# Monthly Archives: August 2017

## Organic Prestige Doesn’t Scale

Some parts of our world, such as academia, rely heavily on prestige to allocate resources and effort; individuals have a lot of freedom to choose topics, and are mainly rewarded for seeming impressive to others. I’ve talked before about how some hope for a “Star Trek” future where most everything is done that way, and I’m now reading Walkaway, outlining a similar hope. I was skeptical:

In academia, many important and useful research problems are ignored because they are not good places to show off the usual kinds of impressiveness. Trying to manage a huge economy based only on prestige would vastly magnify that inefficiency. Someone is going to clean shit because that is their best route to prestige?! (more)

Here I want to elaborate on this critique, with the help of a simple model. But first let me start with an example. Imagine a simple farming community. People there spend a lot of time farming, but they must also cook and sew. In their free time they play soccer and sing folk songs. As a result of doing all these things, they tend to “organically” form opinions about others based on seeing the results of their efforts at such things. So people in this community try hard to do well at farming, cooking, sewing, soccer, and folk songs.

If one person put a lot of effort into proving math theorems, they wouldn’t get much social credit for it. Others don’t naturally see outcomes from that activity, and not having done much math they don’t know how to judge if this math is any good. This situation discourages doing unusual things, even if no other social conformity pressures are relevant.

Now let’s say that in a simple model. Let there be a community containing people j, and topic areas i where such people can create accomplishments aij. Each person j seeks a high personal prestige pj = Σi vi aij, where vi is the visibly of area i. They also face a budget constraint on accomplishment, Σi aij2 ≤ bj. This assumes diminishing returns to effort in each area.

In this situation, each person’s best strategy is to choose aij proportional to vi. Assume that people tend to see the areas where they are accomplishing more, so that visibility vi is proportional to an average over individual aij. We now end up with many possible equilibria having different visibility distributions. In each equilibria, for all individuals j and areas i,k we have the same area ratios aij / akj = Vi/ Vk.

Giving individuals different abilities (such as via a budget constraint Σi aij2 / xij ≤ bj) could make individual choose somewhat different accomplishments, but the same overall result obtains. Spillovers between activities in visibility or effort can have similar effects. Making some activities be naturally more visible might push toward those activities, but there could still remain many possible equilibria.

This wide range of equilibria isn’t very reassuring about the efficiency of this sort of prestige. But perhaps in a small foraging or farming community, group selection might over a long run push toward an efficient equilibria where the high visibility activates are also the most useful activities. However, larger societies need a strong division of labor, and with such a division it just isn’t feasible for everyone to evaluate everyone else’s specific accomplishments. This can be solved either by creating a command and status hierarchy that assigns people to tasks and promotes by merit, or by an open market with prestige going to those who make the most money. People often complain that doing prestige in these ways is “inauthethnic”, and they prefer the “organic” feel of personally evaluating others’ accomplishments. But while the organic approach may feel better, it just doesn’t scale.

In academia today, patrons defer to insiders so much regarding evaluations that disciplines become largely autonomous. So economists evaluate other economists based mostly on their work in economics. If someone does work both in economics and also in aother area, they are judged mostly just on their work in economics. This penalizes careers working in multiple disciplines. It also suggests doubts on if different disciplines get the right relative support – who exactly can be trusted to make such a choice well?

Interestingly, academic disciplines are already organized “inorganically” internally. Rather than each economist evaluating each other economist personally, they trust journal editors and referees, and then judge people based on their publications. Yes they must coordinate to slowly update shared estimates of which publications count how much, but that seems doable informally.

In principle all of academia could be unified in this way – universities could just hire the candidates with the best overall publication (or citation) record, regardless of in which disciplines they did what work. But academia hasn’t coordinated to do this, nor does it seem much interested in trying. As usual, those who have won by existing evaluation criteria are reluctant to change criteria, after which they would look worse compared to new winners.

This fragmented prestige problem hurts me especially, as my interests don’t fit neatly into existing groups (academic and otherwise). People in each area tend to see me as having done some interesting things in their area, but too little to count me as high status; they mostly aren’t interested in my contributions to other areas. I look good if you count my overall citations, for example, but not if you only my citations or publications in each specific area.

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## Compare Institutions To Institutions, Not To Perfection

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

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

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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