161–170 of 179 posts
Very funny. If it's true that all the talk of computers and statistical modelling at Bridgewater is bullshit, and it's just a vanilla hedge fund, then it reminds me of the story from The Man Who Solved the Market that Renaissance was founded as a quant fund but none of the algorithms actually worked, so Simons just traded on hunches until D.E Shaw started competing with them years later.
Earlier quoted context omitted.
Yeah that's the thing with investing. Markets change, trades get crowded, data/tech gets commoditized, and strategies stop working. If you do not have a process and evolve, you are roadkill. So some guys who are early, big and right can attract a halo that long outlives its reality. To build some piece of tech you need to have some sort of process, and it is probably replicable to building another piece of tech. It i…
Buffett seems to do quite well for himself from what we often hear.
Very funny. If it's true that all the talk of computers and statistical modelling at Bridgewater is bullshit, and it's just a vanilla hedge fund, then it reminds me of the story from The Man Who Solved the Market that Renaissance was founded as a quant fund but none of the algorithms actually worked, so Simons just traded on hunches until D.E Shaw started competing with them years later.
The mythology focusing on math/quant I think is a feature, for them.
Apologies for not finding the original; my $SE appears to be dominated by a recent tax deal instead.
It's interesting. I have seen rating systems work – in the military. They're harsh to some, but in some situations, you have to filter out B and C players to keep the A players alive. I do think transparent systems w/360º peer reviews are better than many others. I wonder what else is out there that might work.
We’re talking about OERs and NCOERs right? Those pencil-whipped, boilerplate evals where if you have a 300 PT score and don’t get a DUI/beat your spouse, 1/1? The process that takes 24 months to kick someone out? I don’t think you know what you’re talking about in this regard and/or didn’t serve, candidly.
NCOERs/OERs aren't great, I agree. They're almost as bad and useless as command climate surveys.
It's interesting. I have seen rating systems work – in the military. They're harsh to some, but in some situations, you have to filter out B and C players to keep the A players alive. I do think transparent systems w/360º peer reviews are better than many others. I wonder what else is out there that might work.
You mention rating so maybe you're talking about enlisted only, which granted I don't know how those work. But the up-or-out system used for officers in the US military is notorious for being primarily political after like O3 and historically almost comically incapable of rejecting unqualified but politically sophisticated/connected officers.
Earlier quoted context omitted.
You mention rating so maybe you're talking about enlisted only, which granted I don't know how those work. But the up-or-out system used for officers in the US military is notorious for being primarily political after like O3 and historically almost comically incapable of rejecting unqualified but politically sophisticated/connected officers.
SNCOs have up-or-out too, for sure. I agree that the promotion system across the board is FUBAR. I'm talking about other sytems, typically at the unit level, that can be used to supplement or even side-load the current 'official' rating system, which is a joke.
I worked with Ferrucci in BW. The internals of the employee rating system that preceded him were hilarious. In short: Everyone is encouraged to rate anyone else in a variety of categories, as often as possible. Every rating is public. You know who is rating you, and how they did it. Those ratings are put together to get a score in every category, and can be seen by anyone. It's your Baseball Card. The problem is that…
If those scores can be seen by everybody it should be possible to mine the data and reverse engineer the algorithm. A bad actor could use this to craft a „malicious review“.
I worked with Ferrucci in BW. The internals of the employee rating system that preceded him were hilarious. In short: Everyone is encouraged to rate anyone else in a variety of categories, as often as possible. Every rating is public. You know who is rating you, and how they did it. Those ratings are put together to get a score in every category, and can be seen by anyone. It's your Baseball Card. The problem is that…
If those scores can be seen by everybody it should be possible to mine the data and reverse engineer the algorithm. A bad actor could use this to craft a „malicious review“.
Earlier quoted context omitted.
> My high credibility makes someone else have bad credibility, which changes the rankings again. How many iterations do we run before we consider the results stable? Tangential to your point but this is actually a solved problem and you only have to run once. You might even recognize the solution [1] [2]. [1]: https://en.wikipedia.org/wiki/PageRank [2]: https://en.wikipedia.org/wiki/Eigenvalues_and_eigenvectors
More generally factor graph optimization / msg passing, if you don't need the constraint that it must be an eigenvector/eigenvalue operation. The number of iterations is bounded by graph width but is locally constant, if memory serves.