I’ve been wondering for several years how selection functions and lenses relate to each other, I felt intuitively that there should be some connection – and not just because they both show up in the foundations of game theory. Last night I came up with an answer, which isn’t a complete answer but looks like the starting point for a complete answer.Continue reading “Selection functions and lenses”
I’m starting to worry that my open games implementation is getting ahead of what I’ve written in papers in a few ways, and I should correct that with documentation blog posts. This one is about the module OpenGames.Engine.StatefulPayoffs, which is a pragmatic solution to a fundamental conceptual problem with open games: the identity of agents is not well-defined. Rather the fundamental unit is a decision, and if two decisions are made by the same agent then it is the user’s responsibility to make sure that those decisions have the same payoff, or at least game-theoretically “coherent” payoffs.
For a long time I thought this was a conceptual problem but not a practical one. But recent work with my collaborators Philipp Zahn, Seth Frey and Joshua Tan has stress-tested open games in new ways and revealed it to a problem after all. Specifically, if one agent makes 2 decisions on different sides of an abstraction boundary, then the programmer must explicitly design the boundary to accommodate that agent’s payoff. This feels like an abstraction leak.Continue reading “Open games with stateful payoffs”
I’ve been threatening a few times recently to blog about bits of mathematical folklore that I use, i.e. important things that aren’t easy to find in the literature. I’m going to start with an easy one that won’t take me long to write.
Theorem: Given a commutative strong monad on a symmetric monoidal category, the kleisli category is symmetric monoidal in a canonical way.
(Recall that monads are not a good topic for your first blog post.)
I intend to bootstrap a blog by writing about 2 of my old papers, Monad Transformers for Backtracking Search and The Selection Monad as a CPS Transformation. (Wall Street will be spared for the time being.)
I’m going to write about this little bit of Haskell code:
import Control.Monad.Cont sat n = runCont $ sequence $ replicate n $ cont $ \k -> k $ k True
It’s a SAT solver: you give it a boolean function, and the number of variables to search, and it decides whether that function will ever return true for any values of those variables.
How does this work? Haven’t the foggiest. If anyone can explain what it does at runtime, there’s probably a research paper in it for you. If you can also predict how long it takes, that’s a big deal.