Prediction Markets Update

Prediction markets continue to offer great potential to improve society at many levels. Their greatest promise lies in helping organizations to better aggregate info to enable better key decisions. However, while such markets have consistently performed well in terms of cost, accuracy, ease of use, and user satisfaction, they have also tended to be politically disruptive – they often say things that embarrass powerful people, who get them killed. It is like putting a smart autist in the C-suite, someone who with lots of valuable info but oblivious to the firm’s political landscape. Such an executive just wouldn’t last long, no matter how much they knew.

Like most promising innovations, prediction markets can’t realize their potential until they have been honed and evaluated in a set of increasingly substantial and challenging trials. Abstract ideas must be married to the right sort of complementary details that allow them to function in specific contexts. For prediction markets, real organizations with concrete forecasting needs related to their key decisions need to experiment with different ways to field prediction markets, in search of arrangements that minimize political disruption. (If you know of an organization willing to put up with the disruption that such experimentation creates, I know of a patron willing to consider funding such experiments.)

Alas, few such experiments have been happening. So let me tell you what has been happening instead.

For example, some public markets, such as PredictIt continue to function, and others, such as InTrade, have gone away. While I wish such markets well, I’m not that optimistic about markets on broad public questions, sold to the public, relative to markets on specific organization questions, funded by those organizations. I’m skeptical that there is much public demand for betting on odd questions, or that such markets do much to promote the more promising organizational markets.

Academics and their patrons have showed continued interest, but mostly in the form of abstract efforts such as papers, theorems, and lab experiments. We continue to collect an overhang of abstractly promising mechanisms that haven’t been tried in real organizations. (Including my combinatorial prediction markets.)

A few books have reached wide popular audiences, but mainly by focusing on signs that readers can use to tell themselves that they are better forecasters than their rivals. For example, The Wisdom of Crowds lets ordinary people tell themselves that those damn self-appointed experts are over-rated, compared to we wise crowds. Superforecastors gives several other indicators that readers can collect to tell themselves they are better. While the authors of these books clearly favor creating more markets, they are aware that this isn’t the readers’ priority.

In business, the firms who a decade ago tried to sell straight prediction market software and services to firms for their key decisions have mostly either gone out of business or switched to safer related products. These related products tend to keep the appearance of markets but undercut their main incentive benefits, and they tend to stay away from topics on which key firm insiders might express opinions.

For example, “innovation markets” suggest new research or development projects for firms to pursue. But instead of betting on the consequences of starting such projects, which would take years to see, they just bet on which projects will be funded. And
prediction market research” replaces focus groups with an interface where users seem to bet on which products will be more popular. Except they are just taking a survey, not actually betting on real outcomes.

The last few years has seen great interest in “blockchain” technology and ventures. While these are often used for illegal purposes, authorities have not cracked down as much as they might, as blockchains are new and sexy and promise many non-illegal and useful applications. However, these other applications have been slow in coming. I expect that if blockchains do not soon deliver a majority of their activity as legal laudable applications, authorities will crack down. And while hardcore fans may do what it takes to continue to use them even in the face of such a crackdown, most users will cave and quit, resulting in far lower activity levels.

Many firms have recently issued “crypto coins” to support their blockchain efforts, and current market prices suggest that speculators see a substantial (even if low) chance of large future blockchain activity levels. Even if the underlying technology has promise, such market prices can still be in error in either direction, and it is my opinion that such prices are now too high.

Prediction markets are one of the most frequently mentioned blockchain applications, and I’ve advised several related ventures. As sports betting seems one of the most likely uses of such blockchain based prediction markets, it isn’t clear that activity in this area will count as the legal laudible applications that blockchains need to survive. Even so, many are pursuing this possibility.

Overall blockchain based ventures tend to be heavy on algorithms and software, and light on all the other inputs needed to make a successful business venture. Many of these ventures seek to create general platforms, and hope that other more specific ventures will fill in the more specific business details.

The first firm to issue a blockchain prediction market coin was Augur, two years ago. They still haven’t delivered their software product, but they say they are close, and hopefully extra quality and reliability will result from their extra effort.

Gnosis issued their coin back in April, and they also say they are near ready to deliver their software. They also plan to do a set of experiments to test decision market variations. Both Augur and Gnosis are focused on creating general platforms, expecting others to fill in the specific betting topics and to do the marketing to attract specific customers.

Stox issued its prediction market coin last month. They say they plan more to team with existing trading websites: ”Stox incentivizes other industry leaders with existing customer bases, like invest.com, to join the Stox network and drive traffic to the network.” But they have yet to announce specific teaming deals.

Enjin is issuing its coin now. Instead of supporting prediction markets, Enjin says its coin makes it easier to trade assets from games like Minecraft, even without the support of the makers of such games.

I wish all these ventures well, though I fear a blockchain price crash is coming soon, and I wish there was more of a focus on selling organization over amateur prediction markets, and on particular business applications, rather than general software platforms. But it isn’t yet too late for someone to start to focus there.

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Prepare for Nuclear Winter

If a 1km asteroid were to hit the Earth, the dust it kicked up would block most sunlight over most of the world for 3 to 10 years. There’s only a one in a million chance of that happening per year, however. Whew. However, there’s a ten times bigger chance that a super volcano, such as the one hiding under Yellowstone, might explode, for a similar result. And I’d put the chance of a full scale nuclear war at ten to one hundred times larger than that: one in ten thousand to one thousand per year. Over a century, that becomes a one to ten percent chance. Not whew; grimace instead.

There is a substantial chance that a full scale nuclear war would produce a nuclear winter, with a similar effect: sunlight is blocked for 3-10 years or more. Yes, there are good criticisms of the more extreme forecasts, but there’s still a big chance the sun gets blocked in a full scale nuclear war, and there’s even a substantial chance of the same result in a mere regional war, where only 100 nukes explode (the world now has 15,000 nukes).

I’ll summarize this as saying we face roughly a one in 10,000 chance per year of most all sunlight on Earth being blocked for 5 to 10 years. Which accumulates to become a 1% chance per century. This is about as big as your one in 9000 personal chance each year of dying in a car accident, or your one in 7500 chance of dying from poisoining. We treat both of these other risks as nontrivial, and put substantial efforts into reducing and mitigating such risks, as we also do for many much smaller risks, such as dying from guns, fire, drowning, or plane crashes. So this risk of losing sunlight for 5-10 years seems well worth reducing or mitigating, if possible.

Even in the best case, the world has only enough stored food to feed everyone for about a year. If the population then gradually declined due to cannibalism of the living, the population falls in half every month, and we’d all be dead in a few years. To save your family by storing ten years of food, you not only have to spend a huge sum now, you’d have to stay very well hidden or defended. Just not gonna happen.

Yeah, probably a few people live on, and so humanity doesn’t go extinct. But the only realistic chance most of us have of surviving in this scenario is to use our vast industrial and scientific abilities to make food. We actually know of many plausible ways to make more than enough food to feed everyone for ten years, even with no sunlight. And even if big chunks of the world economy are in shambles. But for that to work, we must preserve enough social order to make use of at least the core of key social institutions.

Many people presume that as soon as everyone hears about a big problem like this, all social institutions immediately collapse and everyone retreats to their compound to fight a war of all against all, perhaps organized via local Mad-Max-style warlords. But in places where this happens, everyone dies, or moves to places where something else happens.

Many take this as an opportunity to renew their favorite debate, on the right roles for government in society. But while there are clearly many strong roles for government to play in such a situation, it seems unlikely that government can smoothly step into all of the roles required here. Instead, we need an effective industry, to make food, collect its inputs, allocate its workers, and distribute its products. And we need to prepare enough to allow a smooth transition in a crisis; waiting until after the sunlights goes to try to plan this probably ends badly.

Thus while there are important technical aspects of this problem, the core of the problem is social: how to preserve functioning social institutions in a crisis. So I call to social scientist superheroes: we light the “bat signal”, and call on you to apply your superpowers. How can we keep enough peace to make enough food, so we don’t all starve, if Earth loses sunlight for a decade?

To learn more on making food without sunlight, see ALLFED.

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Too Much of a Good Thing

When people are especially eager to show allegiance to moral allies, they often let themselves be especially irrational. They try not to let this show, but most aren’t very good at hiding it. One cute way to watch this behavior is to ask people if it is possible to have too much of a good thing, or too little of a bad thing. The fully rational answer is of course yes, it is usually possible to go too far in most any direction. But many seem to fear seeming disloyal if they admit this.

For example, I recently gave this poll to my twitter followers:

One of my followers, who has many more followers than I, asked hers a related poll:

While my and Aella’s followers similarly say we do too little on global warming, hers are far more likely to say that isn’t possible to do too much. And my followers who think we do too much tend to be less reasonable in that more of them think it isn’t possible to do too little.

(Note that the third option in Aella’s poll is a logical contradiction: if people actually do too much, surely it must be possible to do too much. )

This seems ripe for a larger more representative poll. Which side is more reasonable in admitting that one could go too far in their direction? And which other kinds of people are more reasonable? How does this can’t-have-too-much effect vary with the topic?

Added 3:30p: If you can understand the first question, on if we do too much or little, you should be able to understand the second question, on if such things are possible. I don’t get how you can be confused about the meaning of the second question, yet can easily answer the first question.

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MRE Futures, To Not Starve

The Meal, Ready-to-Eat – commonly known as the MRE – is a self-contained, individual field ration in lightweight packaging bought by the United States military for its service members for use in combat or other field conditions where organized food facilities are not available. While MREs should be kept cool, they do not need to be refrigerated. .. MREs have also been distributed to civilians during natural disasters. .. Each meal provides about 1200 Calories. They .. have a minimum shelf life of three years. .. MREs must be able to withstand parachute drops from 380 metres, and non-parachute drops of 30 metres. (more)

Someday, a global crisis, or perhaps a severe regional one, may block 10-100% of the normal food supply for up to several years. This last week I attended a workshop set up by ALLFED, a group exploring new food sources for such situations. It seems that few people need to starve, even if we lose 100% of food for five years! And feeding everyone could go a long way toward keeping such a crisis from escalating into a worse catastrophic or existential risk. But for this to work, the right people, with the means and will to act, need to be aware of the right options at the right time. And early preparation, before a crisis, may go a long way toward making this feasible. How can we make this happen?

In this post I will outline a plan I worked out at this workshop, a plan intended to simultaneously achieve several related goals:

  1. Support deals for food insurance expressed in terms that ordinary people might understand and trust.
  2. Create incentives for food producers, before and during a crisis, to find good local ways to make and deliver food.
  3. Create incentives for researchers to find new food sources, develop working processes, and demonstrate their feasibility.
  4. Share information about the likelihood and severity of food crises in particular times, places, and conditions.

My idea starts with a new kind of MRE, one inspired by but not the same as the familiar military MRE. This new MRE would also be ready to eat without cooking, and also have minimum requirements for calories (after digesting), nutrients, lack of toxins, shelf life, and robustness to shocks. But, and this is key, suppliers would be free to meet these requirements using a wide range of exotic food options, including bacteria, bugs, and rats. (Or more conventional food made in unusual ways, like sugar from corn stalks or cows eating tree leaves.) It is this wide flexibility that could actually make it feasible to feed most everyone in a crisis. MREs might be graded for taste quality, perhaps assigned to three different taste quality levels by credentialed food tasters.

As an individual, you might want access to a source of MREs in a crisis. So you, or your family, firm, club, city, or nation, may want to buy or arrange for insurance which guarantees access to MREs in a crisis. A plausible insurance deal might promise access to so many MREs of a certain quality level per per time period, delivered at standard periodic times to a standard location “near” you. That is, rather than deliver MREs to your door on demand, you might have to show up at a certain more central location once a week or month to pick up your next batch of MREs.

The availability of these MREs might be triggered by a publicly observable event, like a statistical average of ordinary food prices over some area exceeding a threshold. Or, more flexibly, standard MRE insurance might always give one the right to buy, at a pre-declared high price and at standard places and times, a certain number of MREs per time period.  Those who fear not having enough cash to pay this pre-declared MRE price in a crisis might separately arrange for straight financial insurance, which pays cash tied either to a publicly triggered event, or to a market MRE price. Or the two approaches could be combined, so that MRE are available at a standard price during certain public events.

The organizations that offer insurance need ways to ensure customers that they can actually deliver on their promises to offer MREs at the stated times, places, and prices, given relevant public events. In addition, they want to minimize the prices they pay for these supplies of MREs, and encourage suppliers to search for low cost ways to make MREs.

This is where futures markets could help. In a futures market for wheat, people promise to deliver, or to take delivery, of certain quantities of certain types of wheat at particular standard times and places. Those who want to ensure a future supply of wheat against risks of changing prices can buy these futures, and those who grow wheat can ensure a future revenue for their wheat by selling futures. Most traders in futures markets are just speculating, and so arrange to leave the market before they’d have to make or take delivery. But the threat of making or taking delivery disciplines the prices that they pay. Those who fail to make or take delivery as promised face large financial and other penalties.

Analogously, those who offer MRE insurance could use MRE futures markets to ensure an MRE supply, and convince clients that they have ensured a supply. Yes, compared to the terms of the insurance offered by insurance organizations, the futures markets may offer fewer standard times, places, quality levels, and triggering public events. (Though the lab but not field tested tech of combinatorial markets make feasible far more combinations.) Even so, customers might find it easy to believe that, if necessary, an organization that has bought futures for a few standard times and places could actually take delivery of these futures contracts, store the MREs for short periods, and deliver them to the more numerous times and places specified in their insurance deals.

MRE futures markets could also ensure firms who explore innovative ways to make MREs of a demand for their product. By selling futures to deliver MREs at the standard times and places, they might fund their research, development, and production. When it came time to actually deliver MREs, they might make side deals with local insurance organizations to avoid any extra storage and transport costs of actually transferring MREs according to the futures contract details.

To encourage innovation, and to convince everyone that the system actually works, some patron, perhaps a foundation or government, could make a habit of periodically but randomly annoucning large buy orders for MRE futures at certain times and places in the near future. They actually take delivery of the MREs, and then auction them off to whomever shows up there then to taste the MREs at a big social event. In this way ordinary people can sometimes hold and taste the MREs, and we can all see that there is a system capable of producing and delivering at least modest quantities on short notice. The firms who supply these MREs will of course have to set up real processes to actually deliver them, and be paid big premiums for their efforts.

These new MREs may not meet current regulatory requirements for food, and it may not be easy to adapt them to meet such requirements. Such requirements should be relaxed in a crisis, via a new crisis regulatory regime. It would be better to set that regime up ahead of time, instead of trying to negotiate it during a crisis. Such a new regulatory regime could be tested during these periodic random big MRE orders. Regulators could test the delivered MREs and only let people eat the ones that pasts their tests. Firms that had passed tests at previous events might be pre-approved for delivering MREs to future events, at least if they didn’t change their product too much. And during a real crisis, such firms could be pre-approved to rapidly increase production and delivery of their product. This offers an added incentive for firms to participate in these tests.

MRE futures markets might also help the world to coordinate expectations about which kinds of food crises might appear when under what circumstances. Special conditional futures contracts could be created, where one only promises to deliver MREs given certain world events or policies. If the event doesn’t happen, you don’t have to deliver. The relative prices of future contracts for different events and policies would reveal speculator expectations about how the chance and severity of food crises depend on such events and policies.

And that’s my big idea. Yes it will cost real resources, and I of course hope we never have to use it in a real crisis. But it seems to me far preferable to most of us starving to death. Far preferable.

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Tegmark’s Book of Foom

Max Tegmark says his new book, Life 3.0, is about what happens when life can design not just its software, as humans have done in Life 2.0, but also its hardware:

Life 1.0 (biological stage) evolves its hardware and software
Life 2.0 (cultural stage) evolves its hardware, designs much of its software
Life 3.0 (technological stage): designs its hardware and software ..
Many AI researchers think that Life 3.0 may arrive during the coming century, perhaps even during our lifetime, spawned by progress in AI. What will happen, and what will this mean for us? That’s the topic of this book. (29-30)

Actually, its not. The book says little about redesigning hardware. While it says interesting things on many topics, its core is on a future “singularity” where AI systems quickly redesign their own software. (A scenario sometimes called “foom”.)

The book starts out with a 19 page fictional “scenario where humans use superintelligence to take over the world.” A small team, apparently seen as unthreatening by the world, somehow knows how to “launch” a “recursive self-improvement” in a system focused on “one particular task: programming AI Systems.” While initially “subhuman”, within five hours it redesigns its software four times and becomes superhuman at its core task, and so “could also teach itself all other humans skills.”

After five more hours and redesigns it can make money by doing half of the tasks at Amazon Mechanical Turk acceptably well. And it does this without having access to vast amounts of hardware or to large datasets of previous performance on such tasks. Within three days it can read and write like humans, and create world class animated movies to make more money. Over the next few months it goes on to take over the news media, education, world opinion, and then the world. It could have taken over much faster, except that its human controllers were careful to maintain control. During this time, no other team on Earth is remotely close to being able to do this.

Tegmark later explains: Continue reading "Tegmark’s Book of Foom" »

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Forager v Farmer, Elaborated

Seven years ago, after a year of reading up on forager lives, I first started to explore a forager vs. farmer axis:

A lot of today’s political disputes come down to a conflict between farmer and forager ways, with forager ways slowly and steadily winning out since the industrial revolution. It seems we acted like farmers when farming required that, but when richer we feel we can afford to revert to more natural-feeling forager ways. The main exceptions, like school and workplace domination and ranking, are required to generate industry-level wealth. (more)

Recently I decided to revisit the idea, to see if I could find a clearer story that accounts better for many related patterns. Here is what I’ve come up with.

Our primate ancestors lived in a complex Machiavellian social world, with many nested levels of allies each coordinating to oppose outside rival groups of allies, often via violence. Humans, however, managed to collapse most of those levels into one: what Boehm has called a “reverse dominance hierarchy.” Human bands were mostly on good terms with neighboring bands, who they met infrequently. Inside each band, the whole group used weapons and language to coordinate to enforce shared social norms, to create a peaceful egalitarian safe space.

Individuals who saw a norm violation could tell others, and then the whole band could discuss what to do about it. Once a consensus formed, the band could use weapons to enforce their collective decision. As needed, punishments could escalate from scolding to shunning to exile to death. Common norms included requirements to share food and protection, and bans on violence, giving orders, bragging, and creating subgroup factions.

This worked often, but not always. People retained general Machiavellian social abilities, and usually used them covertly, just out of view of group norm enforcement. But sometimes the power of the collective waned, and then many would switch to acting more overtly Machiavellian. For example, an individual or a pair of allies might become so powerful that they could openly defy the group’s disapproval. Or such a pair might violate norms semi-privately, and use a threat of strong retaliation to dissuade others from openly decrying their violations. Or a nearby rival group might threaten to attack. Or a famine or flood might threaten mass mortality.

In the absence of such threats, the talky collective was the main arena that mattered. Everyone worked hard to look good by the far-view idealistic and empathy-based norms usually favored in collective views. They behaved well when observed, learned to talk persuasively to the group, and made sure to have friends to watch and talk for them. They expressed their emotions, and acted like they cared about others.

When they felt on good terms with the group, people could relax and feel safe. They then become more playful, and acted like animals generally do when playful. Within a bounded safe space, behavior becomes more varied, stylized, artistic, humorous, teasing, self-indulgent, and emotionally expressive. For example, there is more, and more varied, music and dance. New possibilities are explored.

A feeling of safety includes feeling safe to form more distinct subgroups, without others seeing such subgroups as threatening factions. And that includes feeling safe to form groups that tend to argue together for similar positions within talky collective discussions, and to disagree with the larger group. After all, it is hard for a talky collective to function well unless members are allowed to openly disagree with one another.

But when the group was stressed and threatened by dominators, outsiders, or famine, the collective view mattered less, and people reverted to more general Machiavellian social strategies. Then it mattered more who had what physical resources and strength, and what personal allies. People leaned toward projecting toughness instead of empathy. And they demanded stronger signals of loyalty, such as conformity, and were more willing to suspect people of disloyalty. Subgroups and non-conformity became more suspect, including subgroups that consistently argued together for unpopular positions.

And here is the key idea: individuals vary in the thresholds they use to switch between focusing on dealing with issues via an all-encompassing norm-enforcing talky collective, and or via general Machiavellian social skills, mediated by personal resources and allies. Everyone tends to switch together to a collective focus as the environment becomes richer and safer. (This is one of the many ways that behaviors and values consistently change with wealth.) But some switch sooner: those better at working the collective, such as being better at talking and empathy, and those who gain more from collective choices, such as physically weaker folks who can’t hunt or gather as well. And also people just generally less prone to feeling afraid as a result of ambiguous cues.

People who feel less safe are more afraid of changing whatever has worked in the past, and so hold on more tightly to typical past behaviors and practices. They are more worried about the group damaging the talky collective, via tolerating free riders, allowing more distinct subgroups, and by demanding too much from members who might just up and leave. Also, those who feel less able to influence communal discussions prefer groups norms to be enforced more simply and mechanically, without as many exceptions that will be more influenced by those who are good at talking.

I argue that this key “left vs. right” inclination to focus more vs less on a talky collective is the main parameter that consistently determines who people tend to ally with in large scale political coalitions. Other parameters can matter a lot in different times and places, but this is the one that consistently matters. This parameter doesn’t matter much for how individuals relate to each other personally, and at smaller social scales like clubs or firms, coalitions form more via our general Machiavellian abilities, based on parameters than matter directly in those contexts. But everyone has an intuitive sense for how much we all expect and want big issues to be handled by a talky collective of “everyone” with any power. The first and primary political question is how much to try to resolve issues via a big talky collective, or to let smaller groups decide for themselves.

This account that I’ve just outline does reasonably well at accounting for many known left-right patterns. For example, the right is more conscientious, while the left is more open to experience. The left prefers more varied niche types of sports, movies, and music, while the right prefers fewer standardized types. Artists, musicians, and comedians tend to be on the left. Right sports focus more on physical strength and combat, stronger men have stronger political opinions, and when low status they favor more redistribution. People on the right are less reflective, prefer simpler arguments, are more sensitive to disgust, and startle more easily.

Education elites are more left than business elites. In romance and spirituality, the left tends to favor authentic feelings while the right cares more about standards of behavior. The left is more spiritual while right is more religious. Left jobs focused more on talking and on a high tail of great outcomes, while right jobs focus more on avoiding a low tail of bad outcomes.

The left is more okay with people forming distinct subgroups, even as it thinks more in terms of treating everyone equally, even across very wide scopes, and including wide scopes in more divisive debates. The right wants to make redistribution more conditional, more wants to punish free riders, and wants norm violators to be more consistently punished. The left tends to presume large scale cooperation is feasible, while right tends to presume competition more. The left hopes for big gains from change while the right worries about change damaging things that now work.

Views tend to drift leftward as nations and the world get richer. Left versus right isn’t very useful for predicting individual behavior outside of politics, even as it is the main parameter that robustly determines large scale political inclinations. People tend to think differently about politics on what they see as the largest scales; for example, there are whole separate fields of political science and political philosophy, which don’t overlap much with fields dealing with smaller scale politics, such as in clubs and firms.

I shouldn’t need to say it but I will anyway: it is obvious that a safe playful talky collective is sometimes but not always the best way to deal with things. Its value varies with context. So sometimes those who are more reluctant to invoke it are right to be wary, while at other times those who are eager to apply it are right to push for it. It is not obvious, at least to me, whether on average the instincts of the left or the right are more helpful.

I’ve noted before that if one frames left attitudes as better when the world is safe, while right attitudes as better when world is harsh, the longer is the timescale on which you evaluate outcomes, the harsher is the world.

Added 9Sept: This post didn’t say much directly about farmers. In the much larger farmer social groups, simple one layer talky collectives were much less feasible. Farmer lives had new dangers of war and disease, and neighboring groups were more threatening. The farmer world more supported property in spouses and material goods and had more social hierarchies, farmer law less relied on a general discussion of each accused, and more reliable food meant there was less call for redistribution. Farmers worked more and had less time for play.  Together, these tended to reduce the scope of safe playful talky collectives, moving society in a rightward direction relative to foragers.

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Meaning is Easy to Find, Hard to Justify

One of the strangest questions I get when giving talks on Age of Em is a variation on this:

How can ems find enough meaning in their lives to get up and go to work everyday, instead of committing suicide?

As the vast majority of people in most every society do not commit suicide, and manage to get up for work on most workdays, why would anyone expect this to be a huge problem in a random new society?

Even stranger is that I mostly get this question from smart sincere college students who are doing well at school. And I also hear that such students often complain that they do not know how to motivate themselves to do many things that they “want” to do. I interpret this all as resulting from overly far thinking on meaning. Let me explain.

If we compare happiness to meaning, then happiness tends to be an evaluation of a more local situation, while meaning tends to be an evaluation of a more global situation. You are happy about this moment, but you have meaning regarding your life.

Now you can do either of these evaluations in a near or a far mode. That is, you can just ask yourself for your intuitions on how you feel about your life, within over-thinking it, or you can reason abstractly and idealistically about what sort of meaning you should have or can justify having. In that later more abstract mode, smart sincere people can be stumped. How can they justify having meaning in a world where there is so much randomness and suffering, and that is so far from being a heaven?

Of course in a sense, heaven is an incoherent concept. We have so many random idealistic constraints on what heaven should be like that it isn’t clear that anything can satisfy them all. For example, we may want to be the hero of a dramatic story, even if we know that characters in such stories wish that they could live in more peaceful worlds.

Idealistic young people have such problems in spades, because they haven’t lived long enough to see how unreasonable are their many idealistic demands. And smarter people can think up even more such demands.

But the basic fact is that most everyone in most every society does in fact find meaning in their lives, even if they don’t know how to justify it. Thus I can be pretty confident that ems also find meaning in their lives.

Here are some more random facts about meaning, drawn from my revised Age of Em, out next April.

Today, individuals who earn higher wages tend to have both more happiness and a stronger sense of purpose, and this sense of purpose seems to cause higher wages. People with a stronger sense of purpose also tend to live longer. Nations that are richer tend to have more happiness but less meaning in life, in part because they have less religion. .. Types of meaning that people get from work today include authenticity, agency, self-worth, purpose, belonging, and transcendence.

Happiness and meaning have different implications for behavior, and are sometimes at odds. That is, activities that raise happiness often lower meaning, and vice versa. For example, people with meaning think more about the future, while happy people focus on the here and now. People with meaning tend to be givers who help others, while happy people tend to be takers who are helped by others. Being a parent and spending time with loved ones gives meaning, but spending time with friends makes one happy.

Affirming one’s identity and expressing oneself increase meaning but not happiness. People with more struggles, problems, and stresses have more meaning, but are less happy. Happiness but not meaning predicts a satisfaction of desires, such as for health and money, and more frequent good relative to bad feelings. Older people gain meaning by giving advice to younger people. We gain more meaning when we follow our gut feelings rather than thinking abstractly about our situations.

My weak guess is that productivity tends to predict meaning more strongly than happiness. If this is correct, it suggests that, all else equal, ems will tend to think more about the future, more be givers who help others, spend more time with loved ones and less with friends, more affirm their identity and express themselves, give more advice, and follow gut feelings more. But they will also have more struggles and less often have their desires satisfied.

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Entrenchit Happens

Most artificial systems, made by humans, slowly degrade over time until they become dysfunctional, and are replaced. Such systems rarely change or improve over time, and so are sometimes replaced while still functional, with new improved competitors.

Many systems, such as organisms and some kinds of firms, try to adapt to changing external conditions. But internal damage accumulates and eventually limits their ability to adapt quickly or well enough, and so they lose out to competitors. Empires may also decline due to internal damage.

Some larger systems, like species, nations, languages, and many kinds of firms, face many similar competitors, and rise and fall in ways that seem so random that it is hard to tell if they suffer much from internal damage, including in their ability to adapt to context.

In contrast, other larger systems face no competitors, at least for a long time, even as they are drawn from large spaces of possible systems. Consider, for example, that the community of mathematicians has created a total system of math that hangs together and is stable in many ways, and yet is drawn from a vastly larger space of possibilities. The space of possible math axioms is astronomical, but mathematicians consistently reuse the same tiny set of axioms. One could say that those axioms have become “entrenced” in math practice.

Many other kinds of widely shared systems have few competitors, and yet entrench a set of specific practices drawn from a much larger space of possibilities. Consider, for example, the DNA code, the basic architectures of cells, and standard methods of making multi-cellular organisms. Or consider the shared features of most human languages, legal systems, financial systems, economic systems, and firm organization. Or even of computer languages and computer architectures. In each of these cases most of the world has long shared the same common set of interrelated practices, even though a vastly larger space of possibilities is known to exist and to have been little explored.

Such shared practices plausibly persist because they are just too much trouble to change. As I wrote last year:

When an architecture is well enough matched to a stable problem, systems build on it can last long, and grow large, because it is too much trouble to start a competing system from scratch. But when different approaches or environments need different architectures, then after a system grows large enough, one is mostly forced to start over from scratch to use a different enough approach, or to function in a different enough environment.

In sum, entrenchment (or “entrenchit”) happens. I mention this to suggest that, as per my last post, known styles of software really could continue to dominate for long into the future. Many seem confident that very different styles will arise relatively soon on a civilizational time scale, and then mostly displace familiar styles. But who thinks we will soon see domination by new very different kinds of math axioms, human languages, legal systems, or world economic systems? Why expect more radical change in software than in most other things?

Yes, sometimes new systems really do arise to displace old ones. But you can’t help but notice that while small systems are often replaced, revolutions to replace interlocking sets of common worldwide practices much rarer. And for such systems there are far more proposed and attempted revolutions than successful ones.

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Can Human-Like Software Win?

Many, perhaps most, think it obvious that computer-like systems will eventually be more productive than human-like systems in most all jobs. So they focus on how humans might maintain control, even after this transition. But this eventuality is less obvious than it seems, depending on what exactly one means by “human-like” or “computer-like” systems. Let me explain.

Today the software that sits in human brains is stuck in human brain hardware, while the other kinds of software that we write (or train) sit in the artificial hardware that we make. And this artificial hardware has been improving rapidly far more rapidly than has human brain hardware. Partly as a result of this, systems of artificial software and hardware have been improving rapidly compared to human brain systems.

But eventually we will find a way to transfer the software from human brains into artificial hardware. Ems are one way to do this, as a relatively direct port. But other transfer mechanics may be developed.

Once human brain software is in the same sort of artificial computing hardware as all the other software, then the relative productivity of different software categories comes down to a question of quality: which categories of software tend to be more productive on which tasks?

Of course there will many different variations available within each category, to match to different problems. And the overall productivity of each category will depend both on previous efforts to develop and improve software in that category, and also on previous investments in other systems to match and complement that software. For example, familiar artificial software will gain because we have spent longer working to match it to familiar artificial hardware, while human software will gain from being well matched to complex existing social systems, such as language, firms, law, and government.

People give many arguments for why they expect human-like software to mostly lose this future competition, even when it has access to the same hardware. For example, they say that other software could lack human biases and also scale better, have more reliable memory, communicate better over wider scopes, be easier to understand, have easier meta-control and self-modification, and be based more directly on formal abstract theories of learning, decision, computation, and organization.

Now consider two informal polls I recently gave my twitter followers:

Surprisingly, at least to me, the main reason that people expect human-like software to lose is that they mostly expect whole new categories of software to appear, categories quite different from both the software in the human brain and also all the many kinds of software with which we are now familiar. If it comes down to a contest between human-like and familiar software categories, only a quarter of them expect human-like to lose big.

The reason I find this surprising is that all of the reasons that I’ve seen given for why human-like software could be at a disadvantage seem to apply just as well to familiar categories of software. In addition, a new category must start with the disadvantages of having less previous investment in that category and in matching other systems to it. That is, none of these are reasons to expect imagined new categories of software to beat familiar artificial software, and yet people offer them as reasons to think whole new much more powerful categories will appear and win.

I conclude that people don’t mostly use specific reasons to conclude that human-like software will lose, once it can be moved to artificial hardware. Instead they just have a general belief that the space of possible software is huge and contains many new categories to discover. This just seems to be the generic belief that competition and innovation will eventually produce a lot of change. Its not that human-like software has any overall competitive disadvantage compared to concrete known competitors; it is at least as likely to have winning descendants as any such competitors. Its just that our descendants are likely to change a lot as they evolve over time. Which seems to me a very different story than the humans-are-sure-to-lose story we usually hear.

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There’s Always Subtext

Our new book, The Elephant in the Brain, argues that hidden motives drive much of our behavior. If so, then to make fiction seem realistic, those who create it will need to be aware of such hidden motives. For example, back in 2009 I wrote:

Impro, a classic book on theatre improvisation, convincingly shows that people are better actors when they notice how status moves infuse most human interactions. Apparently we are designed to be very good at status moves, but to be unconscious of them.

The classic screenwriting text Story, by Robert McKee, agrees more generally, and explains it beautifully:

Text means the sensory surface of a work of art. In film, it’s the images onscreen and the soundtrack of dialogue, music, and sound effects. What we see. What we hear. What people say. What people do. Subtext is the life under that surface – thoughts and feelings both known and unknown, hidden by behavior.

Nothing is what it seems. This principle calls for the screen-writer’s constant awareness of the duplicity of life, his recognition that everything exists on at least two levels, and that, therefore, he must write a simultaneous duality: First, he must create a verbal description of the sensory surface of life, sight and sound, activity and talk. Second, he must create the inner world of conscious and unconscious desire, action and reaction, impulse and id, genetic and experiential imperatives. As in reality, so in fiction: He must veil the truth with a living mask, the actual thoughts and feelings of characters behind their saying and doing.

An old Hollywood expression goes “If the scene is about what the scene is about, you’re in deep shit.” It means writing “on the nose,” writing dialogue and activity in which a character’s deepest thoughts and feelings are expressed by what the character says and does – writing the subtext directly into the text.

Writing this, for example: Two attractive people sit opposite each other at a candlelit table, the lighting glinting off the crystal wineglasses and the dewy eyes of the lovers. Soft breezes billow the curtains. A Chopin nocturne plays in in the background. The lovers reach across the table, touch hands, look longingly in each others’ eyes, say, “I love you, I love you” .. and actually mean it. This is an unactable scene and will die like a rat in the road. ..

An actor forced to do the candlelit scene might attack it like this: “Why have these people done out of their way to create this movie scene? What’s with the candlelight, soft music, billowing curtains? Why don’t they just take their pasta to the TV set like normal people? What’s wrong with this relationship? Because isn’t that life? When do the candles come out? When everything’s fine? No. When everything’s fine we take our pasta to the TV set like normal people. So from that insight the actor will create a subtext. Now as we watch, we think: “He says he loves her and maybe he does, but look, he’s scared of losing her. He’s desperate.” Or from another subtext: “He says he loves her, but look, he’s setting her up for bad news. He’s getting ready to walk out.”

The scene is not about what it seems to be about. Its about something else. And it’s that something else – trying to regain her affection or softening her up for the barkeep – that will make the scene work. There’s always a subtext, and inner life that contrasts with or contradicts the text. Given this, the actor will create a multi layered work that allows us to see through the text to the truth that vibrates beyond the eyes, voice and gestures of life. ..

In truth, it’s virtually impossible for anyone, even the insane, to fully express what’s going on inside. No matter how much we wish to manifest our deepest feelings, they elude us. We never fully express the truth, for in fact we rarely know it. .. Nor does this mean that we can’t write powerful dialogue in which desperate people try to them the truth. It simply means that the most passionate moments must conceal an even deeper level. ..

Subtext is present even when a character is alone. For if no one else is watching us, we are. We wear masks to thinner our true selves from ourselves. Not only do individuals wear masks, but institutions do as well and hire public relations experts to keep them in place. (pp.252-257)

Added 17Sep: More on subtext of sound and images:

The power of an organized return of images is immense, as variety and repetition drive the Image System to the seat of the audiences unconscious. Yet, and most important, a film’s poetics must be handled with virtual invisibility and go consciously unrecognized. (p.402) ..

Symbolism is powerful, more powerful than most realize, as long as it bypasses the conscious mind and slips into the unconscious. As it does while we dream. The use of symbolism follows the same principle as scoring a film. Sound doesn’t need cognition, and music can deeply affect us when we’re unconscious of it. In the same way, symbols touch us and move us – as long as we don’t recognize them as symbolic. Awareness of a symbol turns it into a neutral, intellectual curiosity, powerless and virtually meaningless. (p.407)

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