The Chinese Robber Fallacy

The Chinese Robber Fallacy is where you use a generic problem to attack a specific person or group, even though other groups have the problem just as much (or even more so).

Suppose you’re racist against Chinese people. You can go on the Internet and say:

“Man, screw the Chinese. The Chinese are thieves.”

And when someone replies: “Hey, is that really true?”

“Yeah! Just look at <example> and <example> and <example> of these robberies by evil Chinese criminals.”

“Sure, but that’s just anecdotal evidence.”

“Our statistics say that Chinese people commit an average of <big number> thefts a year. That’s a lot! How could you trust a Chinese person?”

“But don’t non-Chinese rob people too?”

“Maybe, but if so, that doesn’t make the Chinese any less guilty, does it? First we should deal with the Chinese criminal problem, and then if we’re successful, maybe we can move on to other types of theft.”

“Are the Chinese really the first group we should target in our anti-theft campaign?”

“Hey, quit trying to change the subject. Are you trying to deny the immorality of stealing people’s hard-earned property? Why, just go into Chinatown and walk around for a while, you’ll see a Chinese mugger soon enough, it’s right there in front of our eyes… “

Checking Out The Necronomicon

(Song by Raymond Arnold. To the tune of ‘Winter Wonderland’.)

Dusty tome, lies forgotten
Cover worn, pages rotten
A curious book
I’ll just take a look
Checking out the Necronomicon

Creepy words, pages turnin’
As your brain, is a churnin’
Insidious memes
Infecting your dreams
Haunted by the Necronomicon

In the graveyard I can make a promise.
That is not dead which eternal lies,
Soon I’ll reunite with brother Thomas!
(For with) strange aeons even death may die…

More I’ve read, the more I’m listenin’
In my head, voices whisperin’…
‘Tonight is the night’,
‘The stars are all right’,
Time to use the Necronomicon

In the graveyard we could raise an army,
Send it out to ravage all the land…
Sure, the thought may seem a bit alarming,
(But if you) read the book, I swear you’ll understand!

Later on, we’ll conspire
As we dream by the fire
To face unafraid
The plans that we made
Studying the Necronomicon

Robin Hanson on Cold Fusion

(Quotes taken from a Facebook thread)

Robin: “This report on a test of the E-Cat cold-fusion device is disturbing. Our theoretical prior is against it, but this does look competent, if not elegant or overwhelming. I think it has earned a replication attempt; someone should try to replicate it.”

Me: “This is fraud every six ways from Sunday.”

Matthew Bailey: “This “independent verification” is biased just like the previous one was. Rossi chose someone who was heavily invested in the E-Cat to “review” it (making them not so independent). Remember, about a year ago, he did this EXACT same thing.

Yes… It has earned a Replication Attempt. BUT…………………….. Rossi won’t tell anyone what is in the actual Reaction Vessel so that it CAN be replicated. Remember, this is the guy who has been convicted of Fraud about half a dozen times, nearly every one of them involving some form of “Free Energy.”

As someone else said: The priors seem to indicate that this too is fraud.”

Robin: “Matthew, I agree Rossi’s track record is a big cause for concern, and justifies a very careful test watching for that. But I don’t think one should need to see Reaction Vessel details to attempt replication.”

Me: “Robin: what are your odds that Rossi’s discovered anything which might plausibly be useful for fusion? Want to make a bet?”

Robin: “Alyssa, I’d give at least 2%, so 50-1 odds. That tempt you?”

Me: “Robin: You’re on. $10,000 against $200? Am at work now but can draw up formal terms tomorrow.”

Robin: “Alyssa, can you wait 25 years for settlement? It has already been 25 years since the first cold fusion results were claimed, so clearly this subject takes a very long time to sort out.”

Me: “The claim is not for cold fusion generally but for one specific device. Fleischmann’s results from 1989 were thoroughly debunked within a year or two (for that specific experiment).” [link for context]

Robin: “Conditional on this new device working as claimed, it seems not at all clear the ’89 results were debunked. I could give $200 to one of my sons, and make him responsible for paying you, so you could be assured the responsible party will probably be alive. It could be $200 then, or what $200 today becomes using a standard investment vehicle.”

Me: “What evidence would you accept, then, as it having been debunked? The 1989 results were given dozens of replication attempts, all of which failed.”

Robin: “Showing that experiments didn’t rise to a reasonable standard of proof to convince an audience is not at all the same as a showing that its claim was false. Just as a lack of a statistically significant estimate of a parameter is not proof that the parameter equals zero.”

Me: “Robin: Can you name one single case, in the history of the hard sciences (physics, chemistry, biology, etc.), where:

a) a specific experiment was conducted, and it produced results which were both implausible under contemporary science and commercially valuable;

b) at least a dozen other people tried the same experiment, with the exact same setup and materials and tools and equipment, and got different data which were in accordance with science as then understood and indicated no commercial value;

c) the results from a) were later shown to be real after all?”

Robin: “Alyssa, I don’t have time or stats to debate all possible experiment scenarios. Experiments are almost never exactly the same, that is why replication is hard. See Collins’ book Changing Order, for example.”

Me: “Collins spent a lot of time investigating the paranormal, and was apparently convinced by proponents that it was at least plausible. That certainly doesn’t mean he’s wrong about everything (Newton wasted years on theology), but as a source of information on the scientific method it makes him highly suspect.”

Robin: “Collins is an excellent sociologist of science.”

[context: Collins’ paper “The construction of the paranormal: Nothing unscientific is happening” sadly does not seem to be online. However, the book Robin recommends (Changing Order) is on Google Books in snippet form, and in it Collins says (for example) “It is particularly unfair to expose fragile new sciences to the rigours of critiques based on a canonical model which even established sciences cannot attain. The ‘Committee for the Scientific Investigation of the Claims of the Paranormal’ and other such vigilante organizations are suspect in this regard.”]

Academic Support for MIRI

This is a response to su3su2u1‘s critique of the Machine Intelligence Research Institute (MIRI).

“MIRI bills itself as a research institute, so I judge them on their produced research. The accountability measure of a research institute is academic citations.”

The author is obviously smart, but there are really two distinct claims here, and he/she confuses the issue by equivocating between them:

Claim 1: Number of academic citations is in fact a perfect or near-perfect indicator of the quality/importance of a body of research. Hence, if a body of work has few citations, we can safely ignore it as low-quality or unimportant.

Claim 2: Number of academic citations is treated by certain institutions as such an indicator. Hence, to obtain status within these institutions, it is instrumentally useful to get more citations.

I think claim #1 can be easily shown to be false. There are many strong arguments against it, but one obvious one is that the absolute number of citations is equal to the fraction of people in a field who cite something, times the total number of people working in that field. And the size of fields varies wildly from place to place.

Eg., consider the paper “The entropy formula for the Ricci flow and its geometric applications“. This paper, which proved the century-old Poincare conjecture, was hailed as one of the most important mathematical advances of the 21st century. It was the first paper to win a million-dollar Millennium Prize, and Science named the paper as its “Breakthrough of the Year”, the only time it has ever done so for a mathematical result (as opposed to a discovery in the physical world). According to Google Scholar, it has been cited 1,382 times.

Now, contrast Perelman’s famous paper with the medical research paper “Familial Alzheimer’s disease in kindreds with missense mutations in a gene on chromosome 1 related to the Alzheimer’s disease type 3 gene“. This paper has 1,760 citations. I wouldn’t call it “unimportant”, but I doubt even Rogaev (the lead author) would claim it’s more important than proving the Poincare conjecture. Medical research is simply a much larger field than mathematics, and so medical papers will get many more cites than math papers of equal importance. Even MIRI’s toughest critics would be hard-pressed to argue that MIRI’s research is less important or high-quality than a doctor rediscovering freshman calculus, a paper which got 75 citations.

By definition, the foundational work in any field is done when it’s new and small. And a new and small field will always have fewer citations than an established one, partly because of the issue above (fewer researchers = fewer citations), and partly because there’s been less time for citations to accumulate. So, if we are to believe Claim #1, foundational work is lower quality and less important than work in a mature field where the low-hanging fruit is already picked. I think everyone remembers prominent counterexamples.

More on Claim #2 in a bit.

“You can measure how much influence they [MIRI] have on researchers by seeing who those researchers cite and what they work on. You could have every famous cosmologist in the world writing op-eds about AI risk, but its worthless if AI researchers don’t pay attention, and judging by citations, they aren’t. (…) This isn’t because I’m amazing, its because no one in academia is paying attention to MIRI.”

This is a separate, third, claim: that MIRI’s number of citations is a good measure of how many researchers are paying attention to it. This claim is not justified, it’s simply assumed. And if one directly asks the question “how many prominent academics are paying attention to MIRI?” – rather than simply assuming citations are a good proxy, and measuring the proxy – even the most cursory Googling shows the answer is “quite a lot”. A very far from complete list:

… and one could go on for a while, but I think the point is made. When data and theory contradict, one must throw out the theory; you don’t keep the theory and throw out the data.

“And yes, I agree this one result looks interesting, but most mathematicians won’t pay attention to it unless they get it reviewed.”

This is an argument from claim #2: that regardless of whether citations to peer-reviewed papers are a good measure or not, you need them to get credibility. Claim #2 is, in fact, largely true within American research universities. However, I think it’s not true for many individual scientists, a number of whom have published scathing critiques of the current academic publication system. I’m pretty sure many, possibly most, younger researchers in math and computer science think of publishing in Elsevier and other for-profit journals as a necessary evil to get ahead within the current system. Since MIRI isn’t part of that system, why should they?

The author later suggests that MIRI should post their math papers on arXiv, one alternative to typical journals. This is a great idea, and I support it 100%. However, the original claim was not that MIRI should post to arXiv, but that (to quote) “Based on their output over the last decade, MIRI is primarily a fanfic and blog-post producing organization. That seems like spending money on personal entertainment.” This is simply not supported by the evidence.

“If they are making a “strategic decision” to not submit their self-published findings to peer review, they are making a terrible strategic decision, and they aren’t going to get most academics to pay attention that way.”

This is another argument from claim #2, and it flies in the face of all the evidence mentioned previously. Moreover, MIRI’s main goal (unlike labs that need government grants) is not to maximize academic attention, but just to get math done as quickly as possible. Some attention is probably good, but too much would be actively harmful: being a celebrity is really distracting and a huge time sink.

Moreover, any academic will tell you that peer review is not simply “submitting” a research paper, the way one submits an essay in undergrad. It is typically a months-long process that demands large amounts of time and mental capacity. This cost becomes obvious when you consider that MIRI once had seven writeups from a single week-long workshop. Even if a few of these writeups were combined into larger papers, how many weeks would it take to get them all peer-reviewed? Twenty? Forty?

“I didn’t know Russell was in any way affiliated with MIRI, he is nowhere mentioned on their staff page, and has never published a technical result with them.”

Russell and Norvig on Friendly AI

And while this other interview doesn’t explicitly mention MIRI, it’s pretty obvious that the ideas derive from Yudkowsky, Bostrom, and other MIRI-sphere folks:

“It’s very difficult to say what we would want a super intelligent machine to do so that we can be absolutely sure that the outcome is what we really want as opposed to what we say. That’s the issue. I think we, as a field, are changing, going through a process of realization that more intelligent is not necessarily better. We have to be more intelligent and controlled and safe, just like the nuclear physicist when they figured out chain reaction they suddenly realized, “Oh, if we make too much of a chain reaction, then we have a nuclear explosion.” So we need controlled chain reaction just like we need controlled artificial intelligence.”

“If he [Russell] is interested in helping MIRI, the best thing he could do is publish a well received technical result in a good journal with Yudkowsky. That would help get researchers to pay actual attention.”

I don’t doubt that this would be a good thing, but it’s at least worth noting that MIRI has a long history of being advised to do various things to get more academic credibility, and this advise failing more often than not:

“If the one is called upon to explain the rejection, not uncommonly the one says, “Why should I believe anything Yudkowsky says? He doesn’t have a PhD!” And occasionally someone else, hearing, says, “Oh, you should get a PhD, so that people will listen to you.” Or this advice may even be offered by the same one who disbelieved, saying, “Come back when you have a PhD.” (…)

And more to the point, if I had a PhD, people would not treat this as a decisive factor indicating that they ought to believe everything I say. Rather, the same initial rejection would occur, for the same reasons; and the search for justification, afterward, would terminate at a different stopping point. They would say, “Why should I believe you? You’re just some guy with a PhD! There are lots of those. Come back when you’re well-known in your field and tenured at a major university.” (…)

It has similarly been a general rule with the Singularity Institute [now MIRI] that, whatever it is we’re supposed to do to be more credible, when we actually do it, nothing much changes. “Do you do any sort of code development? I’m not interested in supporting an organization that doesn’t develop code” —> OpenCog —> nothing changes. “Eliezer Yudkowsky lacks academic credentials” —> Professor Ben Goertzel installed as Director of Research —> nothing changes. The one thing that actually has seemed to raise credibility, is famous people associating with the organization, like Peter Thiel funding us, or Ray Kurzweil on the Board.”

Moreover, it’s not at all obvious that publishing with Russell or other famous professors (in and of itself) gets people that much attention. Over Russell’s lengthy career, how many Berkeley grad students have co-authored with him? And of those, how many got anywhere near as much academic attention as MIRI already has (as demonstrated by the above links) as a direct result of co-authoring, rather than becoming famous for something else years later? I haven’t counted, but I know which way I’d bet.

(Disclaimer: I am not a MIRI employee, and do not speak for MIRI.)

Typology of Conflict

In an idealized far future, would there be conflict? I think so. Competition is one of the thousand shards of human desire, and a lot of people would be sad if there were no more football games or chess or Team Fortress 2.

But such conflicts are not driven by universal goals. Here I must handwave a bit as to what “universal goal” means, but it is something in the neighborhood of being a utilitarian-style drive, rather than a biological-style drive. A human (or other animal) who wants a cheeseburger won’t, even if given the chance, obsessively optimize the atoms of Alpha Centauri to maximize cheeseburger probability. A naively-constructed AI would, giving rise to the problem Nick Bostrom calls “infrastructure profusion”. A “universal goal” is, roughly, one that you would optimize everything in the Universe to meet, not a chess match where you forget about losing the week after.

In a scenario high on the coordination axis, there would be no meaningful conflict over universal goals. Everything with the power to affect the entire universe would agree about non-trivial aspects of how to do so. This is what Bostrom calls the “singleton” scenario, and it’s likely to obey Stein’s Principle: such a system would have both a strong motivation to prevent goal drift or competing systems, and the ability to implement such motivations to enforce long-term stability. Call this null case Type 0 conflict.

Go a bit lower on coordination, and you might encounter a universe with several different systems of comparable ability, which agree about basics like existential risk but disagree on other goals. For example, you could have AIs A, B, and C, where each of them thinks the universe should be blue, red, or green. The simplest scenario is one where the conflict between them is static: each AI gets roughly one third of the universe, and this stays fixed over time, with all AIs having strong reason to expect it to remain fixed. This might be made to obey Stein’s Principle, but it is more of a risky bet. One would need strong reason for believing that any AI getting a “bigger share” was impossible. If, for example, one AI could hack the others, in a manner similar to modern-day computer or social hacking, this would allow for “victory” and introduce instability. Solving this in the general case might be an NP-complete problem; if the computers have some sensory input, you must be able to know that no point in an exponentially large input space will cause serious failure. But, a solution might happen. Call this Type 1 conflict.

Going down more on coordination, one finds scenarios where there are still a fixed number of agents, but their relative positions change over time; call this Type 2 conflict. By Stein’s Principle, the only way to make this work is through negative feedback loops: if there is any case where winning a bit causes you to win more, this will spiral on itself, until one agent ceases to exist. And (handwave) maintaining universal negative feedback seems quite hard. In the human world, advantage in conflict is a combination of many different factors; you would have to get negative feedback on all of them, or else they would collapse and stop oscillating.

If we dare to go down even farther, to worlds with stable long-term conflict in which it is still possible to “win”, one must also allow for the emergence of new players, or else the number of players will monotonically decrease to one (or zero, in an x-risk scenario). And all players should have a basic drive to prevent the creation of new players with differing goals. For this scenario (Type 3 conflict) to work, Stein’s Principle requires that the existing players be unable to prevent the creation of new players (at comparable levels of ability), and simultaneously be able to ensure with extremely high accuracy that all new players pose no threat of existential risk. This seems, a priori, extremely implausible.

And of course, we have Type 4 conflict, the sort present among humanity today, which is obviously not long-term stable. The strange thing is that almost nobody seems to realize it.

Polarity as Flawed Categorization

“Indeed, the more choices you have, the worse off you are. The worst situation of all would be somebody coming up to you and offering you a choice between two identical packages of M&Ms. Since choosing one package (which you value at $.75) means giving up the other package (which you also value at $.75), your economic profit is exactly zero! So being offered a choice between two identical packages of M&Ms is in fact equivalent to being offered nothing.

Now, a lay person might be forgiven for thinking that being offered a choice between two identical packages of M&Ms is in fact equivalent to being offered a single package of M&Ms. But economists know better.” – Improbable Research

In his excellent book Superintelligence, Nick Bostrom divides the future into two groups of scenarios. In one set, the “singleton scenarios”, one agent has overwhelming power over all others. In the other, “multipolar scenarios”, there are many different agents at the same level of ability, with no one in charge overall.

This dichotomy is simple, but it may be flawed. Consider, on one extreme, a very old universe where human civilization has spread beyond Earth’s cosmological horizon. Even in a singleton scenario, large portions of the Universe now can’t communicate with Earth. The AI controlling those portions and the AI controlling the Earth may be identical, but they are causally distinct agents. Is this a “multipolar scenario”? I think not. It’s a choice between a bag of M&Ms, and an identical bag of M&Ms.

On the other extreme, one can imagine a multipolar scenario with a huge variety of agents, each of which may stay the same, change, or be replaced by an entirely different agent. However, this scenario violates Stein’s Principle. At a minimum, to remain stable, each multipolar agent must have a set of common instrumental goals, derived from the common terminal goal of avoiding x-risk. Moreover, Stein’s Principle will likely ensure other similarities. For example, each agent will desire to keep its utility function stable, as the ones that don’t will rapidly get replaced.

Hence, rather than two distinct and widely-separated categories, we have a spectrum of possible futures. On the one end, agents at the top level are identical; on the other, they have just enough in common to ensure stability. Using Bostrom’s terminology, one can visualize these as being at different points along the “coordination axis”.

1950s America is a Special Case

“Advances invented either solely or partly by government institutions include, as mentioned before, the computer, mouse, Internet, digital camera, and email. Not to mention radar, the jet engine, satellites, fiber optics, artificial limbs, and nuclear energy. (…) Even those inventions that come from corporations often come not from startups exposed to the free market, but from de facto state-owned monopolies. For example, during its fifty years as a state-sanctioned monopoly, the infamous Ma Bell invented (via its Bell Labs division) transistors, modern cryptography, solar cells, the laser, the C programming language, and mobile phones…” – “Competence of Government

I think it’s worth paying attention to the fact that, of this apparently arbitrary list of inventions, none of them came from the current US political system (I’ll abbreviate CUSPS). Every one of them was developed many decades ago. And more specifically, every one of them was developed between about 1930 and 1975. Electricity, automobiles, telephones, telegraphs, airplanes, movies, radios, and other much older inventions aren’t included either. Rather than general examples of innovative government, across different cultures and time periods, these are all from one specific political system (the immediate predecessor to CUSPS).

If we were merely using this time period as an example, to show government could innovate, one data point suffices. If we were economic historians, dispassionately debating how large the space of possible civilizations was, we could stop there. However, in what Scott calls the motte-and-bailey defense, that is never how this argument is used in practice. For one example, if you Google (eg.) “government” “arpanet”, of the first ten results every one is in the context of a policy debate about what CUSPS should do and what our attitude should be towards it. ARPANET and things in its category are invariably used, de facto, as justifications for CUSPS, despite not having been created by CUSPS.

Scott’s model of how the world operates here is (to quote) “de novo invention seems to come mostly from very large organizations that can afford basic research”; this is a much stronger claim than that the evidence shows “examples exist of large organizations which did well at de novo invention”, or (a quote from the introduction to the document) “we [can’t] be absolutely certain free market always works better than government before we even investigate the issue”. Given more detailed historical data, I would suggest an alternative model.

In the US, there had always been a great deal of innovation, before the federal government was funding it en masse, and even before the federal government had much power at all. Railroads and steamships and telegraphs came from a time when Washington D.C. could not prevent half the states from raising their own armies and fighting a bloody civil war, much the same as the government in 2014 Iraq.

Later, in the 1930s, the immensely more powerful federal government created policies (taxation, fixed regulatory costs, the SEC, etc.) that strongly favored large organizations over smaller ones. The pre-existing base of inventive individuals, like everyone else, then simply got sucked into large institutions for lack of anywhere else to go. This neatly explains the entire historical trajectory. There were smart guys who invented stuff; the government then hired most of them, so most inventions started coming out with a government logo stamped on them; and when CUSPS was created, its incompetence started driving the smart guys away, again increasing private innovation at the expense of public.


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