and choose more based on track records. And I plan to write more. But in this post I wanted to make the key point that what should drive us in this direction is skepticism about how well we can trust our usual experts, chosen mainly for their prestige (and loyalty and procedures) and using weak payment incentives. You might feel embarrassed by such skepticism, thinking it shows you to be low status and anti-social. After all, don’t all the friendly high status popular people trust their experts?

But the ancient skeptics were right about distrusting their sources on the physical world, and following their inclination helped to create science and industry, and our vast wealth today. Continuing to follow skeptical intuitions, this time regarding our expert agents, may allow us to create and maintain far better systems of law, medicine, governance, and much more. Onward, to Science 2.0!

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The Big Change In Blame

Law is our main system of official blame; it is how we officially blame people for things. So it is a pretty big deal that, over the last few centuries, changes to law have induced big changes in who officially blames who for most things that go wrong. These changes may be having big bad effects.

Long ago most everyone could use law to blame most everyone else. Even though people were poor, the legal process was simple enough for most to use it without needing a lawyer. (Many places actually banned lawyers.) Those found liable could often be sold into slavery to pay their legal debts, and their larger family clans could also be held responsible for their debts. So basically, people blamed people, with families as guarentors.

Over the last few centuries, the legal system has become far more complex and expensive, now requiring people to pay lawyers to sue. But at the same time we’ve made it harder to get people who are found liable to pay. We don’t sell them into slavery or make their families pay, and going bankrupt has become easier and less painful. So when ordinary people suffer a harm and look for someone to sue, their lawyers usually strongly advise that they focus on any deep pockets at all related to their harm.

The law, sympathetic to their plight, has found ways to blame the rich and big firms for most everything that goes wrong. For example, these are all real examples.

  • A rape in an abandoned building is blamed on the building owner.
  • Harassment in a stadium parking lot is blamed on the stadium owner.
  • A student harming another student in an off-campus apartment is blamed on the school.
  • A post-event bad-weather auto-accident is blamed on event host for not cancelling.
  • A harm from using a product bought from a 3rd party is blamed on its manufacturer.

As ordinary people aren’t suing each other much, the government steps in to discipline ordinary folks’ behavior, via regulation and crime law. So, while once people blamed people, law now trains people to blame the rich and big business, and to expect to be blamed by government. So it maybe isn’t so strange that in the recent US Democratic presidential debates, the main parties blamed are the rich and big business. And if ordinary people are seen as doing something wrong (as with guns), regulation or crime law is assumed to be the solution.

When bad things happen in government spaces, like roads, it gets harder to find a rich person or business to blame. So on the roads we have introduced a system of requiring liability insurance, to make sure there’s a big rich business to pay if something goes wrong. As a result, on the road people blame people. That seems a healthier situation to me, and my vouching proposal would try to apply that idea much more widely, to help us return to a world where more often people blame people, rather than people blaming business or government blaming people.

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The Puzzle of Human Sacrifice

Harvey Whitehouse in New Scientist:

Today’s small-scale societies tend to favour infrequent but traumatic rituals that promote intense social cohesion – the kind that is necessary if people are to risk life and limb hunting dangerous animals together. An example would be the agonising initiation rites still carried out in the Sepik region of Papua New Guinea, involving extensive scarification of the body to resemble the skin of a crocodile, a locally revered species. …

With the advent of farming, … [and their] larger populations, … new kinds of rituals seem to have provided that shared identity. These were generally painless practices like prayer and meeting in holy places that could be performed frequently and collectively, allowing them to be duplicated across entire states or empires. …

A puzzle, however, is that many of these early civilisations also practised the brutal ritual of human sacrifice. This reached its zenith in the so-called archaic states that existed between about 3000 BC and 1000 BC, and were among the cruellest and most unequal societies ever. In some parts of the globe, human sacrifice persisted until relatively recently. The Inca religion, for example, had much in common with today’s world religions: people paid homage to their gods with frequent and, for the most part, painless ceremonies. But their rulers had divine status, their gods weren’t moralising and their rituals included human sacrifice right up until they were conquered by the Spanish in the 16th century. …

Instead of helping foster cooperation as societies expanded, Big Gods appeared only after a society had passed a threshold in complexity corresponding to a population of around a million people. … something other than Big Gods allowed societies to grow. … that something was the shift in the nature of rituals from traumatic and rare to painless and repetitive. … human sacrifice was used as a form of social control. The elites – chiefs and shamans – did the sacrificing, and the lower orders paid the price, so it maintained social stability by keeping the masses terrorised and subservient. … the practice started to decline when populations exceeded about 100,000. … 

Piecing all this together, here is what we think happened. As societies grew by means of agricultural innovation, the infrequent, traumatic rituals that had kept people together as small foraging bands gave way to frequent, painless ones. These early doctrinal religions helped unite larger, heterogeneous populations just enough to overcome the free-riding problem and ensure compliance with new forms of governance. However, in doing so they rendered them vulnerable to a new problem: power-hungry rulers. These were the despotic god-kings who presided over archaic states. Granted the divine right to command vast populations, they exploited it to raise militias and priesthoods, shoring up their power through practices we nowadays regard as cruel, such as human sacrifice and slavery. But archaic states rarely grew beyond 100,000 people because they, in turn, became internally unstable and therefore less defensible against invasion.

The societies that expanded to a million or more were those that found a new way to build cooperation – Big Gods. They demoted their rulers to the status of mortals, laid the seeds of democracy and the rule of law, and fostered a more egalitarian distribution of rights and obligations. (more)

It makes sense that complex intense rituals can only work for small societies, while larger societies need simpler rituals that everyone can see or do. It also makes sense that moralizing gods help promote cooperation. But I’m not convinced that we understand any of the rest of these patterns. The human sacrifice part seems to me especially puzzling. I can sort of see how it could serve a function, but I don’t see why that function would be especially effective in societies of population 10-100K.

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Pre-Civilization Egypt

When we look into the distant past, we often compare ourselves to ancient Greeks and Romans. But their peaks were actually closer in time to us than to the peak of the prior society that they compared themselves with: ancient Egypt.

A recent Nature paper had this dramatic graph, showing that most ancient civilizations had a key initial period of rapid increase in social complexity:

Thus in most regions, history can be divided into before and after the start of “civilization.” As writing also usually started around then, we know far less about “pre-historic” life. Those lives are even stranger to us than forager lives, as we have been returning to forager values lately as we’ve gotten rich. For example, before civilization they mostly didn’t have moralizing gods, and human sacrifice (of valued locals, not just enemies) was quite common.

The first known civilization started in Egypt, about 4800 years ago. To better see strange pre-history lives, I’ve listened to a lecture series on ancient Egypt, watched John Romer’s TV series, and read his book, A History of Ancient Egypt, Part I. Here is an interesting graph from that book:

Below the fold is a long list of what I thought were interesting quotes:  Continue reading "Pre-Civilization Egypt" »

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Youth As Abundance

Many technologies and business practice details have changed greatly over the last few centuries. And looking at the specifics of who did what when, much of this change looks like selection and learning. That is, people tried lots of things, some of these worked, and then others copied the winning practices. The whole pattern looks much like a hard to predict random walk.

Many cultural attitudes and values have also changed greatly over those same few centuries. However, the rate, consistency, and predictability of much of this change makes it hard to tell a similar story of selection and learning. This change instead looks more like how many of our individual human behaviors change over our lifespans – the execution of a previously developed strategy. We need not as individuals learn to explore more when young, and exploit more when old, if our genetic and cultural heritage can just tell us to make these changes.

The idea is that some key context, like wealth, has been changing steadily over the last few centuries, and our attitudes have changed steadily in response to that changing context. Just as individuals naturally change their behaviors as they age, cultures may naturally change their attitudes as they get rich. In addition to wealth, other plausibly triggering context factors include increasing health, peace, complexity, work structure, social group size, and alienation from nature.

Even if wealth isn’t the only cause, it seems a big cause, and it likely causes and it caused by other key causes. It also seems quite plausible for humanity to have learned to change our behavior in good times relative to bad times. Note that good time behavior overlaps with, but isn’t quite the same as, how individual behavior changes as individuals get rich, but their society doesn’t. The correlation between individual behavior and wealth is probably influenced a lot by selection: some behaviors tend more to produce individual wealth. Selection has less to do with how a society’s behaviors change as it gets rich.

I’ve written before on a forager vs. farmer account of attitude changes over the last few centuries. Briefly, the social pressures that turned foragers into farmers depended a lot on fear, conformity, and religion, which are complemented by poverty. As we get rich those pressures feel less compelling to us, and we less create such pressures on others. I think this forager-farmer story is helpful, but in this post I want to outline another complementary story: neoteny. One of the main ways that humans are different from other animals is our neoteny; we retrain youthful features and behaviors longer into life. This helps us to be more flexible and also learn more.

Being young is in many ways like living in a rich society. Young people have more physical energy, face less risk of physical damage, and have fewer responsibilities. Which is a lot like being rich. In a rich society you tend live longer, making you effectively younger at any given calendar age. And when young, it makes more sense to be more playful, to learn and explore new possibilities rather than just exploit old skills and possibilities, and to invest more in social connections and in showing off, such as via art, music, stories, or sport. All these also make more sense in good times, when resources are plentiful.

If living in a rich society is a lot like being young, then in makes sense to act more youthful during good times. And so humanity might have acquired the heuristic of thinking and acting more youthful in good times. And that right there can help explain a lot of changes in attitudes and behaviors over the last few centuries. I don’t think it explains quite as many as the back-to-foragers story, but it is very a priori plausible. Not that the forager story is that implausible, but still, priors matter.

From 2006 to 2009, Bruce Charlton wrote a series of articles exploring the idea that people are acting more youthful today:

A child-like flexibility of attitudes, behaviours and knowledge is probably adaptive in modern society because people need repeatedly to change jobs, learn new skills, move to new places and make new friends. (more)

Yes, the world changes more quickly in the industrial era than it did in the farming era, but that rate of change hasn’t increased much in the last century. So this one-time long-ago change in the social rate of change seems a poor explanation for the slow steady trend toward more youthful behavior we’ve seen over the last century. More neoteny as a response to increasing wealth makes more sense to me.

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Overconfidence From Moral Signaling

Tyler Cowen in Stubborn Attachments:

The real issue is that we don’t know whether our actions today will in fact give rise to a better future, even when it appears that they will. If you ponder these time travel conundrums enough, you’ll realize that the effects of our current actions are very hard to predict,

While I think we often have good ways to guess which action is more likely to produce better outcomes, I agree with Tyler than we face great uncertainty. Once our actions get mixed up with a big complex world, it becomes quite likely that, no matter what we choose, in fact things would have turned out better had we made a different choice.

But for actions that take on a moral flavor, most people are reluctant to admit this:

If you knew enough history you’d see >10% as the only reasonable answer, for most any big historical counterfactual. But giving that answer to the above risks making you seem pro-South or pro-slavery. So most people express far more confidence. In fact, more than half give the max possible confidence!

I initially asked a similar question on if the world would have been better off overall if Nazis had won WWII, and for the first day I got very similar answers to the above. But I made the above survey on the South for one day, while I gave two days for the Nazi survey. And in its second day my Nazi survey was retweeted ~100 times, apparently attracting many actual pro-Nazis:

Yes, in principle the survey could have attracted wise historians, but the text replies to my tweet don’t support that theory. My tweet survey also attracted many people who denounced me in rude and crude ways as personally racist and pro-Nazi for even asking this question. And suggested I be fired. Sigh.

Added 13Dec: Many call my question ambiguous. Let’s use x to denote how well the world turns out. There is x0, how well the world actually turned out, and x|A, how well the world have turned out given some counterfactual assumption A. Given this terminology, I’m asking for P(x>x0|A).  You may feel sure you know x0, but you should not feel sure about  x|A; for that you should have a probability distribution.

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