The specific example didn't make me spin, but in general I think gaming perspectives, when we have not just a simply dice thrower, but interacting players that encode and update their own maps of the other players are very good and deep analogies, where one can find common mathematics. Such examples can I think be good ways to illustrate both symmetries, as well as the difference between descriptive and guiding probability concepts.
Its how I secrectly thinkg of things as well. But then it's interacting Qbist agents, that has to learn and surviva, or get outcompeted. The "stable" population of players could conceptually correspond to the population of elementary particles, and the interaction rules enoded in the relational expectation the agents has about each other. But the agents decisions does, just like QM does
not require conscious observers,
not require brains. The idea is that it's all about guided random processes (I mean, given any stance, it's natural to fall forwards; So the agents expectation of the future, determines its rambling direction - this is the meaning of probability in the qbist view as well, it reflects the agents prospensity for actions) And the quest is: How can we understand the emerge of players populating the game, that correspond to the groups and interactions of standard model (and gravity?)
Smolin had also interesting ideas sniffing along these lines, but still very immature.
https://arxiv.org/abs/1205.3707
But to make it really exciting one has to allow for the agents (or active bookkeepers in the xample) to learn and evolve. How this is going to be modeled requires further assumptions. I can't blame anyone for leaving out these details though, but it's what would have made me spin.
There is ALOT of bits and pieces everywhere, different researchers sniffing along these lines, but none I have seen has the big picture.
Edit: See also
Evolutionary game theory using agent-based methods
https://arxiv.org/pdf/1404.0994.pdf, its computational biology gets you in the mood
/Fredrik