Tikhon Jelvis – Reasoning under Uncertainty

Tikhon Jelvis explains how to model and solve Markov decision processes using Haskell to make optimal decisions under uncertainty.

Tikhon Jelvis I like programming languages. A lot. Especially Haskell. I’m a member of the Haskell.org committee, I’ve used Haskell professionally and I’ve written about it extensively on both Quora and Stack Overflow. I developed an interest in stochastic control after spending several years on retail inventory optimization and simulation before switching focus to demand forecasting. Reasoning under Uncertainty How can we make optimal decisions when outcomes aren’t deterministic? Traditional optimization techniques can’t apply directly because they don’t address uncertainty. Instead, we need a framework for specifying the random behaviors of a system, and algorithms that can handle randomness. Markov decision processes offer such a framework and come with a suite of optimization algorithms. Haskell is a great learning tool for making mathematical abstractions more concrete, so this talk will teach Markov decision processes by designing a Haskell library for working with them. haskell.love twitter.com/_haskellove