[vidarh]

> What's the point of a simulation pretending to simulate a complex system when it could have just implemented a simpler system in the first place?

Why are you assuming the simpler system would achieve the goal of the simulation? If the intent is to see how a "player" responds to an environment that is a realistic reproduction of a given environment, then placing them in a simulation that isn't a realistic reproduction may be insufficient.

> Also, What is "observable"? If you mean "what people [well, simulation subjects] might look at", recall that people do take past data of weather, including data that's not collected at the time (e.g. tree ring data).

The only "observable" data that complicates things are data that has been affected by "players", and where the effect has been observed by them and may be remembered by them. Anything that has not been affected by "players" can, assuming a simulation that consists of a deterministic base parameterised by time with a layer of "player modifications", be generated purely from the simulation. Anything that has been affected by "players" requires you to record those changes and alter the simulation based on those recorded differences.

Recording those changes has a significant "cost" because if they have knock on effects you also need to account for those knock on effects.

But as long as you store that state, you can still recreate any past state.

(consider here Minecraft as a gross simplification: If I chop down a tree, but leave a stump, and then walk outside of the chunks the simulation runs, and walk back again, I still expect to see the stump; this requires storing state where you otherwise could have re-generated the chunk purely from the seeds of the simulator - the ability to minimize that state is what has the potential to make a complex simulation viable)

What I was getting at was that a way of optimizing away most of that stored state is to keep track of when you can plausibly perturb what should be pseudo-random values in the simulation to "nudge" things back towards the base outcome of the simulator. When you can, you can then throw away saved state going forwards from that point in the simulation.

(e.g. add decay and growth, and the changing environment means you can bit by bit throw away state where throwing it away can be justified by the passage of time altering the environment)

The recording of things like weather data has no impact on that. You're not changing what the weather was. You're reducing the cost of recording aberrations from the baseline simulation to cut the cost of simulation going forwards.

[yyyk]

"Why are you assuming the simpler system would achieve the goal of the simulation? If the intent is to see how a "player" responds to an environment that is a realistic reproduction of a given environment, then placing them in a simulation that isn't a realistic reproduction may be insufficient."

In a chaotic quantum system, everything has an influence, including the butterfly 2000km from where you are sitting now.

We can't 'just implement the parts mattering to the players', because the butterfly could cause a storm in your location a month from now, and we can't know whether it does or does not without actually computing it.

So if the system is modeling all the butterflies, you have a simulation cost problem. If it not modeling the butterflies, than the system does not truly implement 'the weather', but a simpler system, in which point it would have been easier to simulate a simpler system with less noise.

"The only "observable" data that complicates things are data that has been affected by "players", and where the effect has been observed by them and may be remembered by them."

Lets look at the example of past data. Quite a lot of it is not by "players" (lets assume humans are the "players" here). Tree ring data, geological formations... How does our simulation deal with that?

A) Calculate everything in advance. That's not very promising.

B) Calculate everything the 'players' may care about in advance.

In some systems, that's equivalent to A. Even in other systems, the players pick their own motivation and tests. If the simulation is of any worth, we may not know in advance what tests they chose, so that's not promising either.

C) When the players are somewhere, calculate the past of anything they might be examined by them.

That would require enormous amounts of state or massive calculation. Calculating the past of quantum systems can be as complicated as approach A.

D) Read the players' mind and just give them what they want (why not? It's not outside the scope of a full simulation).

There are some coordination issues here (what if different players expect different things? or if player has a flaw in the original calculation?), but that seems manageable. Even more so if we are allowed to rewrite the players' mind. Of course, that means there'll never be a proof of the simulation since it will be prevented/rewritten immediately...

E) Don't bother with consistency, lie to the players all the time and hope they could rationalize everything. I'd have expected the world to be weirder and more inconsistent than it is in that case.

[vidarh]

> We can't 'just implement the parts mattering to the players', because the butterfly could cause a storm in your location a month from now, and we can't know whether it does or does not without actually computing it.

Because a central aspect of quantum mechanics is that we can not possibly predict what "should" have happened, then it follows that as long as the simulated outcome does not introduce predictability that shouldn't be there, a generated outcome is impossible to distinguish from a real one.

But that does of course not matter, because we have nothing to compare it against, so we don't know what the outcome "should" have been.

If we are in a simulation, then quantum mechanics could very well simply be the simulators attempt at an information barrier to prevent "obvious" patterns that would make it easy to discern that we're in a simulator, as well as a barrier that provides a lower limit for what the simulator needs to be able to simulate.

But there is another problem with this argument:

> So if the system is modeling all the butterflies, you have a simulation cost problem. If it not modeling the butterflies, than the system does not truly implement 'the weather', but a simpler system, in which point it would have been easier to simulate a simpler system with less noise.

This argument rests on the assumption that a "simpler system" that would be "easier to simulate" would have less noise. That's a shaky assumption in itself.

But we tend to interpret "clean" data as artificial, and simple mechanisms also makes it easier to spot inconsistencies. If we are the way we are because we are simulations of entities outside "our" universe, then it would be clear that if said entities don't want us to realize we're in a simulation, then we would want to create simulations that look noisy.

At the same time, if the intent is to simulate the world the "outside" entities live in, then there is no way to avoid noise if these systems are noisy in their world.

Luckily that does not mean the noise needs to be perfect, and apparent/pseudo-random "noise" is cheap and easy to generate from very small functions. We're getting pretty good at using noise to generate terrain simulations that are looking increasingly plausible from small inputs, for example. It is in fact a significantly simpler approach vs. trying to model everything perfectly, because noise can hide a lot of imperfections and generate very complex-looking features from deceptively simple inputs.

And again what you need to remember is that we don't have a basis for comparison. We don't have pictures of the "outside world" to compare to in order to look for flaws in the simulation. If we are in a simulation, then for what we know the simulation is really awful and full of flaws, but close enough for the purposes of those running it.

> Lets look at the example of past data. Quite a lot of it is not by "players" (lets assume humans are the "players" here). Tree ring data, geological formations... How does our simulation deal with that?

You're missing:

F) Use an approach at least as old as Elite: Layered generation (Elite uses a pseudo rng, but it doesn't need to be), and generate values dependent on the higher level simulation to "drill down" to generate the state at any given point in time. You can make this just as consistent as you want, because at each level if you want you can use contiguous functions that makes it appear like reasonable causal chains. If you have to mix in "player aberrations" you need to actually simulate those steps and save the results, which is why you then want to keep track of a timeline for how rapidly you can perturb out the player aberrations), but everything else can be thrown away and recalculated depending on what is cost effective.

You want geological formations? Use a generator function that returns the placements of tectonic plates and other geological features at time t, and a given position. This is no different from how e.g. Minecraft generates terrains other than adding a time parameter to allow for things like tectonic shift and erosion and the like to be layered into the generator.

You want tree rings? Look up the landscape data for the current chunk, run a generator function that returns the trees in that chunk, and their data, look up the tree the player has felled, record the aberration and generate the data for the felled tree. Plenty of game engines already e.g. gives you different amounts of resources for felling trees of different size; this is not a hard problem. And increasingly the believability of environmental simulators is a multi-billion dollar business already today. Now keep in mind that for the purposes of the simulation argument, for the odds to be heavily in favour of a simulation only at least two simulations of this place and time needs to be run before the heath death of the universe. That's a lot of time.

But a lot of the perceived complexity of that also rests on the assumption of a world roughly like how we perceive it: billions of people etc. But without knowing how many "players" there are vs "NPCs", it's impossible to estimate the complexity of the simulation. Most of us only ever see a tiny part of the world, and meet a tiny number of people, and interact deeply with a much smaller number of people. If it's all a simulation, you don't know how much it is actually simulating.

Maybe you're alone in here. Maybe I am. Maybe you didn't exist before you read this comment. Maybe you won't exist after you read it.