Well in your case there's a "phase transition" for the task around 1 minute, so in that case the return function is sublinear in t (if you assume given time G starts at 0. G=t), or linear in it (if you assume it starts at 50, G=50+t). I just wanted to highlight this principle: his statement is equivalent to "For small t, the return for freeing t seconds from computer tasks in superlinear on t". And in general: "The analysis of time redistribution hinges on the linearity of return function for the freed time t".
I do believe that for most persons and most situations, they do tasks in a segmented fashion, working to completion. So if you give them a small amount of extra time it's just going to shift their segments until it hits a synchronized event, like sleeping at a predetermined time or having a meeting -- so all that happens is they get e.g. a millisecond each before they sleep. Under this model it's quite clear to me for very small t the return function is superlinear, and it's better to give 1 person 100 seconds than 100k persons a millisecond (assuming this is one-off of course): for the 100k persons the millisecond is insufficient to elicit any change whatsoever. It's much less than a neuron firing time.
I do believe that for most persons and most situations, they do tasks in a segmented fashion, working to completion. So if you give them a small amount of extra time it's just going to shift their segments until it hits a synchronized event, like sleeping at a predetermined time or having a meeting -- so all that happens is they get e.g. a millisecond each before they sleep. Under this model it's quite clear to me for very small t the return function is superlinear, and it's better to give 1 person 100 seconds than 100k persons a millisecond (assuming this is one-off of course): for the 100k persons the millisecond is insufficient to elicit any change whatsoever. It's much less than a neuron firing time.