Author Archives: Robin Hanson

We Agree On So Much

In a standard Bayesian model of beliefs, an agent starts out with a prior distribution over a set of possible states, and then updates to a new distribution, in principle using all the info that agent has ever acquired. Using this new distribution over possible states, this agent can in principle calculate new beliefs on any desired topic. 

Regarding their belief on a particular topic then, an agent’s current belief is the result of applying their info to update their prior belief on that topic. And using standard info theory, one can count the (non-negative) number of info bits that it took to create this new belief, relative to the prior belief.  (The exact formula is Sumi pi ln(pi/qi), where pi is the new belief, qi is the prior, and i ranges over possible answers to this topic question.)  

How much info an agent acquires on a topic is closely related to how confident they become on that topic. Unless a prior starts out very confident, high confidence later can only come via updating on a great many info bits. 

Humans typically acquire vast numbers of info bits over their lifetime. By one estimate, we are exposed to 34GB per day. Yes, as a practical matter we can’t remotely make full use of all this info, but we do use a lot of it, and so our beliefs do over time embody a lot of info. And even if our beliefs don’t reflect all our available info, we can still talk about the number of bits are embodied in any given level of confidence an agent has on a particular topic. 

On many topics of great interest to us, we acquire a huge volume of info, and so become very confident. For example, consider how confident you are at the moment about whether you are alive, whether the sun is shining, that you have ten fingers, etc. You are typically VERY confident about such things, because have access to a great many relevant bits.

On a great many other topics, however, we hardly know anything. Consider, for example, many details about the nearest alien species. Or even about the life of your ancestors ten generations back. On such topics, if we put in sufficient effort we may be able to muster many very weak clues, clues that can push our beliefs in one direction or another. But being weak, these clues don’t add up to much; our beliefs after considering such info aren’t that different from our previous beliefs. That is, on these topics we have less than one bit of info. 

Let us now collect a large broad set of such topics, and ask: what distribution should we expect to see over the number of bits per topic? This number must be positive, for many familiar topics it is much much larger than one, while for other large sets of topics, it is less than one. 

The distribution most commonly observed for numbers that must be positive yet range over many orders of magnitude is: lognormal. And so I suggest that we tentatively assume a (large-sigma) lognormal distribution over the number of info bits that an agent learns per topic. This may not be exactly right, but it should be qualitatively in the ballpark.  

One obvious implication of this assumption is: few topics have nearly one bit of info. That is, most topics are ones where either we hardly know anything, or where we know so much that we are very confident. 

Note that these typical topics are not worth much thought, discussion, or work to cut biases. For example, when making decisions to maximize expected utility, or when refining the contribution that probabilities on one topic make to other topic probabilities, getting 10% of one’s bits wrong just won’t make much of difference here. Changing 10% of 0.01 bit makes still leaves one’s probabilities very close to one’s prior. And changing 10% of a million bits still leaves one with very confident probabilities.  

Only when the number of bits on a topic is of order unity do one’s probabilities vary substantially with each bit, or with 10% of one’s bits. These are the topics where it can be worth paying a fixed cost per topic to refine one’s probabilities, either to help make a decision or to help update other probability estimates. And these are the topics where we tend to think, talk, argue, and worry about our biases.

It makes sense that we tend to focus on pondering such “talkable topics”, where such thought can most improve our estimates and decisions. But don’t let this fool you into thinking we hardly agree on anything. For the vast majority of topics, we agree either that we hardly know anything, or that we quite confidently know the answer. We only meaningfully disagree on the narrow range of topics where our info is on the order of one bit, topics where it is in fact worth the bother to explore our disagreements. 

Note also that for these key talkable topics, making an analysis mistake on just one bit of relevant info is typically sufficient to induce large probability changes, and thus large apparent disagreements. And for most topics it is quite hard to think and talk without making at least one bit’s worth of error. Especially if we consume 34GB per day! So its completely to be expected that we will often find ourselves disagreeing on talkable topics at the level of few bits.

So maybe cut yourself and others a bit more slack about your disagreements? And maybe you should be more okay with our using mechanisms like betting markets to average out these errors. You really can’t be that confident that it is you who has made the fewest analysis errors. 

GD Star Rating
Tagged as:


A wide-ranging review of research … rocked psychology because it showed experience simply did not create skill in a wide range of real-world scenarios, from college administrators assessing student potential to psychiatrists predicting patient performance to human resources professionals deciding who will succeed in job training. In those domains, which involved human behavior and where patterns did not clearly repeat, repetition did not cause learning. Chess, golf, and firefighting are exceptions, not the rule. …

In wicked domains, the rules of the game are often unclear or incomplete, there may or may not be repetitive patterns and they may not be obvious, and feedback is often delayed, inaccurate, or both. In the most devilishly wicked learning environments, experience will reinforce the exact wrong lessons. (more)

David Epstein’s book Range is a needed correction to other advice often heard lately, that the secret of life success is to specialize as early as possible. While early specializing works in some areas, more commonly one learns more by ranging more widely, collecting analogies and tools which can be applied too many new problems, and better learning which specialties fits you best.

I’ve done a lot of wide ranging in my life, so I naturally like this advice. However, as one can obviously take this advice too far, the hard question is how widely to range for how long, and then how quickly to narrow when.

Alas, Epstein seems less useful on this hard tradeoff question. He does make it plausible that your chance of achieving the very highest success in creative areas like art or research is maximized by a wider range than is typical. But as most people have little chance of reaching such heights, this doesn’t say much to them.

I’m struck by the fact that all of his concrete examples of wide rangers who succeeded are people who at some point specialized to enough gain status within a particular speciality area. He gives stats which suggest that wide rangers continue to be productive and useful to society even if they never specialize so much, but those people are apparently not seen as personal successes.

For example, Epstein cites a study showing that innovative academic papers which cite journals never before cited in the same paper are published at first in less prestigious journals, but eventually get more citations. Yet in fields like economics, status depends much more on journal prestige than eventual citations.

So while you might contribute more to the world by continuing to range widely, you often succeed more personally by ranging somewhat widely at first, and then specializing enough to make specialists see you as one of them.

The hard problem then is how to get specialists to credit you for advancing their field when they don’t see you as a high status one of them. Epstein quotes people who say we should just fund all research topics even if they don’t seem promising, but that obviously just won’t work.

GD Star Rating
Tagged as: ,

Stephenson’s Em Fantasy

Neal Stephenson’s Snow Crash (’92) and Diamond Age (’95) were once some of my favorite science fiction novels. And his Anathem (’08) is the very favorite of a friend. So hearing that his new book Fall; or, Dodge in Hell (’19) is about ems, I had to read it. And given that I’m author of Age of Em and care much for science fiction realism, I had to evaluate this story in those terms. (Other reviews don’t seem to care: 1 2 3 4 5)

Alas, in terms of em realism, this book disappoints. To explain, I’m going to have to give spoilers; you are warned. Continue reading "Stephenson’s Em Fantasy" »

GD Star Rating
Tagged as: ,

Decision Markets for Monetary Policy

The goals of monetary policy are to promote maximum employment, stable prices and moderate long-term interest rates. By implementing effective monetary policy, the Fed can maintain stable prices, thereby supporting conditions for long-term economic growth and maximum employment. (more)

Caltech, where I got my PhD in social science, doesn’t have specialists in macroeconomics, and they don’t teach the subject to grad students. They just don’t respect the area enough, they told me. And I haven’t gone out of my way to make up this deficit in my background; other areas have seemed more interesting. So I mostly try not to have or express opinions on macroeconomics

I periodically hear arguments for NGDP Targeting, such as from Scott Sumner, who at one point titles his argument “How Prediction Markets Can Improve Monetary Policy: A Case Study.” But as far as I can tell, while this proposal does use market prices in some ways, it depends more on specific macroeconomic beliefs than a prediction markets approach needs to. 

These specific beliefs may be well supported beliefs, I don’t know. But, I think it is worth pointing out that if we are willing to consider radical changes, we could instead switch to an approach that depends less on particular macroeconomic beliefs: decision markets. Monetary policy seems an especially good case to apply decision markets because they clearly have two required features: 1) A clear set of discrete decision options, where it is clear afterward which option was taken, 2) A reasonably strong consensus on measurable outcomes that such decisions are trying to increase. 

That is, monetary policy consists of clear public and discrete choices, such as on short term interest rates. Call each discrete choice option C. And it is widely agreed that the point of this policy is to promote long term growth, in part via moderating the business cycle. So some weighted average of real growth, inflation, unemployment, and perhaps a few more after-the-fact business cycle indicators, over the next decade or two seems a sufficient summary of the desired outcome. Let’s call this summary outcome O.  

So monetary policy just needs to pick a standard metric O that will be known in a decade or two, estimate E[O|C] for each choice C under consideration, and compare these estimates. And this is exactly the sort of thing that decisions markets can do well. There are some subtitles about how exactly to do it best. But many variations should work pretty well. 

For example, I doubt it matters that much how exactly we weight the contributions to O. And to cut off skepticism on causality, we could use a 1% chance of making each discrete choice randomly, and have decision market estimates be conditional on that random choice. Suffering a 1% randomness seems a pretty low cost to cut off skepticism.

For more, see the section “Monetary Policy Example” in my paper Shall We Vote on Values, But Bet on Beliefs?

GD Star Rating
Tagged as:

Our Prestige Obsession

Long ago our distant ancestors lived through both good times and bad. In bad times, they did their best to survive, while in good times they asked themselves, “What can I invest in now to help me in coming bad times?” The obvious answer was: good relations and reputations. So they had kids, worked to raise their personal status, and worked to collect and maintain good allies.

This has long been my favored explanation for why we now invest so much in medicine and education, and why those investment have risen so much over the last century. We subconsciously treat medicine as a way to show that we care about others, and to let others show they care about us. As we get richer, we devote a larger fraction of our resources to this plan, and to other ways of showing off.

I’d never thought about it until yesterday, but this theory also predicts that, as we get rich, we put an increasing priority on associating with prestigious doctors and teachers. In better times, we focus more on gaining prestige via closer associations with more prestigious people. So as we get rich, we not only spend more on medicine, we more want that spending to connect us to especially prestigious medical professionals.

This increasing-focus-on-prestige effect can also help us to understand some larger economic patterns. Over the last half century, rising wage inequality has been driven to a large extent by a limited number of unusual services, such as medicine, education, law, firm management, management consulting, and investment management. And these services tend to share a common pattern.

As a fraction of the economy, spending on these services has increased greatly over the last half century or so. The public face of each service tends to be key high status individuals, e.g., doctors, teachers, lawyers, managers, who are seen as driving key service choices for customers. Customers often interact directly with these faces, and develop personal relations with them. There are an increasing number of these key face individuals, their pay is high, and it has been rising faster than has average pay, contributing to rising wage inequality.

For each of these services, we see customers knowing and caring more about the prestige of key service faces, relative to their service track records. Customers seem surprisingly disinterested in big ways in which these services are inefficient and could be greatly improved, such as via tech. And these services tend to be more highly regulated.

For example, since 1960, the US has roughly doubled its number of doctors and nurses, and their pay has roughly tripled, a far larger increase than seen in median pay. As a result, the fraction of total income spent on medicine has risen greatly. Randomized trials comparing paramedics and nurse practitioners to general practice doctors find that they all produce similar results, even though doctors cost far more. While student health centers often save by having one doctor supervise many nurses who do most of the care, most people dislike this and insist on direct doctor care.

We see very little correlation between having more medicine and more health, suggesting that there is much excess care and inefficiency. Patients prefer expensive complex treatments, and are suspicious of simple cheap treatments. Patients tend to be more aware of and interested in their doctor’s prestigious schools and jobs than of their treatment track record. While medicine is highly regulated overall, the much less regulated world of animal medicine has seen spending rise a similar rate.

In education, since 1960 we’ve seen big rises in the number of students, the number of teachers and other workers per student, and in the wages of teachers relative to worker elsewhere. Teachers make relatively high wages. While most schools are government run, spending at private schools has risen at a similar rate to public schools. We see a strong push for more highly educated teachers, even though teachers with less schooling seem adequate for learning. Students don’t actually remember much of what they are taught, and most of what they do learn isn’t actually useful. Students seem to know and care more about the prestige of their teachers than about their track records at teaching. College students prefer worse teachers who have done more prestigious research.

In law, since 1960 we’ve similarly seen big increases in the number of court cases, the number of lawyers employed, and in lawyer incomes. While two centuries ago most people could go to court without a lawyer, law is now far more complex. Yet it is far from clear whether we are better off with our more complex and expensive legal system. Most customers know far more about the school and job prestige of the lawyers they consider than they do about such lawyers’ court track records.

Management consultants have greatly increased in number and wages. While it is often possible to predict what they would recommend at a lower cost, such consultants are often hired because their prestige can cow internal opponents to not resist proposed changes. Management consultants tend to hire new graduates from top schools to impress clients with their prestige.

People who manage investment funds have greatly increased in number and pay. Once their management fees are taken into account, they tend to give lower returns than simple index funds. Investors seem willing to accept such lower expected returns in trade for a chance to brag about their association should returns happen to be high. They enjoy associating with prestigious fund managers, and tend to insist that such managers take their phone calls, which credibly shows a closer than arms-length relation.

Managers in general have also increased in number and also in pay, relative to median pay. And a key function of managers may be to make firms seem more prestigious, not only to customers and investors, but also to employees. Employees are generally wary of submitting to the dominance of bosses, as such submission violates an ancient forager norm. But as admiring and following prestigious people is okay, prestigious bosses can induce more cooperative employees.

Taken together, these cases suggest that increasing wage inequality may be caused in part by an increased demand for associating with prestigious service faces. As we get rich, we become willing to spend a larger fraction of our income on showing off via medicine and schooling, and we put higher priority on connecting to more prestigious doctors, teachers, lawyers, managers, etc. This increasing demand is what pushes their wages high.

This demand for more prestigious service faces seems to not be driven by a higher productivity that more prestigious workers may be able to provide. Customers seem to pay far less attention to productivity than to prestige; they don’t ask for track records, and they seem to tolerate a great deal of inefficiency. This all suggests that it is prestige more directly that customers seek.

Note that my story is somewhat in conflict with the usual “skill-biased technical change” story, which says that tech changed to make higher-skilled workers more productive relative to lower-skilled workers.

Added 10June: Note that the so-called Baumol “cost disease”, wherein doing some tasks just takes a certain number of hours unaided by tech gains, can only explain spending increases proportional to overall wage increases, and that only if demand is very inelastic. It can’t explain how some wages rise faster than the average, nor big increases in quantity demanded even as prices increases.

Added 12Jun: This post inspired by reading & discussing Why Are the Prices So Damn High?

GD Star Rating
Tagged as: , , , ,

Progeny Probs: Souls, Ems, Quantum

Consider three kinds of ancestry trees: 1) souls of some odd human mothers, 2) ems and their copies, and 3) splitting quantum worlds. In each kind of tree, agents can ask themselves, “Which future version of me will I become?”

SOULS  First, let’s start with some odd human mothers. A single uber-mother can give rise to a large tree of descendants via the mother relation. Each branch in the tree is a single person. The leaves of this tree are branches that lead to no more branches. In this case, leaves are either men, or they are women who never had children. When a mother looks back on her history, she sees a single chain of branches from the uber-mother root of the tree to her. All of those branches are mothers who had at least one child.

Now here is the odd part: imagine that some mothers see their personal historical chain as describing a singular soul being passed down through the generations. They believe that souls can be transferred but not created, and so that when a mother has more than one child, at most one of those children gets a soul.

Yes, this is an odd perspective to have regarding souls, but bear with me. Such an odd mother might wonder which one of her children will inherit her soul. Her beliefs about the answer to this question, and about other facts about this child, might be expressed in a subjective probability distribution. I will call such a distribution a “progeny prob”.

EMS  Second, let’s consider ems, the subject of my book The Age of Em: Work, Love, and Life when Robots Rule the Earth. Ems don’t yet exist, but they might in the future. Each em is an emulation of a particular human brain, and it acts just like that human would in the same subjective situation, even though it actually runs on an artificial computer. Each em is part of an ancestry tree that starts with a root that resulted from scanning a particular human brain.

This em tree branches when copies are made of individual ems, and the leaves of this tree are copies that are erased. Ems vary in many ways, such as in how much wealth they own, how fast their minds run relative to humans, and how long they live before they end or next split into copies. Split events also differ, such as re how many copies are made, what social role each copy is planned to fill, and which copies get what part of the original’s wealth or friends.

An em who looks toward its next future split, and foresees a resulting set of copies, may ask themselves “Which one of those copies will I be?” Of course they will actually become all of those copies. But as human minds never evolved to anticipate splitting, ems may find it hard to think that way. The fact that ems remember only one chain of branches in the past can lead them to think in terms of continuing on in only one future branch. Em “progeny prob” beliefs about who they will become can also include predictions about life details of that copy, such as wealth or speed. These beliefs can also be conditional on particular plans made for this split, such as which copies plan to take which jobs.

QUANTUM  Third, let’s consider quantum states, as seen from the many worlds perspective. We start with a large system of interest, a system that can include observers like humans and ems. This system begins in some “root” quantum state, and afterward experiences many “decoherence events”, with each such event aligned to a particular key parameter, like the spatial location of a particular atom. Soon after each such decoherence event, the total system state typically becomes closely approximated by a weighted sum of component states. Each component state is associated with a different value of the key parameter. Each subsystem of such a component state, including subsystems that describe the mental states of observers, have states that match this key parameter value. For example, if these observers “measured” the location of an atom, then each observer would have a mental state corresponding to their having observed the same particular location. Continue reading "Progeny Probs: Souls, Ems, Quantum" »

GD Star Rating
Tagged as: ,

Understandable Social Systems

Brennan and Magness’ book Cracks in the Ivory Tower: The Moral Mess of Higher Education reviews many ways that colleges overpromise, and fail to deliver. It confirms (with Caplan’s Case Against Education) a picture wherein ordinary people are pretty clueless about a big institution in their lives. This cluelessness also seems to apply to many other life areas, such as medicine, charity, politics, etc. In each area, most people don’t seem to understand very basic things, like what exactly is the product, and what are the incentives of professionals?

That is, we each live in many complex social systems, such as political, transport, medical, religious, food, and school systems. Due to our poor understanding of such systems, we have low abilities to make intelligent personal choices about them, and even worse abilities to usefully contribute to efforts to reform them. This suggests a key criteria for evaluating social systems: understandability.

When we don’t understand our social systems, we can be seen as having little agency regarding them. They are like the weather; they exist, and may be good or bad, but we are too ignorant to do much about them. If a situation is bad, we can’t work to make it better. Some elites might have agency re such institutions, but not the rest of us. So a key question is: can we reform or create social institutions that are more understandable, to allow ordinary people to have more agency regarding the institutions in their lives?

One possible solution is to use meta-institutions, like academia, news media, or government regulators, that we may better understand and trust. We might, for example, support a particular reform to our medical system based on the recommendation of an academic institution. Our understanding of academia as a meta-institution could give us agency, even when we were ignorant of the institutions of medicine.

As an analogy, imagine that someone visits a wild life refuge. If this visitor does not understand the plants and animals in this area, they might reasonably fear the consequences of interacting with any given plant or animal, or of entering any given region. In contrast, when accompanied by a tour guide who can advise on what is safe versus dangerous, they might relax. But only if they have good reason to think this guide roughly shares their interests.  If your guide is a nephew who inherits your fortune if you die, you may be much less relaxed.

So here’s a key question: is there, at some level of abstraction, a key understandable institution by which we can usefully choose and influence many other parts of our social world? If we think we understand this meta institution well enough to trust it, that could give us substantial agency regarding key large scale features of our social worlds. For example, we could add our weight to particular reform efforts, because we had good reasons to expect such reforms to on average help.

Alas, academia, news media, and government regulators all seem too complex and opaque to serve in this key meta role. But three other widely used and simpler social mechanisms may be better candidates.

  1. Go with the majority. Buy the product that most other people buy, use the social habits that most others use, and have everyone vote on key big decisions. When some people know what’s best, and others abstain or pick randomly, then the majority will pick what’s best. Yes, there are many topic areas where people don’t abstain or pick randomly when they don’t know what’s best. But if we can roughly guess which are the problematic topics, then in other areas we may gain at least rough agency by going with the crowd.
  2. Follow prestige. Humans have rich ancient intuitive mechanisms for coordinating on who we find impressive. These mechanisms actually scale pretty well, allowing us to form consensus on the relative prestige of nations, professions, schools, cities, etc., and via these proxies, of individuals. Related ancient mechanisms let us form consensus on elite opinion, i.e., on what prestigious people tend to think on any given topic. Yes, elites are biased toward themselves, and to express opinions that make them seem impressive. Still, we can do worse than to follow our best.
  3. Embrace Winners. Nations, cities, firms, professions, teams, media, clubs, lovers, etc. often compete, in the sense that some grow at the expense of others that shrink or disappear. Often they compete for our personal support. And often we see judge that the competition is roughly “fair” and open to many potential competitors. In such cases, we may embrace the winners. For example, we may try many competitors, and stick with those we like best. Or we may go with the lowest price offer, if we can control well enough for quality variations.

Each of these big three mechanisms has limits, but they do seem to satisfy the requirement that they are very simple and many ordinary people can at least roughly understand why they work, and where they run into problems. Together they may cover a pretty wide range of cases. In addition, we can augment them with many other approaches. For example, we can just expose ourselves to choices and follow our intuitions on which are best. We can follow choices by those we know and trust well, those who seem to know more about a topic, and those who seem more honest in their evaluations. Together all these tricks may give us substantial agency re the social institutions in our lives.

Yet those examples of how badly most people misunderstand school, medicine, etc. suggest there is vast room for improvement. And so I look for ways to do better. Not just at designing institutions that actually work, in the sense of producing efficiency, equity, generality, robustness, evolvability, etc. Not just at designing meta-institutions with these features. And not just at gaining the support of majorities or elites, or at winning many fair competitions in the world. I seek meta-mechanisms that can also be simple and clear enough to their advantages be understandable to many ordinary people.

This is the context in which I’d like you to see my highest hopes for prediction markets. I offer them not just as mechanisms that actually work, producing and aggregating info at a low cost. After all, there may be other complex and subtle mechanisms that experts expect to achieve similar or even somewhat better results. But the problem in that case is that ordinary people may wonder how well they can trust such expert judgements.

No, I’m interested in the potential for prediction markets to serve as a simple understandable meta-institution, on par with and at the level of going with the majority, following prestige, and embracing winners. Simple enough that many ordinary people can directly understand why they should work well in many applications, and also to understand roughly where their limitations lie. Yes, not everyone can understand this, but maybe most everyone could know and trust someone who does understand.

GD Star Rating
Tagged as: , , ,

Low Prestige Hurts More

It can feel terrible to feel unwanted. Unwanted by schools, labor markets, sport teams, music bands, acting troupes, or romantic partners. We feel bad when we feel unwanted, and we often pity others to see them unwanted. Though we don’t usually pity enough to actually choose them over alternatives. And they can feel even worse to see our pity, as it affirms the visibility of their rejection.

Ever since we were foragers, humans have distinguished two kinds of status: dominance and prestige. Dominance is illicit, and we have norms saying to prevent and resist it, while prestige is not only allowed but encouraged. So one way to sympathize with and support someone who is unwanted is to frame their rejection as illicit dominance.

Since rich folks and big for-profit firms are easily portrayed as illicit dominators, it is easy to blame their illicit dominance when they reject people. So many people like to support those rejected by firms, such as for jobs at firms or loans from banks, by blaming firm dominance. Big firms can also be blamed when the products and services they sell explain why people are rejected by others. E.g., video games, tobacco, and payday lending.

This all helps explain why so many are so quick to blame “capitalist” firms and a larger culture and “system” of capitalism, such as for many kinds of discrimination leading to unfair rejection. Such blamers can then self-righteously sympathize with the rejected without having to actually choose them.

Note that economists often blame public pressures to cut firm rejections for bad economic effects, such as high unemployment in Europe where it is hard to fire workers, and excess home loans to risky households before the 2008 financial crisis.

This perspective also helps explain why people are reluctant to blame their “systems” of romance, friendship, conversation, sport, music, arts, which also result in rejections that make so many feel unwanted. Those systems tend to be associated more directly with prestige, and lack identifiable villains to blame for dominance. Except when big business gets involved. Rejection there can also be blamed on a larger “capitalist” culture causing discrimination, such as re sexual preferences or gender identities.

But here’s the thing: even without any illicit domination, some will have lower prestige than others, and that will hurt. Badly. In fact, it probably hurts even more than having low dominance, as that can be self-righteously blamed on others’ illicit pursuit of high dominance. Being low prestige, in contrast, elicits little sympathy from others, as showing sympathy toward such folks risks being pushed to not reject them, and being seen has having poor evaluation abilities regarding prestige.

The only simple solutions I see are an easy one, ignore it all, and a hard one: sometimes actually and honestly sympathize with the low in prestige. And let them see that sympathy. Which yes, will sometimes lead you to make “pity” choices you might not otherwise make. Do it because it hurts. (Some propose more complex solutions; they must wait for another post.)

GD Star Rating
Tagged as: , , ,

Chiang’s Exhalation

Ted Chiang’s new book Exhalation has received rave reviews. WSJ says “sci-fi for philosophers”, and the Post says “uniformly notable for a fusion of pure intellect and molten emotion.” The New Yorker says

Chiang spends a good deal of time describing the science behind the device, with an almost Rube Goldbergian delight in elucidating the improbable.

Vox says:

Chiang is thoughtful about the rules of his imagined technologies. They have the kind of precise, airtight internal logic that makes a tech geek shiver with happiness: When Chiang tells you that time travel works a certain way, he’ll always provide the scientific theory to back up what he’s written, and he will never, ever veer away from the laws he’s set for himself.

That is, they all seem to agree that Chiang is unusually realistic and careful in his analysis.

I enjoyed Exhalation, as I have Chiang’s previous work. But as none of the above reviews (nor any of 21 Amazon reviews) make the point, it apparently falls to me to say that this realism and care is limited to philosophy and “hard” science. Re social science, most of these stories are not realistic.

Perhaps Chiang is well aware of this; his priority may be to paint the most philosophically or morally dramatic scenarios, regardless of their social realism. But as reviewers seem to credit his stories with social realism, I feel I should speak up. To support my claims, I’m going to have to give “spoilers”; you are warned. Continue reading "Chiang’s Exhalation" »

GD Star Rating
Tagged as: ,

Expand vs Fight in Social Justice, Fertility, Bioconservatism, & AI Risk

Most people talk too much about values relative to facts, as they care more about showing off their values than about learning facts. So I usually avoid talking values. But I’ll make an exception today for this value: expanding rather than fighting about possibilities.

Consider the following graph. On the x-axis you, or your group, get more of what you want. On the y-axis, others get more of what they want. (Of course each axis really represents a high dimensional space.) The blue region is a space of possibilities, the blue curve is the frontier of best possibilities, and the blue dot is the status quo, which happens if no one tries to change it.

In this graph, there are two basic ways to work to get more of what you want: move along the frontier (FIGHT), or expand it (EXPAND). While expanding the frontier helps both you and others, moving along the frontier helps you at others’ expense.

All else equal, I prefer expanding over fighting, and I want stronger norms for this. That is, I want our norms to, all else equal, more praise expansion and shame fighting. This isn’t to say I want all forms of fighting to be shamed, or shamed equally, or want all kinds of expansion to get equal praise. For example, it makes sense to support some level of “fighting back” in response to fights initiated by others. But on average, we should all expect to be better off when our efforts are on averaged directed more toward expanding than fighting. Fighting should be suspicious, and need justification, relative to expansion.

This distinction between expanding and fighting is central to standard economic analysis. We economists distinguish “efficiency improving” policies that expand possibilities from “redistribution” policies that take from some to give to others, and also from “rent-seeking” efforts that actually cut possibilities. Economists focus on promoting efficiency and discouraging rent-seeking. If we take positions on redistribution, we tend to call those “non-economic” positions.

We economists can imagine an ideal competitive market world. The world we live in is not such a world, at least not exactly, but it helps to see what would happen in such a world. In this ideal world, property rights are strong, we each own stuff, and we trade with each other to get more of what we want. The firms that exist are the ones that are most effective at turning inputs into desired outputs. The most cost-effective person is assigned to each job, and each customer buys from their most cost-effective supplier. Consumers, investors, and workers can make trades across time, and innovations happen at the most cost-effective moment.

In this ideal world, we maximize the space of possibilities by allowing all possible competition and all possible trades. In that case, all expansions are realized, and only fights remain. But in other more realistic worlds many “market failures” (and also government failures) pull back the frontier of possibilities. So we economists focus on finding actions and policies that can help fix such failures. And in some sense, I want everyone to share this pro-expansion anti-fight norm of economists.

Described in this abstract way, few may object to what I’ve said so far. But in fact most people find a lot more emotional energy in fights. Most people are quickly bored with proposals that purport to help everyone without helping any particular groups more than others. They get similarly bored with conversations framed as collecting and sharing relevant information. They instead get far more energized by efforts to help us win against them, including conversations framed as arguing with and even yelling at enemies. We actually tend to frame most politics and morality as fights, and we like it that way.

For example, much “social justice” energy is directed toward finding, outing, and “deplatforming” enemies. Yes, when social norms are efficient, enforcing such norms against violators can enhance efficiency. But our passions are nearly as strong when enforcing inefficient norms or norm-like agendas, just as a crime dramas are nearly as exciting when depicting the enforcement of bad crime laws or non-law vendettas. Our energy comes from the fights, not some indirect social benefit resulting from such fights. And we find it way too easy to just presume that the goals of our social factions are very widely shared and efficient norms.

Consider fertility and education. Many people get quite energized on the topic of whether others are having too many or not enough kids, and on whether they are raising those kids correctly. We worry about which nations, religions, classes, intelligence levels, mental illness categories, or political allegiances are having more kids, or getting more kids to be educated or trained in their favored way. And we often seek government policies to push our favored outcomes. Such as sterilizing the mentally ill, or requiring schools to teach our favored ideologies.

But in an ideal competitive world, each family picks how many kids to have and how to raise them. If other people have too many kids and and have trouble feeding them, that’s their problem, not yours. Same for if they choose to train their kids badly, or if those kids are mentally ill. Unless you can identify concrete and substantial market failures that tend to induce the choices you don’t like, and which are plausibly the actual reason for your concerns here, you should admit you are more likely engaged in fights, not in expansion efforts, when arguing on fertility and education.

And it isn’t enough to note that we are often inclined to supply medicine, education, or food collectively. If such collective actions are your main excuse for trying to control other folks’ related choices, maybe you should consider not supplying such things collectively. It also isn’t enough to note the possibility of meddling preferences, wherein you care directly about others’ choices. Not only is evidence of such preferences often weak, but meddling preferences don’t usually change the possibility frontier, and thus don’t change which policies are efficient. Beware the usual human bias to try to frame fighting efforts as more pro-social expansion efforts, and to make up market failure explanations in justification.

Consider bioconservatism. Some look forward to a future where they’ll be able to change the human body, adding extra senses, and modifying people to be smarter, stronger, more moral, and even immortal. Others are horrified by and want to prevent such changes, fearing that such “post-humans” would no longer be human, and seeing societies of such creatures as “repugnant” and having lost essential “dignities”. But again, unless you can identify concrete and substantial market failures that would result from such modifications, and that plausibly drive your concern, you should admit that you are engaged in a fight here.

It seems to me that the same critique applies to most current AI risk concerns. Back when my ex-co-blogger Eliezer Yudkowsky and I discussed his AI risk concerns here on this blog (concerns that got much wider attention via Nick Bostrom’s book), those concerns were plausibly about a huge market failure. Just as there’s an obvious market failure in letting someone experiment with nuclear weapons in their home basement near a crowded city (without holding sufficient liability insurance), there’d be an obvious market failure from letting a small AI team experiment with software that might, in a weekend, explode to become a superintelligence that enslaved or destroyed the world. While I see that scenario as pretty unlikely, I grant that it is a market failure scenario. Yudkowsky and Bostrom aren’t fighting there.

But when I read and talk to people today about AI risk, I mostly hear people worried about local failures to control local AIs, in a roughly competitive world full of many AI systems with reasonably strong property rights. In this sort of scenario, each person or firm that loses control of an AI would directly suffer from that loss, while others would suffer far less or not at all. Yet AI risk folks say that they fear that many or even most individuals won’t care enough to try hard enough to keep sufficient control of their AIs, or to prevent those AIs from letting their expressed priorities drift as contexts change over the long run. Even though such AI risk folks don’t point to particular market failures here. And even though such advanced AI systems are still a long ways off, and we’ll likely know a lot more about, and have plenty of time to deal with, AI control problems when such systems actually arrive.

Thus most current AI risk concerns sound to me a lot like fertility, education, and bioconservatism concerns. People say that it is not enough to control their own fertility, the education of their own kids, the modifications of their own bodies, and the control of their own AIs. They worry instead about what others may do with such choices, and seek ways to prevent the “risk” of others making bad choices. And in the absence of identified concrete and substantial market failures associated with such choices, I have to frame this as an urge to fight, instead of to expand the space of possibilities. And so according to the norms I favor, I’m suspicious of this activity, and not that eager to promote it.

GD Star Rating
Tagged as: , , ,