Book review: The Rationality Quotient: Toward a Test of Rational Thinking, by Keith E. Stanovich, Richard F. West and Maggie E. Toplak.

This book describes an important approach to measuring individual rationality: an RQ test that loosely resembles an IQ test. But it pays inadequate attention to the most important problems with tests of rationality.

Coachability

My biggest concern about rationality testing is what happens when people anticipate the test and are motivated to maximize their scores (as is the case with IQ tests). Do they:

  • learn to score high by “cheating” (i.e. learn what answers the test wants, without learning to apply that knowledge outside of the test)?
  • learn to score high by becoming more rational?
  • not change their score much, because they’re already motivated to do as well as their aptitudes allow (as is mostly the case with IQ tests)?

Alas, the book treats these issues as an afterthought. Their test knowingly uses questions for which cheating would be straightforward, such as asking whether the test subject believes in science, and whether they prefer to get $85 now rather than $100 in three months. (If they could use real money, that would drastically reduce my concerns about cheating. I’m almost tempted to advocate doing that, but doing so would hinder widespread adoption of the test, even if using real money added enough value to pay for itself.)

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Two and a half years ago, Eliezer was (somewhat plausibly) complaining that virtually nobody outside of MIRI was working on AI-related existential risks.

This year (at EAGlobal) one of MIRI’s talks was a bit hard to distinguish from an AI safety talk given by someone with pretty mainstream AI affiliations.

What happened in that time to cause that shift?

A large change was catalyzed by the publication of Superintelligence. I’ve been mildly disappointed about how little it affected discussions among people who were already interested in the topic. But Superintelligence caused a large change in how many people are willing to express concern over AI risks. That’s presumably because Superintelligence looks sufficiently academic and neutral to make many people comfortable about citing it, whereas similar arguments by Eliezer/MIRI didn’t look sufficiently prestigious within academia.

A smaller part of the change was MIRI shifting its focus somewhat to be more in line with how mainstream machine learning (ML) researchers expect AI to reach human levels.

Also, OpenAI has been quietly shifting in a more MIRI-like direction (I’m very unclear on how big a change this is). (Paul Christiano seems to deserve some credit for both the MIRI and OpenAI shifts in strategies.)

Given those changes, it seems like MIRI ought to be able to attract more donations than before. Especially since it has demonstrated evidence of increasing competence, and also because HPMoR seemed to draw significantly more people into the community of people who are interested in MIRI.

MIRI has gotten one big grant from OpenPhilanthropy that it probably couldn’t have gotten when mainstream AI researchers were treating MIRI’s concerns as too far-fetched to be worth commenting on. But donations from MIRI’s usual sources have stagnated.

That pattern suggests that MIRI was previously benefiting from a polarization effect, where the perception of two distinct “tribes” (those who care about AI risks versus those who promote AI) energized people to care about “their tribe”.

Whereas now there’s no clear dividing line between MIRI and mainstream researchers. Also, there’s lots of money going into other organizations that plan to do something about AI safety. (Most of those haven’t yet articulated enough of a strategy to make me optimistic that that money is well spent. I still endorse the ideas I mentioned last year in How much Diversity of AGI-Risk Organizations is Optimal?. I’m unclear on how much diversity of approaches we’re getting from the recent proliferation of AI safety organizations.)

That kind of pattern of donations creates perverse incentives to charities to at least market themselves as fighting a powerful group of people, rather than (as the ideal charity should be) addressing a neglected problem. Even if that marketing doesn’t distort a charity’s operations, the charity will be tempted to use counterproductive alarmism. AI risk organizations have resisted those temptations (at least recently), but it seems risky to tempt them.

That’s part of why I recently made a modest donation to MIRI, in spite of the uncertainty over the value of their efforts (I had last donated to them in 2009).

[Caveat: this post involves abstract theorizing whose relevance to practical advice is unclear. ]

What we call willpower mostly derives from conflicts between parts of our minds, often over what discount rate to use.

An additional source of willpower-like conflicts comes from social desirability biases.

I model the mind as having many mental sub-agents, each focused on a fairly narrow goal. Different goals produce different preferences for caring about the distant future versus caring only about the near future.

The sub-agents typically are as smart and sophisticated as a three year old (probably with lots of variation). E.g. my hunger-minimizing sub-agent is willing to accept calorie restriction days with few complaints now that I have a reliable pattern of respecting the hunger-minimizing sub-agent the next day, but complained impatiently when calorie restriction days seemed abnormal.

We have beliefs about how safe we are from near-term dangers, often reflected in changes to the autonomic nervous system (causing relaxation or the fight or flight reflex). Those changes cause quick, crude shifts in something resembling a global discount rate. In addition, each sub-agent has some ability to demand that it’s goals be treated fairly.

We neglect sub-agents whose goals are most long-term when many sub-agents say their goals have been neglected, and/or when the autonomic nervous system says immediate problems deserve attention.

Our willpower is high when we feel safe and are satisfied with our progress at short-term goals.

Social status

The time-discounting effects are sometimes obscured by social signaling.

Writing a will hints at health problems, whereas doing something about global warming can signal wealth. We have sub-agents that steer us to signal health and wealth, but without doing so in a deliberate enough way that people see that we are signaling. That leads us to exaggerate how much of our failure to write a will is due to the time-discounting type of low willpower.

Video games convince parts of our minds that we’re gaining status (in a virtual society) and/or training to win status-related games in real life. That satisfies some sub-agents who care about status. (Video games deceive us about status effects, but that has limited relevance to this post.) Yet as with most play, we suppress awareness of the zero-sum competitions we’re aiming to win. So we get confused about whether we’re being short-sighted here, because we’re pursuing somewhat long-term benefits, probably deceiving ourselves somewhat about them, and pretending not to care about them.

Time asymmetry?

Why do we feel an asymmetry in effects of neglecting distant goals versus neglecting immediate goals?

The fairness to sub-agents metaphor suggests that neglecting the distant future ought to produce emotional reactions comparable to what happens when we neglect the near future.

Neglecting the distant future does produce some discomfort that somewhat resembles willpower problems. If I spend lots of time watching TV, I end up feeling declining life-satisfaction, which tends to eventually cause me to pay more attention to long-term goals.

But the relevant emotions still don’t seem symmetrical.

One reason for asymmetry is that different goals imply different things for what constitutes neglecting a goal: neglecting sleep or food for a day implies something more unfair to the relevant sub-agents than does neglecting one’s career skills.

Another reason is that for both time-preference and social desirability conflicts, we have instincts that aren’t optimized for our current environment.

Our hunter-gatherer ancestors needed to devote most of their time to tasks that paid off within days, and didn’t know how to devote more than a few percent of their time to usefully preparing for events that were several years in the future. Our farmer ancestors needed to devote more time to 3-12 month planning horizons, but not much more than hunter-gatherers did. Today many of us can productively spend large fractions of our time on tasks (such as getting a college degree) that take more than 5 years to pay off. Social desirability biases show (less clear) versions of that same pattern.

That means we need to override our system 1 level heuristics with system 2 level analysis. That requires overriding the instinctive beliefs of some sub-agents about how much attention their goals deserve. Whereas the long-term goals we override to deal with hunger have less firmly established “rights” to fairness.

Also, there may be some fairness rules about how often system 2 can override system 1 agents – doing that too often may cause coalitions within system 1 to treat system 2 as a politician who has grabbed too much power. [Does this explain decision fatigue? I’m unsure.]

Other Models of Willpower

The depletion model

Willpower depletion captures a nontrivial effect of key sub-agents rebelling when their goals have been overlooked for too long.

But I’m confused – the depletion model doesn’t seem like it’s trying to be a complete model of willpower. In particular, it either isn’t trying explain evolutionary sources of willpower problems, or is trying to explain it via the clearly inadequate claim that willpower is a simple function of current blood glucose levels.

It would be fine if the depletion model were just a heuristic that helped us develop more willpower. But if anything it seems more likely to reduce willpower.

Kurzban’s opportunity costs model

Kurzban et al. have a model involving the opportunity costs of using cognitive resources for a given task.

It seems more realistic than most models I’ve seen. It describes some important mental phenomena more clearly than I can, but doesn’t quite seem to be about willpower. In particular, it seems uninformative about differing time horizons. Also, it focuses on cognitive resource constraints, whereas I’d expect some non-cognitive resource constraints to be equally important.

Ainslie’s Breakdown of Will

George Ainslie wrote a lot about willpower, describing it as intertemporal bargaining, with hyperbolic discounting. I read that book 6 years ago, but don’t remember it very clearly, and I don’t recall how much it influenced my current beliefs. I think my model looks a good deal like what I’d get if I had set out to combine the best parts of Ainslie’s ideas and Kurzban’s ideas, but I wrote 90% of this post before remembering that Ainslie’s book was relevant.

Ainslie apparently wrote his book before it became popular to generate simple models of willpower, so he didn’t put much thought into comparing his views to others.

Hyperbolic discounting seems to be a real phenomenon that would be sufficient to cause willpower-like conflicts. But I’m unclear on why it should be a prominent part of a willpower model.

Distractible

This “model” isn’t designed to say much beyond pointing out that willpower doesn’t reliably get depleted.

Hot/cool

A Hot/cool-system model sounds like an attempt to generalize the effects of the autonomic nervous system to explain all of willpower. I haven’t found it to be very informative.

Muscle

Some say that willpower works like a muscle, in that using it strengthens it.

My model implies that we should expect this result when preparing for the longer-term future causes our future self to be safer and/or to more easily satisfy near-term goals.

I expect this effect to be somewhat observable with using willpower to save money, because having more money makes us feel safer and better able to satisfy our goals.

I expect this effect to be mostly absent after using willpower to loose weight or to write a will, since those produce benefits which are less intuitive and less observable.

Why do drugs affect willpower?

Scott at SlateStarCodex asks why drugs have important effects on willpower.

Many drugs affect the autonomic nervous system, thereby influencing our time preferences. I’d certainly expect that drugs which reduce anxiety will enable us to give higher priority to far future goals.

I expect stimulants make us feel less concern about depleting our available calories, and less concern about our need for sleep, thereby satisfying a few short-term sub-agents. I expect this to cause small increases in willpower.

But this is probably incomplete. I suspect the effect of SSRIs on willpower varies quite widely between people. I suspect that’s due to an anti-anxiety effect which increases willpower, plus an anti-obsession effect which reduces willpower in a way that my model doesn’t explain.

And Scott implies that some drugs have larger effects on willpower than I can explain.

My model implies that placebos can be mildly effective at increasing willpower, by convincing some short-sighted sub-agents that resources are being applied toward their goals. A quick search suggests this prediction has been poorly studied so far, with one low-quality study confirming this.

Conclusion

I’m more puzzled than usual about whether these ideas are valuable. Is this model profound, or too obvious to matter?

I presume part of the answer is that people who care about improving willpower care less about theory, and focus on creating heuristics that are easy to apply.

CFAR does a decent job of helping people develop more willpower, not by explaining a clear theory of what willpower is, but by focusing more on how to resolve conflicts between sub-agents.

And I recommend that most people start with practical advice, such as the advice in The Willpower Instinct, and worry about theory later.

Book review: Doing Good Better, by William MacAskill.

This book is a simple introduction to the Effective Altruism movement.

It documents big differences between superficially plausible charities, and points out how this implies big benefits to the recipients of charity from donors paying more attention to the results that a charity produces.

How effective is the book?

Is it persuasive?

Probably yes, for a small but somewhat important fraction of the population who seriously intend to help distant strangers, but have procrastinated about informing themselves about how to do so.

Does it focus on a neglected task?

Not very neglected. It’s mildly different from similar efforts such as GiveWell’s website and Reinventing Philanthropy, in ways that will slightly reduce the effort needed to understand the basics of Effective Altruism.

Will it make people more altruistic?

Not very much. It mostly seems to assume that people have some fixed level of altruism, and focuses on improving the benefits that result from that altruism. Maybe it will modestly redirect peer pressure toward making people more altruistic.

Will it make readers more effective?

Probably. For people who haven’t given much thought to these topics, the book’s advice is a clear improvement over standard habits. It will be modestly effective at promoting a culture where charitable donations that save lives are valued more highly than donations which accomplish less.

But I see some risk that it will make people overconfident about the benefits of the book’s specific strategies. An ideal version of the book would instead inspire people to improve on the book’s analysis.

The book provides evidence that donors rarely pay attention to how much good a charity does. Yet it avoids asking why. If you pay attention, you’ll see hints that donors are motivated mainly by the desire to signal something virtuous about themselves (for example, see the book’s section on moral licensing). In spite of that, the book consistently talks as if donors have good intentions, and only need more knowledge to be better altruists.

The book is less rigorous than I had hoped. I’m unsure how much of that is due to reasonable attempts to simplify the message so that more people can understand it with minimal effort.

In a section on robustness of evidence, the book describes this “sanity check”:

“if it cost ten dollars to save a life, then we’d have to suppose that they or their family members couldn’t save up for a few weeks, or take out a loan, in order to pay for the lifesaving product.”

I find it confusing to use this as a sanity check, because it’s all too easy to imagine that many people are in desperate enough conditions that they’re spending their last dollar to avoid starvation.

The book alternates between advocating doing more good (satisficing), and advocating the most possible good (optimizing). In practice, it mostly focuses on safe ways to produce fairly good results.

The book barely mentions existential risks. If it were literally trying to advocate doing the most good possible, it would devote a lot more attention to affecting the distant future. But that’s much harder to do well than what the book does focus on (saving a few more lives in Africa over the next few years), and would involve acts of charity that have small probabilities of really large effects on people who are not yet born.

If you’re willing to spend 50-100 hours (but not more) learning how to be more effective with your altruism, then reading this book is a good start.

But people who are more ambitious ought to be able to make a bigger difference to the world. I encourage those people to skip this book, and focus more on analyzing existential risks.

The stock market reaction to the election was quite strange.

From the first debate through Tuesday, S&P 500 futures showed modest signs of believing that Trump was worse for the market than Clinton. This Wolfers and Zitzewitz study shows some of the relevant evidence.

On Tuesday evening, I followed the futures market and the prediction markets moderately closely, and it looked like there was a very clear correlation between those two markets, strongly suggesting the S&P 500 would be 6 to 8 percent lower under Trump than under Clinton. This correlation did not surprise me.

This morning, the S&P 500 prices said the market had been just kidding last night, and that Trump is neutral or slightly good for the market.

Part of this discrepancy is presumably due to the difference between regular trading hours and after hours trading. The clearest evidence for market dislike of Trump came from after hours trading, when the most sophisticated traders are off-duty. I’ve been vaguely aware that after hours markets are less efficiently priced. But this appears to involve at least a few hundred million dollars of potential profit, which somewhat stretches the limit of how inefficient the markets could plausibly be.

I see one report of Carl Icahn claiming

I thought it was absurd that the market, the S&P was down 100 points on Trump getting elected … but I couldn’t put more than about a billion dollars to work

I’m unclear what constrained him, but it sure looked like the market could have absorbed plenty more buying while I was watching (up to 10pm PST), so I’ll guess he was more constrained by something related to him being at a party.

But even if the best U.S. traders were too distracted to make the markets efficient, that leaves me puzzled about asian markets, which were down almost as much as the U.S. market during the middle of the asian day.

So it’s hard to avoid the conclusion that the market either made a big irrational move, or was reacting to news whose importance I can’t recognize.

I don’t have a strong opinion on which of the market reactions was correct. My intuition says that a market decline of anywhere from 1% to 5% would have been sensible, and I’ve made a few trades reflecting that opinion. I expect that market reactions to news tend to get more rational over time, so I’m now giving a fair amount of weight to the possibility that Trump won’t affect stocks much.

I’ve substantially reduced my anxiety over the past 5-10 years.

Many of the important steps along that path look easy in hindsight, yet the overall goal looked sufficiently hard prospectively that I usually assumed it wasn’t possible. I only ended up making progress by focusing on related goals.

In this post, I’ll mainly focus on problems related to general social anxiety among introverted nerds. It will probably be much less useful to others.

In particular, I expect it doesn’t apply very well to ADHD-related problems, and I have little idea how well it applies to the results of specific PTSD-type trauma.

It should be slightly useful for anxiety over politicians who are making America grate again. But you’re probably fooling yourself if you blame many of your problems on distant strangers.

Trump: Make America Grate Again!

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Book review: The Vital Question: Energy, Evolution, and the Origins of Complex Life, by Nick Lane.

This book describes a partial theory of how life initially evolved, followed by a more detailed theory of how eukaryotes evolved.

Lane claims the hardest step in evolving complex life was the development of complex eukaryotic cells. Many traits such as eyes and wings evolved multiple times. Yet eukaryotes have many traits which evolved exactly once (including mitochondria, sex, and nuclear membranes).

Eukaryotes apparently originated in a single act of an archaeon engulfing a bacterium. The result wasn’t very stable, and needed to quickly evolve (i.e. probably within a few million years) a sophisticated nucleus, plus sexual reproduction.

Only organisms that go through these steps will be able to evolve a more complex genome than bacteria do. This suggests that complex life is rare outside of earth, although simple life may be common.

The book talks a lot about mitochondrial DNA, and make some related claims about aging.

Cells have a threshold for apoptosis which responds to the effects of poor mitochondrial DNA, killing weak embryos before they can take up much parental resources. Lane sees evolution making important tradeoffs, with species that have intense energy demands (such as most birds) setting their thresholds high, and more ordinary species (e.g. rats) setting the threshold lower. This tradeoff causes less age-related damage in birds, at the cost of lower fertility.

Lane claims that the DNA needs to be close to the mitochondria in order to make quick decisions. I found this confusing until I checked Wikipedia and figured out it probably refers to the CoRR hypothesis. I’m still confused, but at least now I can attribute the confusion to the topic being hard. Aubrey de Grey’s criticism of CoRR suggests there’s a consensus that CoRR has problems, and the main confusion revolves around the credibility of competing hypotheses.

Lane is quite pessimistic about attempts to cure aging. Only a small part of that disagreement with Aubrey can be explained by the modest differences in their scientific hypotheses. Much of the difference seems to come from Lane’s focus on doing science, versus Aubrey’s focus on engineering. Lane keeps pointing out (correctly) that cells are really complex and finely tuned. Yet Lane is well aware that evolution makes many changes that affect aging in spite of the complexity. I suspect he’s too focused on the inadequacy of typical bioengineering to imagine really good engineering.

Some less relevant tidbits include:

  • why vibrant plumage in male birds may be due to females being heterogametic
  • why male mammals age faster than females

Many of Lane’s ideas are controversial, and only weakly supported by the evidence. But given the difficulty of getting good evidence on these topics, that still represents progress.

The book is pretty dense, and requires some knowledge of biochemistry. It has many ideas and evidence that were developed since I last looked into this subject. I expect to forget many of those ideas fairly quickly. The book is worth reading if you have enough free time, but understanding these topics does not feel vital.

Book review: Notes on a New Philosophy of Empirical Science (Draft Version), by Daniel Burfoot.

Standard views of science focus on comparing theories by finding examples where they make differing predictions, and rejecting the theory that made worse predictions.

Burfoot describes a better view of science, called the Compression Rate Method (CRM), which replaces the “make prediction” step with “make a compression program”, and compares theories by how much they compress a standard (large) database.

These views of science produce mostly equivalent results(!), but CRM provides a better perspective.

Machine Learning (ML) is potentially science, and this book focuses on how ML will be improved by viewing its problems through the lens of CRM. Burfoot complains about the toolkit mentality of traditional ML research, arguing that the CRM approach will turn ML into an empirical science.

This should generate a Kuhnian paradigm shift in ML, with more objective measures of the research quality than any branch of science has achieved so far.

Burfoot focuses on compression as encoding empirical knowledge of specific databases / domains. He rejects the standard goal of a general-purpose compression tool. Instead, he proposes creating compression algorithms that are specialized for each type of database, to reflect what we know about topics (such as images of cars) that are important to us.
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MIRI has produced a potentially important result (called Garrabrant induction) for dealing with uncertainty about logical facts.

The paper is somewhat hard for non-mathematicians to read. This video provides an easier overview, and more context.

It uses prediction markets! “It’s a financial solution to the computer science problem of metamathematics”.

It shows that we can evade disturbing conclusions such as Godel incompleteness and the paradox of the liar, by expecting to only be very confident about logically deducible facts (as opposed to being mathematically certain). That’s similar to the difference between treating beliefs about empirical facts as probabilities, as opposed to boolean values.

I’m somewhat skeptical that it will have an important effect on AI safety, but my intuition says it will produce enough benefits somewhere that it will become at least as famous as Pearl’s work on causality.

Book review: The Moral Economy: Why Good Incentives Are No Substitute for Good Citizens, by Samuel Bowles.

This book has a strange mixture of realism and idealism.

It focuses on two competing models: the standard economics model in which people act in purely self-interested ways, and a more complex model in which people are influenced by context to act either altruistically or selfishly.

The stereotypical example comes from the semi-famous Haifa daycare experiment, where daycare centers started fining parents for being late to pick up children, and the parents responded by being later.

The first half of the book is a somewhat tedious description of ideas that seem almost obvious enough to be classified as common sense. He points out that the economist’s model is a simplification that is useful for some purposes, yet it’s not too hard to find cases where it makes the wrong prediction about how people will respond to incentives.

That happens because society provides weak pressures that produce cooperation under some conditions, and because financial incentives send messages that influence whether people want to cooperate. I.e. the parents appear to have previously felt obligated to be somewhat punctual, but then inferred from the fines that it was ok to be late as long as they paid the price.[*].

The book advocates more realism on this specific issue. But it’s pretty jarring to compare that to the idealistic view the author takes on similar topics, such as acquiring evidence of how people react, or modeling politicians. He treats the Legislator (capitalized like that) as a very objective, well informed, and altruistic philosopher. That model may sometimes be useful, but I’ll bet that, on average, it produces worse predictions about legislators’ behavior than does the economist’s model of a self-interested legislator.

The book becomes more interesting around chapter V, when it analyzes the somewhat paradoxical conclusion that markets sometimes make people more selfish, yet cultures that have more experience with markets tend to cooperate more.

He isn’t able to fully explain that, but he makes some interesting progress. One factor that’s important to focus on is the difference between complete and incomplete contracts. Complete contracts describe everything a buyer might need to know about a product or service. An example of an incomplete contract would be an agreement to hire a lawyer to defend me – I don’t expect the lawyer to specify how good a defense to expect.

Complete contracts enable people to trade without needing to trust the seller, which can lead to highly selfish attitudes. Incomplete contracts lead to the creation of trust between participants, because having frequent transactions depends on some implicit cooperation.

The book ends by promoting the “new” idea that policy ought to aim for making people be good. But it’s unclear who disagrees with that idea. Economists sometimes sound like they disagree, because they often say that policy shouldn’t impose one group’s preferences on another group. But economists are quite willing to observe that people generally prefer cooperation over conflict, and that most people prefer institutions that facilitate cooperation. That’s what the book mostly urges.

The book occasionally hints at wanting governments to legislate preferences in ways that go beyond facilitating cooperation, but doesn’t have much of an argument for doing so.

[*] – The book implies that the increased lateness was an obviously bad result. This seems like a plausible guess. But I find it easy to imagine conditions where the reported results were good (i.e. the parents might benefit from being late more than it costs the teachers to accommodate them).

However, that scenario depends on the fines being high enough for the teachers to prefer the money over punctuality. They appear not to have been consulted, so success at that would have depended on luck. It’s unclear whether the teachers were getting overtime pay when parents were late, or whether the fines benefited only the daycare owner.