It'd be particularly useful to have a chess bot that can play badly in the same way a human does.

The problem with the current chess bots is that they play badly, badly. They choose a terrible random mistake to make every few moves, while some of their other moves are brilliant. They cannot accurately mimic beginner or intermediate level players.

This seems like something DeepMind could create, given the incentive. They were able to train AlphaGo to predict human moves in Go at a very high accuracy (obviously not with AlphaGo Zero, but the inferior human-predictive version is how they determined that AGZ is playing qualitatively differently).

In a sense, that would be like replicating the human brain's functionality, including the bugs and limitations.

I have some idea how it MIGHT work, but it would be a very boring solution involving 'learning' Stockfish's parameters and HOPING to find improvements to something like integrating time management and search/pruning into it.

I wouldn't bet on it though. SMP is notoriously hard to work with alpha-beta search and there are a lot of clever tricks (which is probably still not perfect). Maybe with ASICs, you could make it stronger, but then it wouldn't be as fair a comparison.

Well, all top engines did some kind of search on parameters, not sure if you can find much improvement there.

I'm talking about something similar to the described in the paper, 100% self-learned solution without using human heuristics, based on NNs. That could bring a totally new ideas into chess.