You write, “there have been probably thousands of papers written about ‘bandits’ and it would just confuse the literature to switch to another name.”

If we’re just talking about math problems, then, fine, the name can be whatever. My problem, though, is with applications. My concern is that most of these thousands of papers are solving various math problems that are irrelevant to real applied concerns.

Here’s what often happens in the statistical literature: People start with a real applied problem. Then they abstract a relevant math problem and solve it. So far, so good. But then you’ll get thousands of papers elaborating the math problem, yielding lots of solutions that are irrelevant or even counterproductive to the applied problem that they’re supposed to be solving. Names can make a difference in that they can give misleading impressions of what are the problems that we should be working on.

]]>I don’t like the term “MAP estimator” because it’s presented as an “estimator” rather than as a summary of the posterior distribution. If it were called “MAP summary,” I’d be ok with it.

]]>Andrew’s also told me he finds “MAP” too jargony. I don’t get this one, because every technical term is jargon by definition. It’s not like “posterior mode” is less jargony or even less Latinate. Maybe it’s that “A” which should be an “a”?

I get why Andrew and Jennifer wrote a long explanation in their regression book about the term “random effects” being too imprecise. I get this one. And it’s easy to say X-level effects for the relevant X, as in patient-level effects or individual education-level effects, etc. I just have to run an ongoing translation in my brain from the common language of (non-)linear mixed-effects models (the acronym source for NONMEM and lme4).

]]>Example with citations to older lit: https://www.journals.uchicago.edu/doi/10.1086/599296

So technically, in very special cases, it makes sense to play even if all “bandits” have negative expected value alone. There must be an economics lit observing the same result but using different terms. Portfolio theory maybe.

In any event, I think that all of this reinforces your point about the badness of the “bandit” metaphor.

]]>But if each machine/arm is a part of a genome with fixed values in certain loci, then each one selects from all the others’ ranges. If you’re homing in on a good solution for one, hopefully it will improve the reward from another.

“This all presupposes that either (a) you’re required to play”

If the game is life, well… I suppose one isn’t actually required to play, but those that don’t feel the obligation won’t be part of the phenomenon under study for long – or we won’t ever notice them!

Each pull of the arm of a bandit is a kind of dice throw, but combining randomness with an overall long-term characteristic outcome.

” (b) at least one of the machines has positive expected value”

The winning one usually does!

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