Online Prediction with Selfish Experts
Online Prediction with Selfish Experts
复制标题
与自私专家的在线预测
DOI：
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
Okke Schrijvers
中科院分区：
文献类型：
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作者：
Tim Roughgarden;Okke Schrijvers
我们在专家具有代理权的环境中考虑专家建议的二元预测问题，并寻求最大化他们的可信度。本文主要有三个贡献。首先，它定义了一个模型的原因正式与自私的专家设置，并证明了“激励兼容”（IC）算法是密切相关的设计适当的评分规则。其次，我们设计了具有良好性能保证的绝对损失函数的IC算法。第三，我们给出了一个正式的分离与自私的专家在线预测和诚实的专家在线预测的能力证明IC和非IC算法的下界。特别是，自私的专家和绝对损失函数，没有（随机）算法在线预测-IC或其他--与渐近消失的遗憾。
We consider the problem of binary prediction with expert advice in settings where experts have agency and seek to maximize their credibility. This paper makes three main contributions. First, it defines a model to reason formally about settings with selfish experts, and demonstrates that ``incentive compatible'' (IC) algorithms are closely related to the design of proper scoring rules. Second, we design IC algorithms with good performance guarantees for the absolute loss function. Third, we give a formal separation between the power of online prediction with selfish experts and online prediction with honest experts by proving lower bounds for both IC and non-IC algorithms. In particular, with selfish experts and the absolute loss function, there is no (randomized) algorithm for online prediction---IC or otherwise---with asymptotically vanishing regret.
DOI：
10.1145/2020408.2020495
发表时间：
2011-08
期刊：
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影响因子：
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作者：
Michael Brückner;T. Scheffer
通讯作者：
Michael Brückner;T. Scheffer