aragilar
aragilar has not written any posts yet.

I guess the thing I'm questioning is how well does Approval actually reflect the preferences of the electorate. Let's use Melbourne as an example: it was a safe Labor seat, but is now controlled by the Greens (there a useful summary of the seat's history at https://www.abc.net.au/news/federal-election-2016/guide/melb/ ). Under Approval, Liberal voters (who know their candidate isn't going to win, and want to deny Labor the seat) would vote for the Greens over Labor (e.g. approve Liberal and Greens); Greens voters would only vote Greens (they definitely won't vote Liberal); Labor vote Labor (as adding the Greens to the their vote only weakens their candidate). If the Greens win, Liberal voters can... (read more)
Why would you prefer Approval over IRV? I'm Australian, where we use IRV, and I'd find it significantly harder to work out who to vote for under Approval. Most voting examples I've seen (including jefftk) seem to have a small number of candidates, whereas here, in any seat that's not safe, there's at least 10+ candidates (where probably half I'd think about approving, but if I do that, then we're basically back to a 2-party system, meaning that Approval is worse than IRV in reflecting my preferences), let alone the 100+ candidates for a senate seat (which is multi member, but is sufficiently close to IRV that treating them the same from a voter decision view is reasonable). I can see the advantage of a Condorcet method over IRV (I know the Debian project uses it for elections/project votes), but approval seems only slightly better than FPTP.
One thing no-one has mentioned yet is who is writing the code: I'm assuming your background is a web developer/software engineer? Most deep learning users I've encountered are ex-scientists. This strongly discounts your benefits. First, many of them only know one language (most likely one of Python, R or Matlab, but there's a smattering of others which are used). Learning a new language is hard for these ex-scientists (remember, someone doing a CS degree will have likely seen 5+ languages), especially one like JavaScript: rapidly changing, libraries have breaking changes, and a whole new set of tools/processes to learn (e.g. minification or tree-shaking).
Second, while the JS community is huge, the vast majority... (read 384 more words →)