I am not sure I understood the OP though, when he said that Newcomb problems are "the norm."

Yeah, it's a bold claim :-) I haven't made any of the arguments yet, but I'm getting there.

(The rough version is that Newcomblike problems happen whenever knowledge about your decision theory leaks to other agents, and that this happens all the time in humans. Evolution has developed complex signaling tools, humans instinctively make split-second assessments of the trustworthiness of others, etc. In most real-world multi-agent scenarios, we implicitly expect that the other agents have some knowledge of how we make decisions, even if that's only a via knowledge of shared humanity. Any AI interacting with humans who have knowledge of its source code, even tangentially, faces similar difficulties. You could assume away the implications of this "leaked" knowledge, or artificially design scenarios in which this knowledge is unavailable. This is often quite useful as a simplifying assumption or a computational expedient, but it requires extra assumptions or extra work. By default, real-world decision problems on Earth are Newcomblike. Still a rough argument, I know, I'm working on filling it out and turning it into posts.)