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The Internet Identity Layer

The Internet Identity Layer

Another reason rapid advancement is possible is because management and operations skills are so in demand that someone who has them can rise quickly.

A third reason is that operations roles can place you at the centre of the organisation, giving you the internal knowledge and relationships you need to take a more central position in the future. Carrick recounts how he started at FHI as an office manager and became Assistant Director of the Governance of AI Program doing research management:

Keep in mind that operations work at an organization like FHI can be a fantastic way to tool up and gain fluency in this space, orient yourself, discover your strengths and interests, and make contacts.

This is how I snuck into FHI about 2 years ago, on a 3 week temporary contract as an office manager. I flew from the US on 4 days notice for the chance to try to gain fluency in the field. While my case of “working my way up from the mailroom” is not likely to be typical (I had a strong CV), or necessarily a good model to encourage, it is definitely the case that you can pick up a huge amount through osmosis at FHI, and develop a strong effective altruist career network.

This can set you up well for a wise choice of graduate programs or other career direction decisions, as well as more senior positions in operations.

This said, one transition that doesn’t normally work is from operations into research, so if research is your endgame, we wouldn’t recommend starting in operations.

Turning to transitions to other organisations, operations is a skill-set needed in basically all organisations, so you can take the skills you’ll learn and apply them in many different areas in the future. As Tanya says in her talk:

The skills that you develop while working in operations are extremely fungible, they are very transferable skills, so you have the opportunity to work with organisations in a lot of different cause areas. When a cause area becomes extremely important suddenly, you can pivot towards it. Because these skills are so flexible, if you’re good at your job, you will always have the opportunity to work on extremely interesting and exciting projects.

Working in operations at one of these organisations also provides all the other benefits of working at effective altruist non-profits that we cover in our full profile on the topic . Namely, you can make lots of connections with other smart, altruistic people; the organisations often have a strong culture of personal growth, helping you to improve your skills, and the whole field is growing rapidly, making it easier to notch up impressive achievements.

Overall, we think that if you want to work in effective altruist organisations in the future, especially those focused on catastrophic risk and community building, then you can progress significantly faster by entering the community directly, if you have the opportunity.

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On phenomena and existences

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A finite set of events is mutually independent if every event is independent of any intersection of the other events [2] —that is, if and only if for every k -element subset of { A i } i = 1 n {\displaystyle \{A_{i}\}_{i=1}^{n}} ,

This is called the multiplication rule for independent events. Note that it is not a single condition involving only the product of all the probabilities of all single events (see below for a counterexample); it must hold true for all subsets of events.

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generated by them are independent; that is to say, for every a and b , the events { X a } and { Y b } are independent events (as defined above). That is, X and Y with cumulative distribution functions F X ( x ) {\displaystyle F_{X}(x)} and F Y ( y ) {\displaystyle F_{Y}(y)} , and Cover Up Dress Jonathan Simkhai How Much Cheap Price Fashion Style Online Outlet Discount Authentic Sale Release Dates AMnqrk
f X ( x ) {\displaystyle f_{X}(x)} and f Y ( y ) {\displaystyle f_{Y}(y)} , are independent the combined random variable ( X , Y ) has a joint cumulative distribution function

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says:
March 15, 2018 at 4:06 pm

Maybe it will help to consider specific numbers. Suppose the main effect of the treatment is, say, 0.6 and the interaction with sex is 0.3, so that the treatment effect is 0.45 for women and 0.75 for men. Then if you do the regression with the predictors on the (-0.5, 0.5) scale, you’ll get estimates of 0.6 for the treatment effect and 0.3 for the interaction. If you do the regression with the predictors on the (-1, 1) scale, you’ll get estimates of 0.3 for the treatment effect and 0.075 for the interaction.

Depending on the context, maybe I’m being too pessimistic in the above post. One could easily imagine a treatment where the main effect is 0.6, with an effect of 0.3 for women and 0.9 for men. In that case, the interaction is as large as the main effect, so then you only need 4 times the sample size, not 16.

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says:
March 15, 2018 at 11:30 am

Andrew just means it as a discussion point, but I would tend to expect most interesting interactions to be much less than half the size of main effects. For example a common hypothesis is that one’s sex will effect one’s opinion on abortion. But actually religiosity has at least four or five times the explanatory power of sex interactions on that data (and the wrong way for most explanatory hypotheticals, women are slightly more pro life than men). Now as it turns out we’ve done enormous polls on the subject so we can still tease out smaller interactions but I would tend to think of a power effect of 1/2 as being an overestimate for discussion not an underestimate.

Reply to this comment
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Based on some reasonable assumptions regarding main effects and interactions, you need 16 times the sample size to estimate an interaction than to estimate a main effect.

Aren’t you assuming zero intercept and zero sex/gender main effect in this model?

Reply to this comment
says:
March 15, 2018 at 10:59 am

No. The values of the intercept and the “main effect” for sex won’t matter here because, by construction, the four predictors in the model are statistically independent (that is, we’re assuming equal numbers of men and women, or approximately equal numbers of men and women, in the two treatment groups).

Reply to this comment
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I made a post in the other thread (not showing up yet) that fixed a bug and made some modifications to my sim (thanks Arno). Actually, none of these variants of lm duplicated your result but leaving out the sex main effect was necessary:

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