Gerrymandering illustrates how automation in policy can be better than discretion. If you give a legislature the power to chop things up as they will, they will do so in ways that benefit the dominant local party. A better alternative is for an algorithm to draw districts instead, regularly revising so as to keep seats across a territory in line with popular vote (so that, e.g., when 40% of Californians vote GOP, GOP gets 40% of the House seats for California).

Bitcoin obeys a similar principle. Rather than delegating monetary policy to trusted authorities, it automates that policy in highly predictable ways, and regularly revises (i.e., the difficulty adjustment) to keep things in line with what's expected. The outcomes needn't be optimal for this system to be superior, note; for there is no guarantee that those trusted parties will enact optimal policies, and they often fail in this task! The best argument for automated policy, then, is not that it is for the best always and everywhere, but that it is typically for the better.

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I almost agree with you, but I have a slightly different approach hear me out.

Every high school student or registered voter can submit a map for districts, but only one map can win the map that wins is the map where the root mean square of the number of registered voters minus the average number of voters per district multiplied by the total perimeter of each district is minimized.

Because this is an NP-complete problem there will never be a guaranteed way of ensuring you have the best district, but each map can be exactly scored and only one will be the minimum.

Anytime I student or voter submits an improved map. It must be adopted for the next election.