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Random selection is necessary to create stable meritocratic institutions

Campbell's Law (a variant of Goodhart's Law) states that the more a metric is used for social decision-making, the more it will be subject to corruption which distorts and corrupts not only the metric itself, but the very social processes it was meant to measure. Selection criteria for a position of authority are one example of such a metric. When selection criteria are opaque, it is difficult for them to become a target, preserving their utility as measures. For governance positions however, it

Why random selection is necessary to create stable meritocratic institutions

Campbell's Law (a variant of Goodhart's Law) states that the more a metric is used for social decision-making, the more it will be subject to corruption which distorts and corrupts not only the metric itself, but the very social processes it was meant to measure. Selection criteria for a position of authority are one example of such a metric. When selection criteria are opaque, it is difficult for them to become a target, preserving their utility as measures. For governance positions however, it

Hill Space: Neural nets that do perfect arithmetic (to 10⁻¹⁶ precision)

When understood and used properly, the constraint W = tanh(Ŵ) ⊙ σ(M̂) (introduced in NALU by Trask et al. 2018 ) creates a unique parameter topology where optimal weights for discrete operations can be calculated rather than learned . During training, they're able to converge with extreme speed and reliability towards the optimal solution. Most neural networks struggle with basic arithmetic. They approximate, they fail on extrapolation, and they're inconsistent. But what if there was a way to m