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Oct, 2019
从黑匣子决策中提取激励
Extracting Incentives from Black-Box Decisions
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Yonadav Shavit, William S. Moses
TL;DR
本文提出了一种理解算法激励作用的数学框架,将其视为解决马尔可夫决策过程的挑战,并借助求解MDP的工具包(如树形规划、强化学习)来识别每个人在给定模型下受到激励的最佳动作, 并通过两个真实世界环境下的实例展示了该方法的实用性。
Abstract
An
algorithmic decision-maker
incentivizes people to act in certain ways to receive better decisions. These
incentives
can dramatically influence subjects' behaviors and lives, and it is important that both decis
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