I don't think the fund level ratings matter anymore. a16z has more than 300 partners managing $35B across separate funds in tech, bio, crypto, cultural leadership, and other areas. The partner(s) you work with matter more than the shingle outside the office.
Aside: I have an optimization algorithm and I'm curious if ELO ranking (or TrueSkill) would be a decent approximate solution.<p>I have a sparse matrix of probabilities that I want to turn into a DAG. If x[m,n] = pr it means that m is a descendent (direct or transitively) of n with probability pr. I want to construct a DAG over these edges.<p>Most importantly, I want a solution that maintains the DAG property, i.e. no cycles.
Given that constraint, I want to maximize the total probability of edges kept in the DAG combined with the (1 - probability) edges removed from the graph.<p>Any suggestions on how to implement this optimization algorithm?<p>Perhaps I could use an ELO or TrueSkill ranking as an approximation. The difficulty is sampling matches, but perhaps it makes sense to sample non-zero edges randomly, uniformly. So nodes with high in-degree or high out-degree are selected more frequently, since they are more likely to impose constraints on the graph. The probability of winning is determined by the edge probability.<p>This doesn't guarantee a DAG but would be a great initialization point. Anyway, I'm curious about alternate ideas or refinements to the above.
In case anyone else is fuzzy on the precise meaning of "Elo": <a href="https://en.wikipedia.org/wiki/Elo_rating_system" rel="nofollow noreferrer">https://en.wikipedia.org/wiki/Elo_rating_system</a>
Personal vote for Hoxton. Nice people and very knowledgeable, and ethical too. Note that many (top) EU VCs aren't in this dataset.<p>This is probably due to 'We only include firms where we received 100 or more comparisons to other firms.', which in Europe, where the VC landscape is - fortunately - much more fragmented isn't going to happen all that often except for seed funds.<p>Also, it might be worth it to add PE parties as well because that's one track where founders may well end up and those interactions do not always go smoothly.
Interested in some parts of the methodology (if perhaps someone knows or the creators spot this thread):<p>* The pairwise comparison: does it freeze updates and calculate all shifts at the same time? For example if you had A > B > C do you calculate the impact of {A>B, A>C, B>C}, sum these impacts together (grouped by the VC), and then apply them? Or do you do it iteratively: if a firm had {A}, then {A>B}, then {A>B>C} do you add 0 then 1 then 2 comparisons as you get new data?<p>* How do you handle the fact that respondents to the survey are over a large time-frame, so some VCs might get better or worse over that time frame? Is there some Elo-decay applied?