This paper on “Mastering uncertainty: A predictive processing account of having fun with unsure success in online game play” may be very value a learn in case you are within the frontiers of determining what “enjoyable” is. Fortunately for me, it doesn’t say I’ve been on the flawed observe for many years.
It does elevate fascinating questions given its framework — I’d like to see slot machines defined — although there may be some stuff on have an effect on that doubtless ties in. It additionally teases out a few of why I’ve by no means felt snug with the “circulate = enjoyable” equation.
One other fascinating intersection with different materials can be motivations (a la Bartle/Quantic Foundry) and private goal-setting. Gamers DO grind, in any case, as they optimize, and tho the paper mentions individuals don’t get caught in “popping bubble wrap,” they do for lots longer than one would count on.
For me the reply to that ties again to the lemma/heuristic mannequin of pruning chance that’s often mentioned within the context of “what’s recreation depth.” I’ve come to see ahead technique and notion of depth as being about indeterminacy and a way of “victory parity” tilting forwards and backwards as we mission. There’s one thing to tease out in that plus motivations plus this paper that may very well be helpful in excited about assemble recreation metas specifically.
Anyway, I encourage the learn. It ties properly to different work equivalent to OpenAI’s RND.
One of many niftiest components of my profession has been seeing items of my work flip up as constructing blocks for others (equivalent to AI programs attempting to mathematically implement Principle of Enjoyable). At all times feels good when your stuff is constructed on. Nothing will get to remain on the pinnacle for very lengthy, however attending to be a chunk of basis is a fairly cool and so much higher than the choice. 😉
Dan Cook dinner additionally has ideas on this paper which are value a learn, and open but extra rabbit holes to discover.