Arlotto and Gurvich (2019) showed that the regret in the multisecretary problem is bounded in the number of job openings, n, and the number of applicants, k, provided that the applicant valuations are drawn from a distribution with finite support. I show that this result does not hold when...

Rust (1997) discovered a class of dynamic programs that can be solved in polynomial time with a randomized algorithm. Insulated from the curse of dimensionality, this walled garden of tractable dynamic problems is intriguing, if not useful. Unfortunately, I find that the class is more limited...

The bullwhip effect and production smoothing appear antithetical because their empirical tests oppose one another: production variability exceeding sales variability for bullwhip, and vice versa for smoothing. But this is a false dichotomy. We distinguish between the phenomena with a new...

I present two algorithms for solving dynamic programs with exogenous variables: endogenous value iteration and endogenous policy iteration. These algorithms are like relative value iteration and relative policy iteration, except they discard the variation in the value function due solely to the...

We estimate the effect of supply chain proximity on product quality. Merging four automotive datasets, we create a supply chain sample that reports the failure rate of 27,807 auto components, the location of 529 upstream component factories, and the location of 275 downstream assembly plants. We...

Morton and Wecker (1977) stated that the value iteration algorithm solves a dynamic program's policy function faster than its value function when the limiting Markov chain is ergodic. I show that their proof is incomplete, and provide a new proof of this classic result. I use this result to...

We model a single-supplier, 73-store supply chain as a dynamic discrete choice problem. We estimate the model with transaction-level data, spanning 3,251 products and 1,370 days. We find two interrelated phenomena: the bullwhip effect and ration gaming. To establish the bullwhip effect, we show...