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I liked the first few slides, reminded me of OODA loop models. But the rest.. I mean I get the connection between for eg knapsack problem and inventory style games, but it doesn’t get at what makes particular knapsacks interesting for eg. But perhaps that’s too domain specific?
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Like I’m aware of a result that Tetris with only z and S pieces is np hard so Tetris tuning knob becomes percentage of those pieces. But are there principles for coming up with those 6 tiles (s, z, L, J, I, T, o) as a great game set?
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In the case of Tetris, it’s literally every permutation of four units. Five would be too many, three is trivial. And board size matters too. So yeah, there’s a significant tuning factor even once you identify the problem, defining the extent of the problem.
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Yup. The real trick, I think, lies in coming up with the relationships. Suits/numbers axis (poker). Four units and 2D topology (Tetris). Pathing problems and permutations (Tsuro). Arcs and fixed distance barriers (flappy bird)
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I have several down those lines, and you may want to look into the similar work of Dan Cook, perhaps starting with “Chemistry of Game Design.” There is also a bunch of good work by Joris Dormans on a systems driven modeling language
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