[tnecniv]

What you’re basically describing is bounded rationality, which has been widely studied in behavioral economics, psychology, and engineering applications (Simon and Gigerenzer are two big names to google). A common framework for formalizing it is as what boils down versions of rate-distortion problems from information theory (very related to Bayesian statistics).

The reason it’s of engineering interest is, like you observe, bounded-rationality gives you solutions that are sub-optimal but more robust and often simpler.

Moreover, finding wide path solutions emerges naturally from sampling-based motion planners. These planners are asymptotically optimal, but if you terminate them early, they are more likely to give you a solution that goes through large gaps, not smaller ones, because it’s unlikely to sample a trajectory that goes through a tight space without heavy sampling. You could probably formulate that in the rate-distortion framework but I haven’t thought about how to do it precisely.

[CobrastanJorji]

Oh cool! It's always hugely useful to learn the word for the thing you're thinking about. It can be really tricky to figure out if a vague idea has a name unless you're already pretty well read in a field. Now I have some reading to do, thanks!

[tnecniv]

This is actually a big issue with academic research related to bounded rationality. Although you could model it mathematically in another way, by far the most common is to use the rate-distortion approach. Rate distortion theory basically boils down to analyzing optimization problems of the form “minimize cost + (information-theoretic) entropy”. Problems of that form arise and are used for different reasons in fields including, e.g.: statistical mechanics, Bayesian statistics, anything in machine learning using softmax, large deviation theory, differential privacy, and, of course, bounded rationality and information theory.

However, since all these fields refer the same thing by different names, tools for handling problems in one field don’t get picked up by people working in another field. Either someone else rediscovers it later or someone has to have knowledge of multiple fields and see a connection. Sometimes the analysis done by one field isn’t useful in another due to different assumptions and research concerns, but that’s not obvious because you have to peel back a lot of layers of domain-specific jargon when reading the paper. Even though the math is very similar, reading a statistical mechanics paper written by a physicist is a real pain if you’re coming from an applied math / CS background, for example, because fields have their own notational conventions and refer to application scenarios that are meaningless to you and you need to figure out if that thing they reference is important to their development or not in the abstract.

It’s almost like reading House of Leaves. Here’s 30 pages with weird fonts describing the use of light in a non-existent movie and comparing it to both real movies and fake movies real people were supposedly involved in. Will it be relevant to the plot and thus require careful reading or can I skim this section? Maybe, but you won’t know unless you keep reading.

[yamrzou]

This is where ChatGPT usually shines. I gave it your comment and asked it to find the concept. I had to nudge it in the right direction though: https://chat.openai.com/share/f8855a35-7076-43e6-bf9e-19de8b...