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.
Hi Peter,
In machine learning this approach of bundling models is called ‘bagging’:
https://en.m.wikipedia.org/wiki/Bootstrap_aggregating
It’s often complemented with ‘boosting’, building models on top of the predictions of previous models.
It’s interesting to apply these aspects to daily life.