[bazqux2]

I meant generally they are generally not useful. Sometimes they are. It depends on the purpose and what you want to build and who it's for.

Given that you're building a descriptive model it would depend if you're working with facts or with probabilities. If it's facts then Ontologies should work fine, for probabilities I'd recommend Bayesian techniques.

The input for these are usually small. From the sounds of it you're generating the input yourself so you should be safe.

[dredmorbius]

An ontology of technlogical mechanisms (or dynamics):

https://ello.co/dredmorbius/post/klsjjjzzl9plqxz-ms8nww

Particularly in economic and policy discussion, technology is just "technology". A black box. In economics, Solow's Residual is described, by Solow, as "the measure of our ignorance" of factor productivity growth influences -- it's quite literally, statistically, what's left over after accounting for labour and capital.

I see a few quite evident classifications which strike me as useful:

1. Fuels. Apply more energy to something, it tends to happen faster. Wood, plant and animal oils, fossil fuels, nuclear fission, possibly fusion.

2. Material properties. Some things are highly dependent on specific material properties. Conductivity of gold, silver, copper, and aluminium. Ferromagnetism. Hardness of diamond. Softness of graphite. Semiconducting of silicon. Fertilising properties of nitrogen, phosphorus, and potassium. Many others. Point being, you're now locked into availablity and other properties of that material.

3. Specific process knowledge. What used to be called "arts". Most of what's now considered "technology", from agriculture to zymurgy (though zyumurgy's actually fairly close to agriculture...). These approach theoretical efficiency limits.

4. What seem to be dendritic or web structured aspects. Computer chips and Moore's law are today's classic example, but I'd count communications, transport, and trade networks, cities and urbanisations, knowledge itself, and other elements among these. What they have in common is an increasing rate of progress with greater accumulation, modulo retarding factors.

There are several other elements. Sensing and measurement increase various capabilities -- navigation and fine metal machining come to mind. Symbolic processing, from speech and writing to abstract maths and programming. Organisation -- of people, states, business, and finance.

The final element, and one which popped out at me whilst devising the ontology, was the concept of hygiene or pollution factors. They're a distinct class of phenomena which if not addressed tend to put a damper on further growth, everything from infectuous disease in cities to heavy metal pollution, salination of croplands, traffic congestion, spam and fraud in communications and business networks. It's a superset of common categories such as "pollution" or "disease" or "social breakdown".

Anyhow, that's what I'm working on. I find it a useful organising tool, still developing the idea.

[nickpsecurity]

1. In semiconductors, we get more out of stuff when we put in less energy due to shrinking the transistors. Even increasing transistors in same node doesn't always result in more work since bigger chips have slower clock rates. I think you need to look at inputs, which include time, more than fuel given it doesn't apply to a lot of things. Even human body which, as you increase fuel, will work slower due to being gorged and then die with exploded stomach.

2. This is true. It's worth noting such dependencies.

3. Elaborate on that.

4. That's true. There's a lot of work on that topic already that you can draw on. I remember some showing that how the cities grew was similar to how bacteria looked. Weird stuff.

Re waste. You can model it as a separate thing that goes up when certain actions happen, then starts bringing them down. Definitely should be considered.