## Posts

How should my timelines influence my career choice? 2021-08-03T10:14:33.722Z

Comment by Frederik on How should my timelines influence my career choice? · 2021-08-04T12:25:17.618Z · LW · GW

True, it's always good to remind oneself of a broader option space.

Could you elaborate what you mean by 'working at orgs'... since engineering would meet that definition, or do you mean explicitly other roles than engineering, such as ops or management?

I don't think I'd be a good fit for computer security, both in terms of pre-existing skills and interest, but I get your general point. (My undergrad was in physics, not CS, so I'm lacking quite a few of the traditional CS skills, except in the more theoretical subjects). Do you have a pointer to resources discussing the most needed skill sets in this broader cause area?

Comment by Frederik on How should my timelines influence my career choice? · 2021-08-04T01:03:25.695Z · LW · GW

Thanks! Yeah, that's a good point mulling over. I guess it would hinge on the marginal improvement of EV in the doomed scenario to make that assertion. I don't necessarily see things being completely, hopelessly doomed in a 15-20-year-to-AGI world. But I am also uncertain as to which role is more useful in the short-timeline world, aside from an engineer being able to contribute earlier. In the medium-term timeline world it seems to me like the marginal researcher has higher EV.

So if I would be completely uncertain, i.e. 50/50, which one is better in a short timeline world, then becoming a researcher would seem like the safer choice.

Comment by Frederik on How should my timelines influence my career choice? · 2021-08-03T11:41:45.430Z · LW · GW

In terms of flexibility, a PhD seems to score higher since it would be relatively straightforward to just stop it and do engineering instead, whereas the reverse would then require doing the full 4-5 years. I suppose it is also easier to automate an engineering job compared to research job.

Comment by Frederik on Expected utility and repeated choices · 2019-12-28T08:20:01.609Z · LW · GW

Well, if you assume these agents do not employ time-discounting then you indeed cannot compare trajectories, since all of them might have infinite utility (and are computationally intractable as you say) if they don't terminate.

We do run into the same problem if we assume realistic action spaces, i.e. consider all the things we could possibly do, as there are too many even for a single time step.

RL algorithms "solve" this by working with constrained action spaces and discounting future utility.. and also by often having terminating trajectories. Humans also work on (highly) constrained action spaces and have strong time discounting [citation needed], and every model of a rational human should take that into account.

I admit those points are more like hacks we've come up with for practical situations, but I suppose the computational intractability is a reason why we can't already have all the nice things ;-)

Comment by Frederik on Expected utility and repeated choices · 2019-12-27T21:49:31.162Z · LW · GW

The intuitive result you would expect only holds for utility function which are linear in x (I believe..), since we could then apply the utility function at each step and it would yield the same value as if applied to the whole amount.

Another case would be if you were to receive your utility immediately after playing each game (like in a reinforcement learning algorithm). In those cases is also applied to each outcome separately and would yield the result you would expect.

Also: (b) has a better EV in terms of raw $and due to law of large numbers we would expect the actual amount of money won by repeatedly playing (b) to approach that EV. So for many games we should expect any monotonic increasing utility function to favor (b) over (a) as the number of games approaches infinity. The only reason your U favors (a) over (b) for a single game is that it is risk-averse, i.e. sub-linear in x. As the amount of games approaches infinity the risk of choosing to play b becomes less and less until it is the choice between (essentially) winning 0.5$ for sure or 0.67\$ for sure in every game. If you think about it in these terms it becomes more intuitive why the behaviour observed by you is reasonable.

In other words: Yes! You do have to think about the amount of games you play if your utility function is not linear (or you have a strong discount factor).

Comment by Frederik on Deducing Impact · 2019-09-26T21:38:53.830Z · LW · GW

While I agree that using percentages would make impact more comparable between agents and timesteps, it also leads to counterintuitive results (at least counterintuitive to me)

Consider a sequence of utilities at times 0, 1, 2 with , and .

Now the drop from to would be more dramatic (decrease by 100%) compared to the drop from to (decrease by 99%) if we were using percentages. But I think the agent should 'care more' about the larger drop in absolute utility (i.e. spend more resources to prevent it from happening) and I suppose we might want to let impact correspond to something like 'how much we care about this event happening'.