NB

Nathan_Barnard

858 karmaJoined Nov 2019

Bio

Blog at The Good Blog https://thegoodblog.substack.com/

Comments
79

This looks fantastic, thanks for putting it together!

Thanks for your comment Ceb. 

I think my case for more focus on good statistical work when looking at governance is that when doing good statistical work on interventions, we often find very high degrees of variation in effect sizes that are enough to justify the extra work of the intervention. I'm personally very unsure of the effect size of changes in liability law, soft law, and various corporate governance interventions on accident rates. 

There's been lots of great case study/best guess/expert consensus work on these questions which I think is often great and I'm very happy exists, but leaves me with large uncertainties about effect sizes, and so on the current margin, I want more good statistical work. 

I think the case for good statistical work on areas like degree of misuse risk and forecasting is stronger because I think that people's takes on these questions are pretty grounded in quite theoretical arguments (unlike governance interventions which are much more empirically grounded) that I think would benefit a lot from more grounded statistical work. 

This is great Matt! I think I'd be also be interested in work trying to estimate the effect sizes of this stuff, as well as research on optimal design. 

Strongly there should be more explicit defences of this argument. 

One way of doing this in a co-operative way might working on co-operative AI stuff, since it seems to increase the likelihood that misaligned AI goes well, or at least less badly. 

Yeah, I think a Bayesian perspective is really helpful here and this reply seems right.  

I think overrated-underrated is useful because it's trying to say whether we should be doing more or less of X on the margin. Often it's much more useful to know whether something is good on the current margin rather than on average. 

There isn't only one notion of utility - utility in decision theory is different to utility in ethics. Utility in decision theory can indeed be derived from choices over lotteries and is incomparable between individuals (without further assumptions) and is equivalent under positive affine transformation because it's just representing choices. 

 Utility in moral philosophy refers to value and typically refers to the value of experiences (as opposed to other conceptions of the good like satisfaction of preferences), is comparable between individuals without further assumptions and isn't equivalent under positive affine transformation. 

An individual's utility (on either of the definitions) may or may not be changed by the political process. 

Consider a new far-right party entering the political sphere. They successfully changed political conversations to be more anti-immigration and have lots of focus on immigrant men committing sexual violence. 

A voter exposed to these new political conversations has their choice behaviour changed because they now feel more angry towards immigrants and want to hurt them, rather than because they think that more restrictive immigration policies would make them personally safer, for instance. 

This same voter also has utility - in the moral philosophy sense - changed by the new political conversation. Now they feel sadistic pleasure when they hear about immigrants being deported on the news, leading to better subjective experiences when they see immigrants being deported. 

I strongly reject the claim that we should imagine voters as exclusively deciding how to vote in terms of the personal benefits they derive in expectation from policies. I think people support capital punishment mostly because it fits with their inbuilt sense of justice rather than because they think it benefits them. 

We could (probably) represent this voter as being an expected utility maximiser where they have positive utility from capital punishment, in the decision theory sense. This is a different claim from the claim that a voter expects their subjective experiences to be more positively valenced when there's capital punishment. 

I'm afraid I can't comment on what ignorance factors do or do not account for under Bayesian regret without rereading the paper, but it's of course possible that they do account for that disparity between actual and assumed preferences. 

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