Perhaps EA’s roots in philosophy lead it more readily to this failure mode?
Take the diminishing marginal returns framework above. Total benefit is not likely to be a function of a single variable ‘invested resources’. If we break ‘invested resources’ out into constituent parts we’ll hit the buffers OP identifies.
Breaking into constituent parts would mean envisaging the scenario in which the intervention was effective and adding up the concrete things one spent money on to get there: does it need new PhDs minted? There’s a related operational analysis about time lines: how many years for the message to sink in?
Also, for concrete functions, it is entirely possible that the sigmoid curve is almost flat up to an extraordinarily large total investment (and regardless of any subsequent heights it may reach). This is related to why ReLU functions are popular in neural networks: because zero gradients prevent learning.
Perhaps EA’s roots in philosophy lead it more readily to this failure mode?
Take the diminishing marginal returns framework above. Total benefit is not likely to be a function of a single variable ‘invested resources’. If we break ‘invested resources’ out into constituent parts we’ll hit the buffers OP identifies.
Breaking into constituent parts would mean envisaging the scenario in which the intervention was effective and adding up the concrete things one spent money on to get there: does it need new PhDs minted? There’s a related operational analysis about time lines: how many years for the message to sink in?
Also, for concrete functions, it is entirely possible that the sigmoid curve is almost flat up to an extraordinarily large total investment (and regardless of any subsequent heights it may reach). This is related to why ReLU functions are popular in neural networks: because zero gradients prevent learning.