Compare Institutions To Institutions, Not To Perfection

By Robin Hanson · August 5, 2017 9:10 am · Comments (7)

Mike Thicke of Bard College has just published a paper that concludes:

The promise prediction markets to solve problems in assessing scientific claims is largely illusory, while they could have significant unintended consequences for the organization of scientific research and the public perception of science. It would be unwise to pursue the adoption of prediction markets on a large scale, and even small-scale markets such as the Foresight Exchange should be regarded with scepticism.

He gives three reasons:

[1.] Prediction markets for science could be uninformative or deceptive because scientific predictions are often long-term, while prediction markets perform best for short-term questions. .. [2.] Prediction markets could produce misleading predictions due to their requirement for determinable predictions. Prediction markets require questions to be operationalized in ways that can subtly distort their meaning and produce misleading results. .. [3.] Prediction markets offering significant profit opportunities could damage existing scientific institutions and funding methods.

Imagine that you want to travel to a certain island. Some else tells you to row a boat there, but I tell you that a helicopter seems more cost effective for your purposes. So the rowboat advocate replies, “But helicopters aren’t as fast as teleportation, they take longer and cost more when to go longer distances, and you need more expert pilots to fly in worse weather.” All of which is true, but not very helpful.

Similarly, I argue that with each of his reasons, Thicke compares prediction markets to some ideal of perfection, instead of to the actual current institutions it is intended to supplement. Lets go through them one by one. On 1:

Even with rational traders who correctly assess the relevant probabilities, binary prediction markets can be expected to have a bias towards 50% predictions that is proportional to their duration. .. it has been demonstrated both empirically and theoretically .. long-term prediction markets typically have very low trading volume, which makes it unlikely that their prices react correctly to new information. .. [Hanson] envisions Wegener offering contracts ‘to be judged by some official body of geologists in a century’, but this would not have been an effective criterion given the problem of 50%-bias in long-term prediction markets. .. Prediction markets therefore would have been of little use to Wegener.

First a predictable known distortion isn’t a problem at all for forecasts; just invert the distortion to get the accurate forecast. Second, this is much less of an issue in combinatorial markets, where all questions are broken into thousands or more tiny questions, all of which have tiny probabilities, and a global constraint ensures they all add up to one. But more fundamentally, all institutions face the same problem that all else equal, it is easier to give incentives for accurate short term predictions, relative to long term ones. This doesn’t show that prediction markets are worse in this case than status quo institutions. On 2:

Even if prediction markets correctly predict measured surface temperature, they might not predict actual surface temperature if the measured and actual surface temperatures diverge. .. Globally averaged surface air temperature [might be] a poor proxy for overall global temperature, and consequently prediction market prices based on surface air temperature could diverge from what they purport to predict: global warming. .. If interpreting the results of these markets requires detailed knowledge of the underlying subject, as is needed to distinguish global average surface air temperature from global average temperature, the division of cognitive labour promised by these markets will disappear. Perhaps worse, such predictions could be misinterpreted if people assume they accurately represent what they claim to.

All social institutions of science must deal with the facts that there can be complex connections between abstract theories and specific measurements, and that ignorant outsiders may misinterpret summaries. Yes prediction market summaries might mislead some, but then so can grant and article abstracts, or media commentary. No, prediction markets can’t make all such complexities go away. But this hardly means that prediction markets can’t support a division of labor. For example, in combinatorial prediction markets different people can specialize in the connections between different variables, together managing a large Bayesian network of predictions. On 3:

If scientists anticipate that trading on prediction markets could generate significant profits, either due to being subsidized .. or due to legal changes allowing significant amounts of money to be invested, they could shift their attention toward research that is amenable to prediction markets. The research most amenable to prediction markets is short-term and quantitative: the kind of research that is already encouraged by industry funding. Therefore, prediction markets could reinforce an already troubling push toward short-term, application-oriented science. Further, scientists hoping to profit from these markets could withhold salient data in anticipation of using that data to make better informed trades than their peers. .. If success in prediction markets is taken as a marker of scientific credibility, then scientists may pursue prediction-oriented research not to make direct profit, but to increase their reputation.

Again, all institutions work better on short term questions. The fact that prediction markets also work better on short term questions does not imply that using them creates more emphasis on short term topics, relative to using some other institution. Also, every institution of science must offer individuals incentives, incentives which distract them from other activities. Such incentives also imply incentives to withhold info until one can use that info to one’s maximal advantage within the system of incentives. Prediction markets shouldn’t be compared to some perfect world where everyone shares all info without qualification; such worlds don’t exist.

Thicke also mentioned:

Although Hanson suggests that prediction market judges may assign non-binary evaluations of predictions, this seems fraught with problems. .. It is difficult to see how such judgements could be made immune from charges of ideological bias or conflict of interest, as they would rely on the judgement of a single individual.

Market judges don’t have to be individuals; there could be panels of judges. And existing institutions are also often open to charges of bias and conflicts of interest.

Unfortunately many responses to reform proposals fit the above pattern: reject the reform because it isn’t as good as perfection, ignoring the fact that the status quo is nothing like perfection.

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Hazlett’s Political Spectrum

By Robin Hanson · August 4, 2017 8:40 pm · Comments (8)

I just read The Political Spectrum by Tom Hazlett, which took me back to my roots. Well over three decades ago, I was inspired by Technologies of Freedom by Ithiel de Sola Pool. He made the case both that great things were possible with tech, and that the FCC has mismanaged the spectrum. In grad school twenty years ago, I worked on FCC auctions, and saw mismanagement behind the scenes.

When I don’t look much at the details of regulation, I can sort of think that some of it goes too far, and some not far enough; what else should you expect from a noisy process? But reading Hazlett I’m just overwhelmed by just how consistently terrible is spectrum regulation. Not only would everything have been much better without FCC regulation, it actually was much better before the FCC! Herbert Hoover, who was head of the US Commerce Department at the time, broke the spectrum in order to then “save” it, a move that probably helped him rise to the presidency:

“Before 1927,” wrote the U.S. Supreme Court, “the allocation of frequencies was left entirely to the private sector . . . and the result was chaos.” The physics of radio frequencies and the dire consequences of interference in early broadcasts made an ordinary marketplace impossible, and radio regulation under central administrative direction was the only feasible path. “Without government control, the medium would be of little use because of the cacaphony [sic] of competing voices.”

This narrative has enabled the state to pervasively manage wireless markets, directing not only technology choices and business decisions but licensees’ speech. Yet it is not just the spelling of cacophony that the Supreme Court got wrong. Each of its assertions about the origins of broadcast regulation is demonstrably false. ..

The chaos and confusion that supposedly made strict regulation necessary were limited to a specific interval—July 9, 1926, to February 23, 1927. They were triggered by Hoover’s own actions and formed a key part of his legislative quest. In effect, he created a problem in order to solve it. ..

Radio broadcasting began its meteoric rise in 1920–1926 under common-law property rules .. defined and enforced by the U.S. Department of Commerce, operating under the Radio Act of 1912. They supported the creation of hundreds of stations, encouraged millions of households to buy (or build) expensive radio receivers. .. The Commerce Department .. designated bands for radio broadcasting. .. In 1923, .. [it] expanded the number of frequencies to seventy, and in 1924, to eighty-nine channels .. [Its] second policy was a priority-in-use rule for license assignments. The Commerce Department gave preference to stations that had been broadcasting the longest. This reflected a well-established principle of common law. ..

Hoover sought to leverage the government’s traffic cop role to obtain political control. .. In July 1926, .. Hoover announced that he would .. abandon Commerce’s powers. .. Commerce issued a well-publicized statement that it could no longer police the airwaves. .. The roughly 550 stations on the air were soon joined by 200 more. Many jumped channels. Conflicts spread, annoying listeners. Meanwhile, Commerce did nothing. ..

Now Congress acted. An emergency measure .. mandated that all wireless operators immediately waive any vested rights in frequencies ..  the Radio Act … provided for allocation of wireless licenses according to “public interest”.  .. With the advent of the Federal Radio Commission in 1927, the growth of radio stations—otherwise accommodated by the rush of technology and the wild embrace of a receptive public—was halted. The official determination was that less broadcasting competition was demanded, not more.

That was just the beginning. The book documents so so much more that has gone very wrong. Even today, vast valuable spectrum is wasted broadcasting TV signals that almost no one uses, as most everyone gets cable TV. In addition,

The White House estimates that nearly 60 percent of prime spectrum is set aside for federal government use .. [this] substantially understates the amount of spectrum it consumes.

Sometimes people argue that we need an FCC to say who can use which spectrum because some public uses are needed. After all, not all land can be private, as we need public parks. Hazlett says we don’t use a federal agency to tell everyone who gets which land. Instead the public buys general land to create parks. Similarly, if the government needs spectrum, it can buy it just like everyone else. Then we’d know a lot better how much any given government action that uses spectrum is actually costing us.

Is the terrible regulation of spectrum an unusual case, or is most regulation that bad? One plausible theory is that we are more willing to believe that a strange complex tech needs regulating, and so such things tend to be regulated worse. This fits with nuclear power and genetically modified food, as far as I understand them. Social media has so far escaped regulation because it doesn’t seem strange – it seems simple and easy to understand. It has complexities of course, but behind the scenes.

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Foom Justifies AI Risk Efforts Now

By Robin Hanson · August 3, 2017 10:05 am · Comments (51)

Years ago I was honored to share this blog with Eliezer Yudkowsky. One of his main topics then was AI Risk; he was one of the few people talking about it back then. We debated this topic here, and while we disagreed I felt we made progress in understanding each other and exploring the issues. I assigned a much lower probability than he to his key “foom” scenario.

Recently AI risk has become something of an industry, with far more going on than I can keep track of. Many call working on it one of the most effectively altruistic things one can possibly do. But I’ve searched a bit and as far as I can tell that foom scenario is still the main reason for society to be concerned about AI risk now. Yet there is almost no recent discussion evaluating its likelihood, and certainly nothing that goes into as much depth as did Eliezer and I. Even Bostrom’s book length treatment basically just assumes the scenario. Many seem to think it obvious that if one group lets one AI get out of control, the whole world is at risk. It’s not (obvious).

As I just revisited the topic while revising Age of Em for paperback, let me try to summarize part of my position again here. Continue reading "Foom Justifies AI Risk Efforts Now" »

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Philosophy Vs. Duck Tests

By Robin Hanson · July 21, 2017 12:40 pm · Comments (8)

Philosophers, and intellectuals more broadly, love to point out how things might be more complex than they seem. They identify more and subtler distinctions, suggest more complex dependencies, and warn against relying on “shallow” advisors less “deep” than they. Subtly and complexity is basically what they have to sell.

I’ve often heard people resist such sales pressure by saying things like “if it looks like a duck, walks like a duck, and quacks like a duck, it’s a duck.” Instead of using complex analysis and concepts to infer and apply deep structures, they prefer to such use a “duck test” and judge by adding up many weak surface clues. When a deep analysis disagrees with a shallow appearance, they usually prefer to go shallow.

Interestingly, this whole duck example came from philosophers trying to warn against judging from surface appearances: Continue reading "Philosophy Vs. Duck Tests" »

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High Dimensional Societes?

By Robin Hanson · July 17, 2017 2:50 pm · Comments (3)

I’ve seen many “spatial” models in social science. Such as models where voters and politicians sit at points in a space of policies. Or where customers and firms sit at points in a space of products. But I’ve never seen a discussion of how one should expect such models to change in high dimensions, such as when there are more dimensions than points.

In small dimensional spaces, the distances between points vary greatly; neighboring points are much closer to each other than are distant points. However, in high dimensional spaces, distances between points vary much less; all points are about the same distance from all other points. When points are distributed randomly, however, these distances do vary somewhat, allowing us to define the few points closest to each point as that point’s “neighbors”. “Hubs” are closest neighbors to many more points than average, while “anti-hubs” are closest neighbors to many fewer points than average. It turns out that in higher dimensions a larger fraction of points are hubs and anti-hubs (Zimek et al. 2012).

If we think of people or organizations as such points, is being a hub or anti-hub associated with any distinct social behavior?  Does it contribute substantially to being popular or unpopular? Or does the fact that real people and organizations are in fact distributed in real space overwhelm such things, which only only happen in a truly high dimensional social world?

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“Human” Seems Low Dimensional

By Robin Hanson · July 15, 2017 10:30 am · Comments (6)

Imagine that there is a certain class of “core” mental tasks, where a single “IQ” factor explains most variance in such task ability, and no other factors explained much variance. If one main factor explains most variation, and no other factors do, then variation in this area is basically one dimensional plus local noise. So to estimate performance on any one focus task, usually you’d want to average over abilities on many core tasks to estimate that one dimension of IQ, and then use IQ to estimate ability on that focus task.

Now imagine that you are trying to evaluate someone on a core task A, and you are told that ability on core task B is very diagnostic. That is, even if a person is bad on many other random tasks, if they are good at B you can be pretty sure that they will be good at A. And even if they are good at many other tasks, if they are bad at B, they will be bad at A. In this case, you would know that this claim about B being very diagnostic on A makes the pair A and B unusual among core task pairs. If there were a big clump of tasks strongly diagnostic about each other, that would show up as another factor explaining a noticeable fraction of the total variance. Making this world higher dimensional. So this claim about A and B might be true, but your prior is against it.

Now consider the question of how “human-like” something is. Many indicators may be relevant to judging this, and one may draw many implications from such a judgment. In principle this concept of “human-like” could be high dimensional, so that there are many separate packages of indicators relevant for judging matching packages of implications. But anecdotally, humans seem to have a tendency to “anthropomorphize,” that is, to treat non-humans as if they were somewhat human in a simple low-dimensional way that doesn’t recognize many dimensions of difference. That is, things just seem more or less human. So the more ways in which something is human-like, the more you can reasonably guess that it will be human like in other ways. This tendency appears in a wide range of ordinary environments, and its targets include plants, animals, weather, planets, luck, sculptures, machines, and software. Continue reading "“Human” Seems Low Dimensional" »

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Boost For Being Best

By Robin Hanson · July 12, 2017 10:30 am · Comments (23)

The fraction of a normal distribution that is six or more standard deviations above the mean is one in ten billion. But the world has almost eight billion people in it. So in principle we should be able to get six standard deviations in performance gain by selecting the world’s best person at something, compared to using an average person.

I’m revising Age of Em for a paperback edition, expected in April. The rest of this post is from a draft of new text elaborating that point, and its implication for em leisure:

Em workers also earn wage premiums when they are the very best in the world at what they do. Even under the most severe wage competition, a best em can earn an extra wage equal to the difference between their productivity and the productivity of the second best em. When clans coordinate internally on wage negotiations, this is the difference in productivity between clans. (Clans who can’t coordinate internally are selected out of the em world, as they don’t cover their fixed costs, such as for training and marketing.)

Out of 10 billion independently and normally distributed (IID) samples, the maximum is on average about 6.4 standard deviations above the mean. Average spacings between the second, third, fourth highest samples are roughly 0.147, 0.075, and 0.05 standard deviations respectively (Branwen 2017). So when ems are selected out of 10 billion humans, the best em clan may be this much better than other em clans on normally distributed parameters. Using the log-normal wage distribution observed in our world (Provenzano 2015), this predicts that the best human in the world at any particular task is four to five times more productive than the median person, is over three percent more productive than the second most productive person, and is five percent more productive than the third most productive person.

If em clan relative productivity is drawn from this same distribution, if maximum em productivity comes at a 70 hour workweek, and if the best and second best em clans do not coordinate on wages they accept, then even under the strongest wage competition between clans, the best clan could take an extra 20 minutes a day more leisure, or two minutes per work hour, in addition to the six minutes per hour and other work breaks they take to be maximally productive.

This 20 minute figure is an underestimate for four reasons. First, the effective sample size of ems is smaller due to age limits on desirable ems. Second, most parameters are distributed so that the tails are thicker than in the normal distribution (Reed and Jorgensen 2004).

Third, differing wealth effects may add to differing productivity effects. On average over the last 11 years, the five richest people on Earth have each been about 10 percent richer than the next richest person. If future em income ratios were like this current wealth ratio, then the best em worker could afford roughly an extra hour per day of leisure, or an additional six minutes per hour.

Fourth, competition probably does not take the strongest possible form, and the best few ems can probably coordinate to some extent. For example, if the best two em clans coordinate completely on wages, but compete strongly with the third best clan, then instead of the best and second best taking 20 and zero minutes of extra leisure per day, they could take 30 and 10 extra minutes, respectively.

Plausibly then, the best em workers can afford to take an additional two to six minutes of leisure per hour of work in a ten hour work day, in addition to the over six minutes per hour of break needed for maximum productivity.

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A Post-Em-Era Hint

By Robin Hanson · July 4, 2017 5:10 pm · Comments (8)

A few months ago I noticed a pattern across the past eras of forager, farmer industry: each era has a major cycle (ice ages, empires rise & fall, business cycle) with a period of about one third of that era’s doubling time. So I tentatively suggested that a em future might also have a major cycle of roughly one third of its doubling time. If that economic doubling time is about a month, the em major cycle period might be about a week.

Now I report another pattern, to be treated similarly. In roughly the middle of each past era, a pair of major innovations in calculating and communicating appeared, and gradually went from barely existing to having big social impacts.

  • Forager: At unknown periods during the roughly two million year forager era, humanoids evolved reasoning and language. That is, we became able to think about and say many complex things to each other, including our reasons for and against claims.
  • Farmer: While the farming era lasted roughly 7 to 10 millennia, the first known writing was 5 millennia ago, and the first known math textbooks 4 millennia ago. About 2.5 millennia ago writing became widespread enough to induce major religious changes worldwide.
  • Industry: While the industry era has lasted roughly 16 to 24 decades, depending on how you count, the telegraph was developed 18 decades ago, and the wholesale switch from mechanical to digital electronic communication happened 4 to 6 decades ago. The idea of the computer was described 20 decades ago, the first digital computer was made 7 decades ago, and computers became widespread roughly 3 decades ago.

Note that innovations in calculation and communication were not independent, but instead intertwined with and enabled each other. Note also that these innovations did not change the growth rate of the world economy at the time; each era continued doubling at the same rate as before. But these innovations still seem essential to enabling the following era. It is hard to imagine farming before language and reasoning, nor industry before math and writing, nor ems before digital computers and communication.

This pattern weakly suggests that another pair of key innovations in calculation and communication may appear and then grow in importance across a wide middle of the em era. This era may only last a year or two in objective time, though typical ems may experience millennia during this time.

This innovation pair would be interdependent, not change the growth rate, and perhaps enable a new era to follow. I can think of two plausible candidates:

  1. Ems might discover a better language for expressing and manipulating something like brain states. This could help ems to share their thoughts and use auxiliary hardware to help calculate useful thoughts.
  2. Ems might develop analogues to combinatorial prediction markets, and thus better share beliefs and aggregate information on a wide range of topics.

(Or maybe the innovation produces some combination of these.) Again, these are crude speculations based on a weak inference from a rough pattern in only three data points. But even so, they give us a vague hint about what an age after ems might look like. And such hints are actually pretty hard to find.

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Beware The Moral Spotlight

By Robin Hanson · June 29, 2017 3:30 pm · Comments (18)

Imagine a large theatre with a singer at center stage. A single bright spotlight illuminates this singer, and the rest of the crowded theatre is as dark as can be, given this arrangement. Morality can do the same thing in the theatre of our mind. Once one issue or choice gets a strong moral color, we can focus on it so much that we just don’t see a much larger theatre of action. This is fine when our moral sense works well. If one murder were happening in a stadium of 50,000 people, it could make sense for the Jumbotron to project it onto the big screen, and for the whole stadium to focus on it, to help them do something about it.

But our moral sense often doesn’t work well. We are so obsessed with showing off our moral feelings and inclinations, relative to being useful to a larger world, that we neglect large theatres where we could be useful, to obsess with a small circle highlighted by our moral spotlight, where we can’t actually do much. Let me give three examples.

1. Some friends were recently arguing about the motives of CEOs, relative to politicians and heads of government agencies. One person was arguing that people go into government in order to help others, but go into business to make money. Thus it is better, all else equal, for activities to be run by government. Another person argued that real business people have a wide range of motives, as do real government people. But first person pointed to official statements of purpose, claiming that governments say on paper that they are to help people, while businesses say on paper that they are to make money.

But even if business and government people do differ on average in their motives, you don’t get to elite positions in either area without paying close attention to the great many practical constraints that each area imposes. Business people must attend to customer reactions, employee moral, media coverage, etc. Government people must attend to official procedures, voter sentiment, rival factions maneuvering, etc. Elites must usually navigate such treacherous shoals successfully for decades before they are allowed to make big decisions on behalf of any organization.

Those selection pressures are what determine most behavior in both areas. If business or government is better at running activities, it is mostly because of differences in those pressures. Any remaining behavior differences due to fundamental motives being influenced by official statements of purpose must be small by comparison. While your moral spotlight might want to focus on purpose-statement-induced-motives, most of what matters is elsewhere.

2. I recently watched the documentary The Red Pill, which mostly reviewed Men’s Rights Movement arguments that I had encountered decades before in the book The Myth of Male Power. They point out that many official rules and widely held expectations, as well as many concrete typical outcomes, are unfavorable to men. Their talks and meetings have faced rude and violent interference by those who see this as undermining feminist consciousness-raising regarding areas where official rules and widely held expectations have been and to some extent continue to be unfavorable to women.

The conflict seems to come down emotionally to a perception of which sex is getting the worse deal overall. And there may in fact be some truth of that matter; maybe one sex does have a worse deal. But many seem eager to infer the existence of an entire system, e.g., “patriarchy”, designed in detail to achieve this worse-for-one-sex outcome, entrenched via the direct support of malicious people from the favored sex, and implicit support from most of the rest of that sex.

This seems to me to mostly result from a moral spotlight in overdrive. Yes one sex may have a worse deal overall. But most of the ways in which we’ve had sex-assymetric official rules and widely held expectations did not result from a conspiracy by one sex to repress the other. They were mostly reasonable responses to sex differences relevant in ancient societies. We may have failed to adapt them quickly enough to our new modern context. But many of them are still complex and difficult issues. We’d do better to roll up our sleeves and deal with each one, than to obsess over which sex has the worse overall deal.

3. When people think about changes they’d like in the world one of their first thoughts, and one they return to often, is wanting more democracy. It’s their first knee-jerk agenda for China, North Korea, ISIS, and so on. Surely with more democracy all the other problems would sort themselves out.

But in fact scholars can find few consistent difference in the outcomes of nations that depend much on their degree of democracy. Democracy doesn’t seem to cause differences in wealth, or even in most specific policies. Democracies today war a bit less, but in the past democracies warred more than others. Democracies have less political repression, and our moral spotlight finds that fact to be of endless fascination. But it is in fact a relatively small effect on nations overall.

Nations today have huge differences in outcomes, and we are starting to understand some of them. But most of them have little to do with democracy. Plausibly larger issues include urbanization, immigration, foreign trade, regulation, culture, rule of law, corruption, suppression or encouragement of family clans or religion, etc. If you want to help nations, you’ll have to look outside the moral spotlight on democracy.

Yes, why should you personally sacrifice to help the world? The world will reward you for taking a clear moral stance regarding whatever is in the shared moral spotlight. And it will suspect you of immorality and disloyalty if you pay too little attention to that spotlight. So why should you look elsewhere? I think you know.

Added 3 July: Bryan Caplan points out that democracy can reduce the worst excesses of totalitarian governments. I accept that point; I had in mind less extreme variations, so North Korea was a poor choice on my part.

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Intellectuals as Artists

By Robin Hanson · June 28, 2017 8:12 am · Comments (15)

Consider some related phenomena:

  1. Casual conversation norms say to wander across many topics, with each person staying relevant to each current topic. This functions well to test individual impressiveness. Today, academic and mass media conversations today follow similar norms, though they did this much less in the ancient world.
  2. While ancient artists and musicians tried to perfect common styles, modern artists and musicians seek more distinctive personal styles. For example, while songs were once designed to sound good when ordinary folks sang them, now songs are designed to create a unique impressive performance by one artist.
  3. Politicians often go out of their way to do “position taking” on many issues, even on issues they have little chance of influencing policy while in office. Voters prefer systems like proportional representation where voters can identify more closely with particular representatives, even if this doesn’t give voters better outcomes overall. Knowing many of a politician’s positions helps voters to identify with them.
  4. “Sophomoric” thinkers, typically college sophomores, are eager to take positions on as many common topics as possible, even if this means taking poorly consider positions. They don’t feel they are adult until they have an opinion ready for most common intellectual conversations. This is more feasible when opinions on each topic area are reduced to choices between a small number of standard “isms”, offering integrated packages of answers. Sophomoric thinkers love isms.
  5. We often try to extract “isms” out of individuals, such as my colleagues Tyler Cowen or Bryan Caplan. We might ask “What is the Caplanian position on X?” That is, we wonder how they would answer random questions, presuming that we can infer a coherent style from past positions that would answer all future questions, at least within some wide scope. Intellectuals who desire wider attention often go out of their way to express opinions on many topics, chosen via a distinctive personal style.

We pretend that we search only for truth, picking each specific position only via the strongest specific evidence and arguments. And in many mundane contexts that’s not a bad approximation. But in many other grander contexts we seek more to become and associate with distinctive intellectual artists. Such artists are impressive both via the wide range of topics on which they can be impressive, and via having a distinctive personal style that they can bring to bear on this range of topics.

This all makes complete sense as an impressiveness contest, but far less sense as a way for the world to jointly estimate accurate Bayesian estimates on each topic. I’m sure you can make up reasons why distinctive intellectual styles that imply positions on wide ranges of topics are really great ways to produce accuracy. But they will mostly sound like excuses to me.

Sophomoric thinkers often retain for a lifetime the random opinions they quickly generate without much thought. Yet they don’t want to just inherit their parents positions; they need to generate their own new opinions. I wonder which effect will dominate when young ems choose opinions; will they tend to adopt standard positions of prior clan members, or generate their own new individual opinions?

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