Frustrating that the lines between the rows in that table kind of shift and wiggle because of the various kinds and unclear relative importance and meaning of different kinds of “feedback”.
The canonical example is Einstein getting special relativity basically perfectly right with almost no reality-feedback because he had math-feedback. Started with a good amount of data but so do we.
In AI research & bio we are blessed with several kinds of useful feedback at several timescales, although the ultimate review hasn’t come yet.
I’m tempted to be rude and say your first post should be titled “tips for interacting with large orgs”. I may be misunderstanding you so that comment isnt really granted. If you did title it that though, I would be just as interested.
For planes, a hill had enough feedback. For masks, a kitchen spray faucet is maybe enough if you’re honest with yourself. The US military gets mountains of data about its operations and their failure causes but whoever is running things might do better having a date night with their diary than having a presentation from their intelligence officers. I don’t think data/experiments are the big missing piece across the board. In policy of course it is about practice and structures and connections 95%...
So all this to say: there are most likely big ways we can get more feedback on all our longterm efforts and we certainly ought to, but I expect that this advice will need to be extremely specific to be useful, and that people are already trying very hard all the time to get meaningful data, and that just saying moar experimetns won’t get us far.
Sorry, but this seems to me to confuse the topic of the post “Experimental Longtermism” and the topic of this post. Note that the posts are independent, and about different topics.
The table in this post is about timescales of OODA loops (observe–orient–decide–act), not about feedback. For example, in a situation which is unfolding on a timescale of days and weeks, as was the early response to covid, some actions are just too slow to have an impact: for example, writing a book, or funding basic research. The same is true for some decision and observation making processes: the speed of the whole loop matters, and if the decide part needs, for example, to 50 ppl from 5 different organizations to convene and reach a consensus, it won’t work.
The first O in OODA implies something new to observe, no? And within the OODAL of a city there are many smaller loops where eg you see if your friend where’s a mask if you ask them.
And with the ToC and such I thought the first post was kind of an introduction/abstract.
Anyway I’m looking forward to these posts and very curious what the OODA loop of a city looks like
Frustrating that the lines between the rows in that table kind of shift and wiggle because of the various kinds and unclear relative importance and meaning of different kinds of “feedback”.
The canonical example is Einstein getting special relativity basically perfectly right with almost no reality-feedback because he had math-feedback. Started with a good amount of data but so do we.
In AI research & bio we are blessed with several kinds of useful feedback at several timescales, although the ultimate review hasn’t come yet.
I’m tempted to be rude and say your first post should be titled “tips for interacting with large orgs”. I may be misunderstanding you so that comment isnt really granted. If you did title it that though, I would be just as interested.
For planes, a hill had enough feedback. For masks, a kitchen spray faucet is maybe enough if you’re honest with yourself. The US military gets mountains of data about its operations and their failure causes but whoever is running things might do better having a date night with their diary than having a presentation from their intelligence officers. I don’t think data/experiments are the big missing piece across the board. In policy of course it is about practice and structures and connections 95%...
So all this to say: there are most likely big ways we can get more feedback on all our longterm efforts and we certainly ought to, but I expect that this advice will need to be extremely specific to be useful, and that people are already trying very hard all the time to get meaningful data, and that just saying moar experimetns won’t get us far.
Sorry, but this seems to me to confuse the topic of the post “Experimental Longtermism” and the topic of this post. Note that the posts are independent, and about different topics.
The table in this post is about timescales of OODA loops (observe–orient–decide–act), not about feedback. For example, in a situation which is unfolding on a timescale of days and weeks, as was the early response to covid, some actions are just too slow to have an impact: for example, writing a book, or funding basic research. The same is true for some decision and observation making processes: the speed of the whole loop matters, and if the decide part needs, for example, to 50 ppl from 5 different organizations to convene and reach a consensus, it won’t work.
The first O in OODA implies something new to observe, no? And within the OODAL of a city there are many smaller loops where eg you see if your friend where’s a mask if you ask them.
And with the ToC and such I thought the first post was kind of an introduction/abstract.
Anyway I’m looking forward to these posts and very curious what the OODA loop of a city looks like