bias

All posts tagged bias

The point of this blog post feels almost too obvious to be worth saying, yet I doubt that it’s widely followed.

People often avoid doing projects that have a low probability of success, even when the expected value is high. To counter this bias, I recommend that you mentally combine many such projects into a strategy of trying new things, and evaluate the strategy’s probability of success.

1.

Eliezer says in On Doing the Improbable:

I’ve noticed that, by my standards and on an Eliezeromorphic metric, most people seem to require catastrophically high levels of faith in what they’re doing in order to stick to it. By this I mean that they would not have stuck to writing the Sequences or HPMOR or working on AGI alignment past the first few months of real difficulty, without assigning odds in the vicinity of 10x what I started out assigning that the project would work. … But you can’t get numbers in the range of what I estimate to be something like 70% as the required threshold before people will carry on through bad times. “It might not work” is enough to force them to make a great effort to continue past that 30% failure probability. It’s not good decision theory but it seems to be how people actually work on group projects where they are not personally madly driven to accomplish the thing.

I expect this reluctance to work on projects with a large chance of failure is a widespread problem for individual self-improvement experiments.

2.

One piece of advice I got from my CFAR workshop was to try lots of things. Their reasoning involved the expectation that we’d repeat the things that worked, and forget the things that didn’t work.

I’ve been hesitant to apply this advice to things that feel unlikely to work, and I expect other people have similar reluctance.

The relevant kind of “things” are experiments that cost maybe 10 to 100 hours to try, which don’t risk much other than wasting time, and for which I should expect on the order of a 10% chance of noticeable long-term benefits.

Here are some examples of the kind of experiments I have in mind:

  • gratitude journal
  • morning pages
  • meditation
  • vitamin D supplements
  • folate supplements
  • a low carb diet
  • the Plant Paradox diet
  • an anti-anxiety drug
  • ashwaghanda
  • whole fruit coffee extract
  • piracetam
  • phenibut
  • modafinil
  • a circling workshop
  • Auditory Integration Training
  • various self-help books
  • yoga
  • sensory deprivation chamber

I’ve cheated slightly, by being more likely to add something to this list if it worked for me than if it was a failure that I’d rather forget. So my success rate with these was around 50%.

The simple practice of forgetting about the failures and mostly repeating the successes is almost enough to cause the net value of these experiments to be positive. More importantly, I kept the costs of these experiments low, so the benefits of the top few outweighed the costs of the failures by a large factor.

3.

I face a similar situation when I’m investing.

The probability that I’ll make any profit on a given investment is close to 50%, and the probability of beating the market on a given investment is lower. I don’t calculate actual numbers for that, because doing so would be more likely to bias me than to help me.

I would find it rather discouraging to evaluate each investment separately. Doing so would focus my attention on the fact that any individual result is indistinguishable from luck.

Instead, I focus my evaluations much more on bundles of hundreds of trades, often associated with a particular strategy. Aggregating evidence in that manner smooths out the good and bad luck to make my skill (or lack thereof) more conspicuous. I’m focusing in this post not on the logical interpretation of evidence, but on how the subconscious parts of my mind react. This mental bundling of tasks is particularly important for my subconscious impressions of whether I’m being productive.

I believe this is a well-known insight (possibly from poker?), but I can’t figure out where I’ve seen it described.

I’ve partly applied this approach to self-improvement tasks (not quite as explicitly as I ought to), and it has probably helped.

Book review: Time Biases: A Theory of Rational Planning and Personal Persistence, by Meghan Sullivan.

I was very unsure about whether this book would be worth reading, as it could easily have been focused on complaints about behavior that experts have long known are mistaken.

I was pleasantly surprised when it quickly got to some of the really hard questions, and was thoughtful about what questions deserved attention. I disagree with enough of Sullivan’s premises that I have significant disagreements with her conclusions. Yet her reasoning is usually good enough that I’m unsure what to make of our disagreements – they’re typically due to differences of intuition that she admits are controversial.

I had hoped for some discussion of ethics (e.g. what discount rate to use in evaluating climate change), whereas the book focuses purely on prudential rationality (i.e. what’s rational for a self-interested person). Still, the discussion of prudential rationality covers most of the issues that make the ethical choices hard.

Personal identity

A key issue is the nature of personal identity – does one’s identity change over time?

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No, this isn’t about cutlery.

I’m proposing to fork science in the sense that Bitcoin was forked, into an adversarial science and a crowdsourced science.

As with Bitcoin, I have no expectation that the two branches will be equal.

These ideas could apply to most fields of science, but some fields need change more than others. P-values and p-hacking controversy are signs that a field needs change. Fields that don’t care much about p-values don’t need as much change, e.g. physics and computer science. I’ll focus mainly on medicine and psychology, and leave aside the harder-to-improve social sciences.

What do we mean by the word Science?

The term “science” has a range of meanings.

One extreme focuses on “perform experiments in order to test hypotheses”, as in The Scientist In The Crib. I’ll call this the personal knowledge version of science.

A different extreme includes formal institutions such as peer review, RCTs, etc. I’ll call this the authoritative knowledge version of science.

Both of these meanings of the word science are floating around, with little effort to distinguish them [1]. I suspect that promotes confusion about what standards to apply to scientific claims. And I’m concerned that people will use the high status of authoritative science to encourage us to ignore knowledge that doesn’t fit within its paradigm.

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Book review: Inadequate Equilibria, by Eliezer Yudkowsky.

This book (actually halfway between a book and a series of blog posts) attacks the goal of epistemic modesty, which I’ll loosely summarize as reluctance to believe that one knows better than the average person.

1.

The book starts by focusing on the base rate for high-status institutions having harmful incentive structures, charting a middle ground between the excessive respect for those institutions that we see in mainstream sources, and the cynicism of most outsiders.

There’s a weak sense in which this is arrogant, namely that if were obvious to the average voter how to improve on these problems, then I’d expect the problems to be fixed. So people who claim to detect such problems ought to have decent evidence that they’re above average in the relevant skills. There are plenty of people who can rationally decide that applies to them. (Eliezer doubts that advising the rest to be modest will help; I suspect there are useful approaches to instilling modesty in people who should be more modest, but it’s not easy). Also, below-average people rarely seem to be attracted to Eliezer’s writings.

Later parts of the book focus on more personal choices, such as choosing a career.

Some parts of the book seem designed to show off Eliezer’s lack of need for modesty – sometimes successfully, sometimes leaving me suspecting he should be more modest (usually in ways that are somewhat orthogonal to his main points; i.e. his complaints about “reference class tennis” suggest overconfidence in his understanding of his debate opponents).

2.

Eliezer goes a bit overboard in attacking the outside view. He starts with legitimate complaints about people misusing it to justify rejecting theory and adopt “blind empiricism” (a mistake that I’ve occasionally made). But he partly rejects the advice that Tetlock gives in Superforecasting. I’m pretty sure Tetlock knows more about this domain than Eliezer does.

E.g. Eliezer says “But in novel situations where causal mechanisms differ, the outside view fails—there may not be relevantly similar cases, or it may be ambiguous which similar-looking cases are the right ones to look at.”, but Tetlock says ‘Nothing is 100% “unique” … So superforecasters conduct creative searches for comparison classes even for seemingly unique events’.

Compare Eliezer’s “But in many contexts, the outside view simply can’t compete with a good theory” with Tetlock’s commandment number 3 (“Strike the right balance between inside and outside views”). Eliezer seems to treat the approaches as antagonistic, whereas Tetlock advises us to find a synthesis in which the approaches cooperate.

3.

Eliezer provides a decent outline of what causes excess modesty. He classifies the two main failure modes as anxious underconfidence, and status regulation. Anxious underconfidence definitely sounds like something I’ve felt somewhat often, and status regulation seems pretty plausible, but harder for me to detect.

Eliezer presents a clear model of why status regulation exists, but his explanation for anxious underconfidence doesn’t seem complete. Here are some of my ideas about possible causes of anxious underconfidence:

  • People evaluate mistaken career choices and social rejection as if they meant death (which was roughly true until quite recently), so extreme risk aversion made sense;
  • Inaction (or choosing the default action) minimizes blame. If I carefully consider an option, my choice says more about my future actions than if I neglect to think about the option;
  • People often evaluate their success at life by counting the number of correct and incorrect decisions, rather than adding up the value produced;
  • People who don’t grok the Bayesian meaning of the word “evidence” are likely to privilege the scientific and legal meanings of evidence. So beliefs based on more subjective evidence get treated as second class citizens.

I suspect that most harm from excess modesty (and also arrogance) happens in evolutionarily novel contexts. Decisions such as creating a business plan for a startup, or writing a novel that sells a million copies, are sufficiently different from what we evolved to do that we should expect over/underconfidence to cause more harm.

4.

Another way to summarize the book would be: don’t aim to overcompensate for overconfidence; instead, aim to eliminate the causes of overconfidence.

This book will be moderately popular among Eliezer’s fans, but it seems unlikely to greatly expand his influence.

It didn’t convince me that epistemic modesty is generally harmful, but it does provide clues to identifying significant domains in which epistemic modesty causes important harm.

The paper When Will AI Exceed Human Performance? Evidence from AI Experts reports ML researchers expect AI will create a 5% chance of “Extremely bad (e.g. human extinction)” consequences, yet they’re quite divided over whether that implies it’s an important problem to work on.

Slate Star Codex expresses confusion about and/or disapproval of (a slightly different manifestation of) this apparent paradox. It’s a pretty clear sign that something is suboptimal.

Here are some conjectures (not designed to be at all mutually exclusive).
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A new paper titled When Will AI Exceed Human Performance? Evidence from AI Experts reports some bizarre results. From the abstract:

Researchers believe there is a 50% chance of AI outperforming humans in all tasks in 45 years and of automating all human jobs in 120 years, with Asian respondents expecting these dates much sooner than North Americans.

So we should expect a 75 year period in which machines can perform all tasks better and more cheaply than humans, but can’t automate all occupations. Huh?

I suppose there are occupations that consist mostly of having status rather than doing tasks (queen of England, or waiter at a classy restaurant that won’t automate service due to the high status of serving food the expensive way). Or occupations protected by law, such as gas station attendants who pump gas in New Jersey, decades after most drivers switched to pumping for themselves.

But I’d be rather surprised if machine learning researchers would think of those points when answering a survey in connection with a machine learning conference.

Maybe the actual wording of the survey questions caused a difference that got lost in the abstract? Hmmm …

“High-level machine intelligence” (HLMI) is achieved when unaided machines can accomplish every task better and more cheaply than human workers

versus

when all occupations are fully automatable. That is, when for any occupation, machines could be built to carry out the task better and more cheaply than human workers.

I tried to convince myself that the second version got interpreted as referring to actually replacing humans, while the first version referred to merely being qualified to replace humans. But the more I compared the two, the more that felt like wishful thinking. If anything, the “unaided” in the first version should make that version look farther in the future.

Can I find any other discrepancies between the abstract and the details? The 120 years in the abstract turns into 122 years in the body of the paper. So the authors seem to be downplaying the weirdness of the results.

There’s even a prediction of a 50% chance that the occupation “AI researcher” will be automated in about 88 years (I’m reading that from figure 2; I don’t see an explicit number for it). I suspect some respondents said this would take longer than for machines to “accomplish every task better and more cheaply”, but I don’t see data in the paper to confirm that [1].

A more likely hypothesis is that researchers alter their answers based on what they think people want to hear. Researchers might want to convince their funders that AI deals with problems that can be solved within the career of the researcher [2], while also wanting to reassure voters that AI won’t create massive unemployment until the current generation of workers has retired.

That would explain the general pattern of results, although the magnitude of the effect still seems strange. And it would imply that most machine learning researchers are liars, or have so little understanding of when HLMI will arrive that they don’t notice a 50% shift in their time estimates.

The ambiguity in terms such as “tasks” and “better” could conceivably explain confusion over the meaning of HLMI. I keep intending to write a blog post that would clarify concepts such as human-level AI and superintelligence, but then procrastinating because my thoughts on those topics are unclear.

It’s hard to avoid the conclusion that I should reduce my confidence in any prediction of when AI will reach human-level competence. My prior 90% confidence interval was something like 10 to 300 years. I guess I’ll broaden it to maybe 8 to 400 years [3].

P.S. – See also Katja’s comments on prior surveys.

[1] – the paper says most participants were asked the question that produced the estimate of 45 years to HLMI, the rest got the question that produced the 122 year estimate. So the median for all participants ought to be less than about 84 years, unless there are some unusual quirks in the data.

[2] – but then why do experienced researchers say human-level AI is farther in the future than new researchers, who presumably will be around longer? Maybe the new researchers are chasing fads or get-rich-quick schemes, and will mostly quit before becoming senior researchers?

[3] – years of subjective time as experienced by the fastest ems. So probably nowhere near 400 calendar years.

I’ve recently noticed some possibly important confusion about machine learning (ML)/deep learning. I’m quite uncertain how much harm the confusion will cause.

On MIRI’s Intelligent Agent Foundations Forum:

If you don’t do cognitive reductions, you will put your confusion in boxes and hide the actual problem. … E.g. if neural networks are used to predict math, then the confusion about how to do logical uncertainty is placed in the black box of “what this neural net learns to do”

On SlateStarCodex:

Imagine a future inmate asking why he was denied parole, and the answer being “nobody knows and it’s impossible to find out even in principle” … (DeepMind employs a Go master to help explain AlphaGo’s decisions back to its own programmers, which is probably a metaphor for something)

A possibly related confusion, from a conversation that I observed recently: philosophers have tried to understand how concepts work for centuries, but have made little progress; therefore deep learning isn’t very close to human-level AGI.

I’m unsure whether any of the claims I’m criticizing reflect actually mistaken beliefs, or whether they’re just communicated carelessly. I’m confident that at least some people at MIRI are wise enough to avoid this confusion [1]. I’ve omitted some ensuing clarifications from my description of the deep learning conversation – maybe if I remembered those sufficiently well, I’d see that I was reacting to a straw man of that discussion. But it seems likely that some people were misled by at least the SlateStarCodex comment.

There’s an important truth that people refer to when they say that neural nets (and machine learning techniques in general) are opaque. But that truth gets seriously obscured when rephrased as “black box” or “impossible to find out even in principle”.
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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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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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Why do people knowingly follow bad investment strategies?

I won’t ask (in this post) about why people hold foolish beliefs about investment strategies. I’ll focus on people who intend to follow a decent strategy, and fail. I’ll illustrate this with a stereotype from a behavioral economist (Procrastination in Preparing for Retirement):[1]

For instance, one of the authors has kept an average of over $20,000 in his checking account over the last 10 years, despite earning an average of less than 1% interest on this account and having easy access to very liquid alternative investments earning much more.

A more mundane example is a person who holds most of their wealth in stock of a single company, for reasons of historical accident (they acquired it via employee stock options or inheritance), but admits to preferring a more diversified portfolio.

An example from my life is that, until this year, I often borrowed money from Schwab to buy stock, when I could have borrowed at lower rates in my Interactive Brokers account to do the same thing. (Partly due to habits that I developed while carelessly unaware of the difference in rates; partly due to a number of trivial inconveniences).

Behavioral economists are somewhat correct to attribute such mistakes to questionable time discounting. But I see more patterns than such a model can explain (e.g. people procrastinate more over some decisions (whether to make a “boring” trade) than others (whether to read news about investments)).[2]

Instead, I use CFAR-style models that focus on conflicting motives of different agents within our minds.

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