激励动力系统中战略传感器的真实报告
Incentivizing Truthful Reporting from Strategic Sensors in Dynamical Systems
摘要：
在动力系统的背景下，人类代理越来越多地充当数据源。与传统传感器不同，人类可能出于自私的原因操纵或忽略数据。因此，本文研究了努力厌恶策略传感器对离散时间LTI系统的影响。在我们的设置中，传感器付出昂贵的努力来收集数据，并将其工作报告给系统操作员。然而，传感器并不直接受益于系统的输出，因此它们不会付出太多努力来确保准确性，甚至可能伪造其报告的努力以最大化其效用。我们探索激励战略传感器如实报告的支付机制。我们证明了真实的和报告的努力对预期的运营成本的影响。然后，我们使用系统成本的实现来构造支付函数。我们表明，通常在静态设置中使用的支付功能将无法引起真实的报告一般，并提出了一个修改后的支付功能，eleclantly真实的报告，这需要的条款，补偿的动态影响的报告的努力对系统的闭环性能。
英文摘要：
Human agents are increasingly serving as data sources in the context of dynamical systems. Unlike traditional sensors, humans may manipulate or omit data for selfish reasons. Therefore, this paper studies the influence of effort-averse strategic sensors on discrete-time LTI systems. In our setting, sensors exert costly effort to collect data, and report their effort to the system operator. However, sensors do not directly benefit from the output of the system, so they will not exert much effort to ensure accuracy and may even falsify their reported effort to maximize their utility. We explore payment mechanisms that incentivize truthful reporting from strategic sensors. We demonstrate the influence of the true and reported effort on the expected operational cost. Then, we use the realizations of the system cost to construct a payment function. We show that payment functions typically used in static settings will not be able to elicit truthful reports in general, and present a modified payment function that elicits truthful reporting, which requires terms that compensate for the dynamic impact of reported efforts on the closed-loop performance of the system.
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