How Lumpy AI Services?

Long ago people like Marx and Engels predicted that the familiar capitalist economy would naturally lead to the immiseration of workers, huge wealth inequality, and a strong concentration of firms. Each industry would be dominated by a main monopolist, and these monsters would merge into a few big firms that basically run, and ruin, everything. (This is somewhat analogous to common expectations that military conflicts naturally result in one empire ruling the world.)

Many intellectuals and ordinary people found such views quite plausible then, and still do; these are the concerns most often voiced to justify redistribution and regulation. Wealth inequality is said to be bad for social and political health, and big firms are said to be bad for the economy, workers, and consumers, especially if they are not loyal to our nation, or if they coordinate behind the scenes.

Note that many people seem much less concerned about an economy full of small firms populated by people of nearly equal wealth. Actions seem more visible in such a world, and better constrained by competition. With a few big privately-coordinating firms, in contrast, who knows that they could get up to, and they seem to have so many possible ways to screw us. Many people either want these big firms broken up, or heavily constrained by presumed-friendly regulators.

In the area of AI risk, many express great concern that the world may be taken over by a few big powerful AGI (artificial general intelligence) agents with opaque beliefs and values, who might arise suddenly via a fast local “foom” self-improvement process centered on one initially small system. I’ve argued in the past that such sudden local foom seems unlikely because innovation is rarely that lumpy.

In a new book-length technical report, Reframing Superintelligence: Comprehensive AI Services as General Intelligence, Eric Drexler makes a somewhat similar anti-lumpiness argument. But he talks about task lumpiness, not innovation lumpiness. Powerful AI is safer if it is broken into many specific services, often supplied by separate firms. The task that each service achieves has a narrow enough scope that there’s little risk of it taking over the world and killing everyone in order to achieve that task. In particular, the service of being competent at a task is separate from the service of learning how to become competent at that task. In Drexler’s words: Continue reading "How Lumpy AI Services?" »

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Conditional Harberger Tax Games

Baron Georges-Eugène Haussmann … transformed Paris with dazzling avenues, parks and other lasting renovations between 1853 and 1870. … Haussmann… resolved early on to pay generous compensation to [Paris] property owners, and he did. … [He] hoped to repay the larger loans he obtained from the private sector by capturing some of the increased value of properties lining along the roads he built. … [He] did confiscate properties on both sides of his new thoroughfares, and he had their edifices rebuilt. … Council of State … forced him to return these beautifully renovated properties to their original owners, who thus captured all of their increased value. (more)

In my last post I described abstractly how a system of conditional Harberger taxes (CHT) could help deal with zoning and other key city land use decisions. In this post, let me say a bit more about the behaviors I think we’d actually see in such a system. (I’m only considering here such taxes for land and property tied to land.)

First, I while many property owners would personally manage their official declared property values, many others would have them set by an agent or an app. Agents and apps may often come packaged with insurance against various things that can go wrong, such as losing one’s property.

Second, yes, under CHT, sometimes people would (be paid well to) lose their property. This would almost always be because someone else credibly demonstrated that they expect to gain more value from it. Even if owners strategically or mistakenly declare values too low, the feature I suggested of being able to buy back a property by paying a 1% premium would ensure that pricing errors don’t cause property misallocations. The highest value uses of land can change, and one of the big positive features of this system is that it makes the usage changes that should then result easier to achieve. In my mind that’s a feature, not a bug. Yes, owners could buy insurance against the risk of losing a property, though that needn’t result in getting their property back.

In the ancient world, it was common for people to keep the same marriage, home, neighbors, job, family, and religion for their entire life. In the modern world, in contrast, we expect many big changes during our lifetimes. While we can mostly count on family and religion remaining constant, we must accept bigger chances of change to marriages, neighbors, and jobs. Even our software environments change in ways we can’t control when new versions are issued. Renters today accept big risks of home changes, and even home “owners” face big risks due to job and financial risks. All of which seems normal and reasonable. Yes, a few people seem quite obsessed with wanting absolute guarantees on preservation of old property usage, but I can’t sympathize much with such fetishes for inefficient stasis. Continue reading "Conditional Harberger Tax Games" »

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Fine Grain Futarchy Zoning Via Harberger Taxes

Futarchy” is my proposed system of governance which approves a policy change when conditional prediction markets give a higher expected outcome, conditional on that change. In a city setting, one might be tempted to use a futarchy where the promoted outcome is the total property value of all land in and near that city. After all, if people don’t like being in this city, and are free to move elsewhere, city land won’t be worth much; the more attractive a city is as a place to be, the more its property will be worth.

Yes, we have problems measuring property values. Property is only traded infrequently, sale prices show a marginal not a total value, much land is never offered for sale, sales prices are often obscured by non-cash terms of trade, and regulations and taxes change sales and use. (E.g., rent control.) In addition, we expect at least some trading noise in the prices of any financial market. As a result, simple futarchy isn’t much help for decisions whose expected consequences for outcomes are smaller than its price noise level. And yes, there are other things one might care about beside property values. But given how badly city governance often actually goes, we could do a lot worse than to just consistently choose policies that maximize a reasonable estimate of city property value. The more precise such property estimates can be, the more effective such a futarchy could be.

Zoning (and other policy that limits land use) is an area of city policy that seems especially well suited to a futarchy based on total property value. After all, the main reason people say that we need zoning is because using some land in some ways decreases how much people are willing to pay to use other land. For example, people might not want to live next to a bar, liquor store, or sex toy store, are so are willing to pay less to buy (or rent) next to such a place. So choosing zoning rules to maximize total property value seems especially promising.

I’ve also written before favorably on Harberger taxes (which I once called “stability rents”). In this system, owners of land (and property tied to that land) must set and may continuously adjust a declared property “value”; they are taxed per unit time as a percentage of momentary value, and must always agree to sell their property at their currently declared value. This system has great advantages in inducing property to be held by those who can gain the most value from it, including via greatly lowering the transaction costs of putting together big property packages. With this system, there’s no more need for eminent domain.

I’ve just noticed a big synergy between futarchy for zoning and Harberger taxes. The reason is that such taxes allow the creation of prices which support a much finer grain accounting of the net value of specific zoning changes. Let me explain.

First, Harberger taxes create a continuous declared value on each property all the time, not just a few infrequent sales prices. This creates a lot more useful data. Second, these declared values better approximate the value that people place on property; the higher an actual value, the higher an owner will declare his or her taxable value to be, to avoid the risk of someone taking it away. Third, these declared values are all relative to a standard terms of trade, not the varying terms of actual sales today. Thus the sum total of all declared property values can be a decent estimate of total city property value. Third, it is possible to generalize the Harberger tax system to create zoning-conditional property ownership and prices.

That is, relative to current zoning rules, one can define a particular alternative zoning scenario, wherein the zoning (or other property use limit) policies have changed. Such as changing the zoning of a particular area from residential to commercial on a particular date. Given such a defined scenario, one can create conditional ownership; I own this property if (and when) this zoning change is made, but not otherwise. The usual ownership then becomes conditional on no zoning changes soon.

With conditional ownership, conditional owners can make conditional offers to sell. That is, you can buy my property under this condition if you pay this declared amount of conditional cash. For example, I might offer to make a conditional sale of my property for $100,000, and you might agree to that sale, but this sale only happens if a particular zoning change is approved.

The whole Harberger tax system can be generalized to support such conditional trading and prices. In the simple system, each property has a declared value set by its owner, and anyone can pay that amount at any time to become the new owner. In the generalized system, each property has a declared value for each (combination of) approved alternative zoning scenario. By default, alternative declared values are equal to the ordinary no-zoning-change declared value, but property owners can set them differently if they want, to be either higher or lower. Anyone can make a scenario-conditional purchase of a property from its current (conditional) owner at its scenario-conditional declared value. To buy a property for sure, buy it conditional on all scenarios.

(For concreteness, assume that only one zoning change proposal is allowed per day per city region, that a decision is made on that proposal in that day, and that the proposal for each day is chosen via open public auction a month before. The auction fee can subsidize markets in bets on if this proposal will be approved and markets in tax-revenue asset conditional differences (explained below). A week before the decision day of a proposal, each right in a property is split into two conditional rights, one conditional on this change and one on not-this-change. At that point, owner declared values conditional on this change (or not) become active sale prices. Taxes are paid in conditional cash. Physical control of a property only transfers to conditional owners if and when a zoning scenario is actually approved.)

Having declared values for all properties under all scenarios gives us even more data with which to estimate total city property value, and in particular helps with estimating the difference in total city property value due to a zoning change. To a first approximation, we can just add up all the zoning-change-conditional declared values, and compare that sum to the sum from the no-change declared values. If the former sum is consistently and clearly higher than the latter sum over the proposal’s decision day, that seems a good argument for adopting this zoning proposal. (It seems safer to choose the higher value option with a chance increasing in value difference, and this all works even when other factors influence a decision.) At least if the news that this zoning proposal seems likely be approved gets spread wide and fast enough for owners to express their conditional declared values. (The bet markets on which properties will be effected helps to notify owners.)

Actually, to calculate the net property value difference that a zoning change makes, we need only sum over the properties that actually have a conditional declared value different from its no-change declared value. For small local zoning changes, this might only be a small number of properties within a short distance of the main changes. As a result, this system seems capable of giving useful advice on very small and local zoning changes, in dramatic contrast to a futarchy based on prices estimating total city property values. For example, it might even be able to say if a particular liquor store should be allowed at a particular location, or if the number of required parking spots at a particular shopping mall can be reduced. As promised, this new system offers much finer grain accounting of the net value of specific zoning changes.

Note that in this system as described, losers are not compensated by winners for zoning rule changes, even though we can roughly identify winners and losers. I’ve thought a bit about ways to add a extra process by which winners compensate losers, but haven’t been able to make that work. So the best I can think of is to have the system look at the distribution of wins and losses, and reject proposed changes where there are too many big losers relative to winners. That would force a search for variations which spread out the pain more evenly.

We are close to a workable proposal, but not quite there yet. This is because we face the problem of owners temporarily inflating their declared values conditional on a zoning change that they seek to promote. This might tip the balance to get a change approved, and then after approval such owners could cut their declared values back down to something reasonable, and only pay a small extra tax for that small decision period. Harberger taxes impose a stronger penalty for declaring overly-low values than overly-high values.

A solution to this problem is to use, instead of declared values, prices for the purely financial assets that represent claims on all future tax revenue from the Harberger tax on a particular property. That is, each property will pay a tax over time, we could divert that revenue into a particular account, and an asset holder could own the right to spend a fraction of the funds in that account. Such tax-revenue assets could be bought and sold in financial markets, and could also be made conditional on particular zoning scenarios. As such assets are easy to create and duplicate, the usual speculation pressures should make it hard to manipulate these prices much in any direction.

A plan to temporarily inflate the declared value of a property shouldn’t do much to the market price for a claim to part of all future tax revenue from that property. So instead of summing over conditional differences in declared-values to see if a zoning change is good, it is probably better to sum over conditional differences in tax revenue assets. Subsidized continuous market makers can give exact if noisy prices for all such differences, and for most property-scenario pairs this difference will be exactly zero.

So that’s the plan for using futarchy and Harberger taxes to pick zoning (and other land use limit policy) changes. Instead of just one declared value per property, we allow owners to specify declared values conditional on each approved zoning change (or not) scenario, and allow conditional purchases as well. By default, conditional values equal no-change values. We should tend more to adopt zoning proposals when, during its decision day, when the sum of its (tax-revenue-asset) conditional differences clearly and consistently exceeds zero.

Thanks to Alex Tabarrok & Keller Scholl for their feedback.

Added 11pm: One complaint people have about a Harberger tax is that owners would feel stressed to know that their property could be taken at any time. Here’s a simple fix. When someone takes your property at your declared value, you can pay 1% of that value to get it back, if you do so quickly. But then you’d better raise your declared value or someone else could do the same thing the next day or week. You pay 1% for a fair warning that your value is too low. Under this system, people only lose their property when someone else actually values it more highly, even after considering the transaction costs of switching property.

Added 2Feb: I edited this post a bit. Note that with severe enough property limits, negative declared property values can make sense. For example, if a property must be maintained so as to serve as a public park, the only people willing to become owners are those who get paid when they take the property, and then get paid per unit time for remanning owners. In this way, city services can be defined and provided via this same decision mechanism.

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Distant Future Tradeoffs

Over the last day on Twitter, I ran three similar polls. One asked:

Software design today faces many tradeoffs, e.g., getting more X costs less Y, or vice versa. By comparison, will distant future tradeoffs be mostly same ones, about as many but very different ones, far fewer (so usually all good features X,Y are feasible together), or far more?

Four answers were possible: mostly same tradeoffs, as many but mostly new, far fewer tradeoffs, and far more tradeoffs. The other two polls replaced “Software” with “Physical Device” and “Social Institution.”

I now see these four answers as picking out four future scenarios. A world with fewer tradeoffs is Utopian, where you can more get everything you want without having to give up other things. In contrast, a world with many more tradeoffs is more Complex. A world where most of the tradeoffs are like those today is Familiar. And a world where the current tradeoffs are replaced by new ones is Radical.  Using these terms, here are the resulting percentages:

The polls got from 105 to 131 responses each, with an average entry percentage of 25%, so I’m willing to believe differences of 10% or more. The most obvious results here are that only a minority foresee a familiar future in any area, and answers vary greatly; there is little consensus on which scenarios are more likely.

Beyond that, the strongest pattern I see is that respondents foresee more complexity, relative to a utopian lack of tradeoffs, at higher levels of organization. Physical devices are the most utopian, social institutions are the most complex, and software sits in the middle. The other possible result I see is that respondents foresee a less familiar social future. 

I also asked:

Which shapes the world more in the long run: the search for arrangements allowing better compromises regarding many complex tradeoffs, or fights between conflicting groups/values/perspectives?

In response, 43% said search for tradeoffs while 30% said value conflicts, and 27% said hard to tell. So these people see tradeoffs as mattering a lot.  

These respondents seriously disagree with science fiction, which usually describes relatively familiar social worlds in visibly changed physical contexts (and can’t be bothered to have an opinion on software). They instead say that the social world will change the most, becoming the most complex and/or radical. Oh brave new world, that has such institutions in it!

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How Does Brain Code Differ?

The Question

We humans have been writing “code” for many decades now, and as “software eats the world” we will write a lot more. In addition, we can also think of the structures within each human brain as “code”, code that will also shape the future.

Today the code in our heads (and bodies) is stuck there, but eventually we will find ways to move this code to artificial hardware. At which point we can create the world of brain emulations that is the subject of my first book, Age of Em. From that point on, these two categories of code, and their descendant variations, will have near equal access to artificial hardware, and so will compete on relatively equal terms to take on many code roles. System designers will have to choose which kind of code to use to control each particular system.

When designers choose between different types of code, they must ask themselves: which kinds of code are more cost-effective in which kinds of applications? In a competitive future world, the answer to this question may be the main factor that decides the fraction of resources devoted to running human-like minds. So to help us envision such a competitive future, we should also ask: where will different kinds of code work better? (Yes, non-competitive futures may be possible, but harder to arrange than many imagine.)

To think about which kinds of code win where, we need a basic theory that explains their key fundamental differences. You might have thought that much has been written on this, but alas I can’t find much. I do sometimes come across people who think it obvious that human brain code can’t possibly compete well anywhere, though they rarely explain their reasoning much. As this claim isn’t obvious to me, I’ve been trying to think about this key question of which kinds of code wins where. In the following, I’ll outline what I’ve come up with. But I still hope someone will point me to useful analyses that I’ve missed.

In the following, I will first summarize a few simple differences between human brain code and other code, then offer a deeper account of these differences, then suggest an empirical test of this account, and finally consider what these differences suggest for which kinds of code will be more cost-effective where. Continue reading "How Does Brain Code Differ?" »

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Youth As Abundance

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

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

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

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

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

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

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

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

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

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

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When Wholes Become Parts

Here’s a nice simple general principle to describe many kinds of systems. When once self-sufficient wholes join together to become parts of a new whole, the parts get simpler and also more different from one another:

The emergence of a higher level entity with functional capabilities is ordinary accompanied by the loss of part types within the lower-level organisms that constitute it. Thus … cells in multicellular organisms will have fewer part types than fee-living protists. … The lower-level organisms are transformed into differentiated parts within the higher-level entity. Along with this, as size increases, parts emerge at an intermediate scale, between the lower level organisms and the higher-level entity. …

In the evolution of multicellularity, cells are transformed from organisms into different tailed parts. Then, as the size of the multicellular entity increased, cells combined to form larger parts, intermediate in scale between as cell and the multicellular organism as a whole. … Cells in metazoans and land plants have fewer part types on average than free-living protists. … found a power law relationship between size and number of cell types in multicellular organisms. Also, the degree of morphological, physiological, and/or behavioral differentiation – in insect societies increases with colony size.

From: Daniel McShea and Carl Anderson. (2005) “The Remodularization of the Organism”, in Werner Callebaut and Diego Rasskin-Gutman, eds., Modularity: Understanding the Development and Evolution of Natural Complex Systems, pp. 185-206, MIT Press, May.

That is, while each cell might in essence need legs, eyes, a mouth, and a stomach, when cells join together they can each live without such things, and they may specialist in order to become part of a leg, eye, etc. for the new organism.

This has an obvious implication for our future. As we humans join together into larger more complex social organizations, our descendants will likely also become simpler and more differentiated. Of course there are limits on how fast these things can change today. Also, the cells in each organism now have a great many parts, and remain similar to each other in a great many ways. Change would likely become much faster if ems become possible.

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Have A Thing

I’m not into small talk; I prefer to talk to people about big ideas. I want to talk big ideas to people who are smart, knowledgeable, and passionate about big ideas, and where it seems that convincing them about something on a big idea has a decent chance of changing their behavior in important ways.

Because of this, I prefer to talk to people who “have a thing.” That is, who have some sort of abstract claim (or question) which they consider important and neglected, for which they often argue, and which intersects somehow with their life hopes/plans. When they argue, they are open to and will engage counter-arguments. They might push this thing by themselves, or as part of a group, but either way it matters to them, they represent it personally, and they have some reason to think that their personal efforts can make a difference to it.

People with a thing allow me to engage a big idea that matters to someone, via someone who has taken the time to learn a lot about it, and who is willing to answer many questions about it. Such a person creates the hope that I might change their actions by changing their mind, or that they might convince me to change my life hopes/plans. I may convince them that some variation is more promising, or that some other thing fits better with the reasons they give. Or I might know of a resource, such as a technique or a person, who could help them with their thing.

Yes, in part this is all because I’m a person with many things. So I can relate better to such people. And after I engage their thing, there’s a good chance that they will listen to and engage one of my things. Even so, having a thing is handy for many people who are different from me. It lets you immediately engage many people in conversation in a way so that they are likely to remember you, and be impressed by you if you are in fact impressive.

Yes, having a thing can be off-putting to the sort of people who like to keep everything mild and low-key, and make sure that their talk has little risk of convincing them to do something that might seem weird or passionate. But I consider this off-putting effect to be largely a gain, in sorting out the sort of people I’m less interested in.

Now having a thing won’t save you if you are a fool or an idiot. In fact, it might make that status more visible. But if you doubt you are either, consider having a thing.

Added 11p: Beware of two common failures modes for people with things: 1) not noticing how much others want to hear about your thing, 2) getting so attached to your thing that you don’t listen enough to criticism of it.

Note also that having things promotes an intellectual division of labor, which helps the world to better think through everything.

Added 11Jan: Beware a third failure mode: being more serious or preachy than your audience wants. You can be focused and interesting without making people feel judged.

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Gender Is Big

Consider the possibility of discrimination against the left-handed. Such discrimination might make efficiency sense in contexts where expensive-to-change complementary equipment is designed for the right-handed. Such as pilots. In other contexts, one might justify mild discrimination based on weak correlations, such as between handedness and intelligence, gender, and health. But these other factors tend to be directly observable, and correlations are weak. So stronger correlations of handedness with success, especially where not explained by these other correlations, are suspicious.

What do we suspect? One possibility is political equilibria wherein an established group of insiders arbitrarily favor people like them against outsiders. We might especially suspect this if we saw people rewarding others for discriminating against the left-handed, as something like this would need to be part of an insiders-favoring political equilibria. It is plausible, though not obvious, that disrupting such an insider-favoring equilibria is good for the world. So we might consider prohibiting or at least hindering discrimination against left-handers. (One might also just think we are in a bad choice out of multiple equilibria, and not blame insiders so much.)

This all makes sense as a way to think about discrimination for what are arguably relatively minor, or small, features such as height or hair-length. But now consider gender. It seems to me that the above framework is far less useful for gender, as gender is not remotely a small feature.

For most people, their main long-term spouse is the most important relationship in their life. And most care greatly about the gender of that spouse. It isn’t just ordinary “straight cis” people who think this way. Gay/lesbians also mostly agree that the genders differ greatly in important features, and they have a strong preference for one end of the gender spectrum. In part because others care about gender, most people also care greatly about how others see their own gender. Most transgender people also care a lot (almost by definition) about how others see their gender; they just make unusual choices about that. So most everyone agrees that most everyone cares a lot about the genders of their associates, and the genders that others assign to them.

Some may postulate gender as an innate atomic feature of the universe of human concerns, so that when we desire that an associate have a certain gender that has nothing to do with their many other associated features. But that seems crazy to me. Much more plausibly, what we like about a gender is strongly tied to the set of associated features that tend to go along with that gender. That is, we like the package of features that “are” a gender. In this case, the fact that we strongly care about genders suggests that different genders differ greatly in many features that are important to us. These features probably include habits, attitudes, preferences, and abilities. Gender is big, and it matters a lot.

Because gender is big, we expect it to correlate substantially with many features that we care about when assigning people to roles. But this means that even strong correlations of gender with success in particular roles is at best only a weak cause for suspicion about insider-favoring or other bad equilibria. There are just too many other good reasons to expect to see large gender-role correlations.

Now you might argue that today’s large correlations between gender and important features are largely a legacy resulting from a bad past. And change takes time. So creating pressures for low gender-role correlations today will push us to move faster toward a better future, even if that costs us today in terms of matching people to roles well.

However, the prospects for a world anytime soon where different genders correlate little with other important features seems to me quite low. (As low as the chance that communist governments would rapidly “whither away” to produce “true” communism.) Yes, gender correlations have changed across societies and across time, but almost always there have been strong correlations between gender and important things. The fact that societies with weaker gender roles have more strongly gendered personalities also (weakly) suggests to me that we fundamentally want genders to differ, even if we aren’t that stuck on most particular differences. We want gender to be big; we want to love and be loved by people that differ from us in big known ways.

Thus I don’t see gender-success correlations as by themselves offering much of a justification for anti-discrimination efforts today to suppress such correlations. At least they don’t in terms of disrupting insider-favoring or other bad equilibria, or in terms of promoting a low-gender-differences future. But I do see some other justifications, which I may write about in future posts.

It seems to me that our public discussion about gender has for a while been somewhat in denial about the likely long continuation of strong gender correlations with important features. If the genders continue to act differently on average, then observers will naturally form gendered expectations based on such behavior. That is, there will be gender roles. We can and should talk about what we want those gender roles to be, but we can’t do that until we admit that such roles will exist.

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Replication Markets Team Seeks Journal Partners for Replication Trial

An open letter, from myself and a few colleagues:

Recent attempts to systematically replicate samples of published experiments in the social and behavioral sciences have revealed disappointingly low rates of replication. Many parties are discussing a wide range of options to address this problem.

Surveys and prediction markets have been shown to predict, at rates substantially better than random, which experiments will replicate. This suggests a simple strategy by which academic journals could increase the rate at which their published articles replicate. For each relevant submitted article, create a prediction market estimating its chance of replication, and use that estimate as one factor in deciding whether to publish that article.

We the Replication Markets Team seek academic journals to join us in a test of this strategy. We have been selected for an upcoming DARPA program to create prediction markets for several thousand scientific replication experiments, many of which could be based on articles submitted to your journal. Each market would predict the chance of an experiment replicating. Of the already-published experiments in the pool, approximately one in ten will be sampled randomly for replication. (Whether submitted papers could be included in the replication pool depends on other teams in the program.) Our past markets have averaged 70% accuracy, and the work is listed at the Science Prediction Market Project page, and has been published in Science, PNAS, and Royal Society Open Science.

While details are open to negotiation, our initial concept is that your journal would tell potential authors that you are favorably inclined toward experiment article submissions that are posted at our public archive of submitted articles. By posting their article, authors declare that they have submitted their article to some participating journal, though they need not say which one. You tell us when you get a qualifying submission, we quickly tell you the estimated chance of replication, and later you tell us of your final publication decision.

At this point in time we seek only an expression of substantial interest that we can take to DARPA and other teams. Details that may later be negotiated include what exactly counts as a replication, whether archived papers reveal author names, how fast we respond with our replication estimates, what fraction of your articles we actually attempt to replicate, and whether you privately give us any other quality indicators obtained in your reviews to assist in our statistical analysis.

Please RSVP to: Angela Cochran, PM acochran@replicationmarkets.com 571 225 1450

Sincerely, the Replication Markets Team

Thomas Pfeiffer (Massey University)
Yiling Chen, Yang Liu, and Haifeng Xu (Harvard University)
Anna Dreber Almenberg & Magnus Johannesson (Stockholm School of Economics)
Robin Hanson & Kathryn Laskey (George Mason University)

Added 2p: We plan to forecast ~8,000 replications over 3 years, ~2,000 within the first 15 months.  Of these, ~5-10% will be selected for an actual replication attempt.

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