Multitasking, Multiarmed Bandits, and the Italian Judiciary
نویسندگان
چکیده
We model how a judge schedules cases as a multi-armed bandit problem. The model indicates that a first-in-first-out (FIFO) scheduling policy is optimal when the case completion hazard rate function is monotonic. But there are two ways to implement FIFO in this context: at the hearing level or at the case level. Our model indicates that the former policy, prioritizing the oldest hearing, is optimal when the case completion hazard rate function decreases, and the latter policy, prioritizing the oldest case, is optimal when the case completion hazard rate function increases. This result convinced six judges of the Roman Labor Court of Appeals—a court that exhibits increasing hazard rates—to switch from hearing-level FIFO to case-level FIFO. Tracking these judges for eight years, we estimate that our intervention decreased the average case duration by 12% and the probability of a decision being appealed to the Italian supreme court by 3.8%, relative to a 44-judge control sample.
منابع مشابه
Evaluation and Analysis of the Performance of the EXP3 Algorithm in Stochastic Environments
EXP3 is a popular algorithm for adversarial multiarmed bandits, suggested and analyzed in this setting by Auer et al. [2002b]. Recently there was an increased interest in the performance of this algorithm in the stochastic setting, due to its new applications to stochastic multiarmed bandits with side information [Seldin et al., 2011] and to multiarmed bandits in the mixed stochastic-adversaria...
متن کاملPAC-Bayesian Analysis of Contextual Bandits
We derive an instantaneous (per-round) data-dependent regret bound for stochastic multiarmed bandits with side information (also known as contextual bandits). The scaling of our regret bound with the number of states (contexts) N goes as
متن کاملMatroid Bandits: Practical Large-Scale Combinatorial Bandits
A matroid is a notion of independence that is closely related to computational efficiency in combinatorial optimization. In this work, we bring together the ideas of matroids and multiarmed bandits, and propose a new class of stochastic combinatorial bandits, matroid bandits. A key characteristic of this class is that matroid bandits can be solved both computationally and sample efficiently. We...
متن کاملMultiarmed Bandits With Limited Expert Advice
We consider the problem of minimizing regret in the setting of advice-efficient multiarmed bandits with expert advice. We give an algorithm for the setting of K arms and N experts out of which we are allowed to query and use only M experts’ advice in each round, which has a regret bound of Õ (√ min{K,M}N M T ) after T rounds. We also prove that any algorithm for this problem must have expected ...
متن کاملOpen Problem: Advice-Efficient Adversarial Multiarmed Bandits with Expert Advice
Adversarial multiarmed bandits with expert advice is one of the fundamental problems in studying the exploration-exploitation trade-off. It is known that if we observe the advice of all experts on every round we can achieve O (√ KT lnN ) regret, where K is the number of arms, T is the number of game rounds, and N is the number of experts. It is also known that if we observe the advice of just o...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
- Manufacturing & Service Operations Management
دوره 18 شماره
صفحات -
تاریخ انتشار 2016