The Median Voter Theorem (MVT) is a cornerstone of modern political science theory. It states that in a single peaked preference profile, the top choice of the median voter is the Condorcet winner.

Multi-armed bandit (MAB) is a popular framework for sequential decision-making in an uncertain environment.

In reinforcement learning, value-function methods such as Q-learning and SARSA(0) with $\epsilon$-greedy exploration are among the state of the art, and their tabular (exact) forms converge to the optimal Q-function under reasonable conditions.

Multi-armed bandits are sequential decision-making problems represented as a collection of probability distributions that one can sample from at every time step.

In a distributed storage system, due to increase of storage capacity of a node, efficient repair of failed nodes is becoming increasingly important in addition to ensuring a given level of reliability and low storage overhead.