"Good judgement" and its components

By Owen Cotton-Barratt @ 2020-08-19T23:30 (+63)

Meta: Lots of people interested in EA (including me) think that something like "good judgement" is a key trait for the community, but there isn't a commonly understood definition. I wrote a quick version of these notes in response to a question from Ben Todd, and he suggested posting them here. These represent my personal thinking about judgement and its components.

Good judgement is about mental processes which tend to lead to good decisions. (I think good decision-making is centrally important for longtermist EA, for reasons I won't get into here.) Judgement has two major ingredients: understanding of the world, and heuristics.

Understanding of the world helps you make better predictions about how things are in the world now, what trajectories they are on (so how they will be at future points), and how different actions might have different effects on that. This is important for helping you explicitly think things through. There are a number of sub-skills, like model-building, having calibrated estimates, and just knowing relevant facts. Sometimes understanding is held in terms of implicit predictions (perhaps based on experience). How good someone's understanding of the world is can vary a lot by domain, but some of the sub-skills are transferrable across domains.

You can improve your understanding of the world by learning foundational facts about important domains, and by practicing skills like model-building and forecasting. You can also improve understanding of a domain by importing models from other people, although you may face challenges of being uncertain how much to trust their models. (One way that models can be useful without requiring any trust is giving you clues about where to look in building up your own models.)

Heuristics are rules of thumb that you apply to decisions. They are usually held implicitly rather than in a fully explicit form. They make statements about what properties of decisions are good, without trying to provide a full causal model for why that type of decision is good. Some heuristics are fairly general (e.g. "avoid doing sketchy things"), and some apply to specific domains (e.g. "when hiring programmers, put a lot of weight on the coding tests").

You can improve your heuristics by paying attention to your experience of what worked well or poorly for you. Experience might cause you to generate new candidate heuristics (explicitly or implicitly) and hold them as hypotheses to be tested further. They can also be learned socially, transmitted from other people. (Hopefully they were grounded in experience at some point. Learning can be much more efficient if we allow the transmission of heuristics between people, but if you don't require people to have any grounding in their own experience or cases they've directly heard about, it's possible for heuristics to be propagated without regard for whether they're still useful, or if the underlying circumstances have changed enough that they shouldn't be applied. Navigating this tension is an interesting problem in social epistemology.)

One of the reasons that it's often good to spend time with people with good judgement is that you can make observations of their heuristics in action. Learning heuristics is difficult from writing, since there is a lot of subtlety about the boundaries of when they're applicable, or how much weight to put on them. To learn from other people (rather than your own experience) it's often best to get a chance to interrogate decisions that were a bit surprising or didn't quite make sense to you. It can also be extremely helpful to get feedback on your own decisions, in circumstances where the person giving feedback has high enough context that they can meaningfully bring their heuristics to bear.

Good judgement generally wants a blend of understanding the world and heuristics. Going just with heuristics makes it hard to project out and think about scenarios which are different from ones you've historically faced. But our ability to calculate out consequences is limited, and some forms of knowledge are more efficiently incorporated into decision-making as heuristics rather than understanding about the world.

One kind of judgement which is important is meta-level judgement about how much weight to put on different perspectives. Say you are deciding whether to publish an advert which you think will make a good impression on people and bring users to your product, but contains a minor inaccuracy which would require much more awkward wording to avoid. You might bring to bear the following perspectives:

A) The heuristic "don't lie"
B) The heuristic "have snappy adverts"
C) The implicit model which is your gut prediction of what will happen if you publish
D) The explicit model about what will happen that you drew up in a spreadsheet
E) The advice of your partner
F) The advice of a professional marketer you talked to

Each of these has something legitimate to contribute. The choice of how to reach a decision is a judgement, which I think is usually made by choosing how much weight to put on the different perspectives in this circumstance (including sometimes just letting one perspective dominate). These weights might in turn be informed by your understanding of the world (e.g. "marketers should know about this stuff"), and also by your own experience ("wow, my partner always seems to give good advice on these kinds of tricky situations").

I think that almost always the choice of these weights is a heuristic (and that the weights themselves are generally implicit rather than explicit). You could develop understanding of the world which specify how much to trust the different perspectives, but as boundedly rational actors, at some point we have to get off the understanding train and use heuristics as shortcuts (to decide when to spend longer thinking about things, when to wrap things up, when to make an explicit model, etc.).

Overall I hope that people can develop good object-level judgement in a number of important domains (strategic questions seem particularly tricky+important, but judgement about technical domains like AI, and procedural domains like how to run organisations also seem very strongly desirable; I suspect there's a long list of domains I'd think are moderately important). I also hope we can develop (and support people to develop) good meta-level judgement. When decision-makers have good meta-level judgement this can act as a force-multiplier on the presence of the best accessible object-level judgement in the epistemic system. It can also add a kind of robustness, making badly damaging mistakes quite a lot less likely.