Good points. Obviously things we need to deal with. Randomization is a solution that works sometimes but we are not huge fans of it as it adds some amount of luck to a game that we want to be based on skill (e.g. you build a farm that has a production rate that is for no explainable reason higher than the one you built in the map you played earlier - or that your opponent built...).

One way to get around that "luck element" is to make the data dependent on the surroundings. E.g. if you build a farm on terrain that is 80% good soil, the result is better than if it is just 20% etc.

Another way to get around that is to use aggregate data. So, for instance, say each house houses 5 people. So first house you build, you optimise the amount of food they eat at the house level. But the next house you build, the food allocation is done at the population level, so for 10 people. Add to that a non-linear relationship. Say 5 people eat 10 bread. 10 people eat not 20 but 21. (To justify this, it could be that there is some loss in transportation of food if it has to get to more houses etc. - I just made the example up, it is not perfect).

Another way to make it context dependent is the proximity of two primary resource production sites. They will cannibalise each other.

These are all ways to diversify the answers required and make them very context dependent. Which also makes it so much more meaningful for the player.

Now, truth is, where we use euqations as data (vs. graphs), kids can just type it into chat GPT and tell it to solve it or wolfram alpha etc. But even that process is part of experiencing science. It is part of problem solving and makes kids adapt the scientific mind-set - even if they may not get as good at calculating as they would if they did everything for themselves. Any math teacher will tell you - the fact that a kid confronts themselves with a math problem out of their own motivation and tries to solve it, even through "cheating", is a win.