I was talking about this with a coworker.
He is a lay carpenter; but a damn good one from what I can judge.
What he said was simply this "You're talking about machines that cost thousands, why are we having this meeting when I can put guys to work."
My response then, as now, is that I can put the right guys in the field faster and they'll to work faster. He quite liked it.
In our consideration we have looked at scenarios where legendary units are available.
The lifetime of a fair game of factorio we will have a situation where we have thousands of lay workers but none of a given quality. We have an egg.
So the next examples where I remove the legendary quality from the entire example.
I believe this will show Kovarex intentions for vertical scaling where realized and that it is more than a simple gambling mechanic.
It is appropriate because the drills I used on previous examples to hit a rate of 69-73 ips always will. That reward never changes. Resource drain does, it scales the time dimension a drill can capture coal. I've seen productivity used for this in Nullius (god bless those big drills) but resource drain is more nuanced because it doesn't change my rate of return.
So my input conditions of item/second are all valid, and my technique is to scale horizontally to capture output with the means available.
This shows how much of a speed mallus we have manipulating a/(1-r) by reducing quality. Manipulations in a aren't always so great. That's why a speed preference can cause hurt with plastic. In other models it's different but this will augment our ability to manipulate those.
Toying with a/(1-.5) shows that we get 2a. 1/(1-.6), will be (1.66)a. 6/6 vs 5/6, or an 18% decrease caused by a .10 shift. Quality module increases work by increasing a rate linearly in this manner. So it's something like a/(1-.1) v. a/(1-.2).
It's easy to see what happens if you use simple fractions to render that. a/(9/10) = a10/9. a/(8/10)= a10/8. Instead of a 1/9 increase we get a 2/8 one.
But I expect a punishing reduction across all builds and bigger than might first be suspected. The trip from a +585% building to a + 110% building is from 6.85 speed to 2.1 speed. So if speed is the only thing considered we expect a 1/3 drop in our builds (and might even forego recycling as a technique).
But in modeling we have at least the suggestion that our mallus will be more like going from a 10*(input)/8 to a 10*(input)/9 bonus. It will punish recycling harder than speed though
drop all output from that source.
So I start with a speed 3 build as my basis input. Speed 1 will increase quality by fractions of a percent here as well, but the jump is not considerable in the recyclers which are so
fast that for the same reason I use a normal assembler for my pipes I just horizontally scale
those.
There is another reason that a view of the test will show.
Basically if I go from speed 3's to speed 3's I'm going scale this layout so much I won't be able to fit it in a screen shot.
And we see the drop was not a 1/4 performance cut but nearly a 1/10 performance cut relative to the legendary build. This on its own shows vertical scaling.
How does an optimized build hold up?
Well relative to the one that I quoted above and showed earlier we went from 60 ips to 8. So the drop is consistently staggering, awful I am thinking is the word?
But relative speeding up input we're going faster: 3 and 8 are two different things. More practically in relation to waste we are directly making more parts in apples:apples comparison. As far as technical difficulty the reader will note the vertical scaling down didn't change my build size.
Blueprint is here in case I missed a module setting it up.
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I do believe that is a cluster of five 4 tiles, so a speed based method benefits from exponential scaling by a factor of about six in terms of area used.
Well, blueprint in case I miss count.
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
It is larger on screen than in the picture.
I cannot simply rest on this. Just because I have a hammer not everything is a nail. To scale to 1 ips to 5 I need to scale my entire drill line when I have legendaries and that costs material. So there's two more test cases to consider at this scale to show how I might cut that cost down if it's possible at all.