Naturally solved problems that are easy to verify but that would be hard to compute
Some of the ensuing responses discussed the fidelity with which such a simulation would need to be run, in order to keep the population living within it guessing as to whether they were in a digital simulation, which is a topic that’s been discussed before on LessWrong:
If a simulation can be not just run, but also loaded from previous saved states then edited, it should be possible for the simulation’s Architect to start it running with low granularity, wait for some inhabitant to notice an anomaly, then rewind a little, use a more accurate but computing intensive algorithm in the relevant parts of the inhabitant’s timecone and edit the saved state to include that additional detail, before setting the simulation running again and waiting for the next anomaly.
construct a system with easy-to-verify but arbitrarily-hard-to-compute behavior (“Project: Piss Off God”), and then scrupulously observe its behavior. Then we could keep making it more expensive until we got to a system that really shouldn’t be practically computable in our universe.
but I’m wondering how easy that would be.
The problem would need to be physical (for example, make a net with labelled strands of differing lengths joining the nodes, then hang it from one corner), else humanity would have to be doing as much work as the simulation.
The solution should be discrete (for example, what are the labels on the strands making up the limiting path that prevents the lowest point from hanging further down)
The solution should be not just analytic, but also difficult to get via numerical analysis.
The problem should be scalable to very large sizes (so, for example, the net problem wouldn’t work, because with large size nets making the strands sufficiently different in length that you could tell two close solutions apart would be a limiting factor)
And, ideally, the problem would be one that occurs (and is solved) naturally, such that humanity could just record data in multiple locations over a period of years, then later decide which examples of the problem to verify. (See this paper by Scott Aaronson: “NP-complete Problems and Physical Reality”)