An incentive-based approach to distributed estimation with strategic sensors

Dobakhshari, Donya G.; Li, Na; Gupta, Vijay

doi:10.1109/cdc.2016.7799213

Cited by 13 publications

(16 citation statements)

References 8 publications

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“…We begin our analysis by determining under what conditions the data sources will accept the collection of contracts offered to them by the data buyers. Recall that data source q will accept all of the contracts offered by the data sources if and only if the ex-ante total payments are non-negative (4) and each data buyer's payment is non-negative ex-ante (5).…”

Section: Resultsmentioning

confidence: 99%

Statistical estimation with strategic data sources in competitive settings

Westenbroek

Dong

Ratliff

et al. 2017

2017 IEEE 56th Annual Conference on Decision and Control (CDC)

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In this paper, we introduce a preliminary model for interactions in the data market. Recent research has shown ways in which a data aggregator can design mechanisms for users to ensure the quality of data, even in situations where the users are effort-averse (i.e. prefer to submit lower-quality estimates) and the data aggregator cannot observe the effort exerted by the users (i.e. the contract suffers from the principalagent problem). However, we have shown that these mechanisms often break down in more realistic models, where multiple data aggregators are in competition. Under minor assumptions on the properties of the statistical estimators in use by data aggregators, we show that there is either no Nash equilibrium, or there is an infinite number of Nash equilibrium. In the latter case, there is a fundamental ambiguity in who bears the burden of incentivizing different data sources. We are also able to calculate the price of anarchy, which measures how much social welfare is lost between the Nash equilibrium and the social optimum, i.e. between non-cooperative strategic play and cooperation.

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Section: Resultsmentioning

confidence: 99%

Statistical estimation with strategic data sources in competitive settings

Westenbroek

Dong

Ratliff

et al. 2017

2017 IEEE 56th Annual Conference on Decision and Control (CDC)

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“…Some of the existing work studies how to incentivize strategic data sources in a static setting, where there is no underlying dynamical system, and the data collectors are typically trying to estimate some underlying fixed function. In [2], an optimal contract is presented that minimizes the total payment of the estimator while guaranteeing strategic sensors to put in sufficient effort and truthfully report the estimate, by assuming one of the sensors is loyal, i.e. reporting true information.…”

Section: Introductionmentioning

confidence: 99%

Incentivizing Truthful Reporting from Strategic Sensors in Dynamical Systems

Zhao,

Dong

2020

Preprint

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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 effortaverse 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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“…The problem of strategic communication between a receiver and a better informed sender, known as the cheap-talk game, has been studied in the economics literature (Crawford and Sobel, 1982;Battaglini, 2002). This problem has more recently attracted attention in the engineering community, where it has application in privacyconstrained communication and cyber-security (Farokhi et al, 2015(Farokhi et al, , 2017 and estimation (Dobakhshari et al, 2016;Westenbroek et al, 2017). These studies mostly consider static estimation problems.…”

Section: Introductionmentioning

confidence: 99%

Feedback control using a strategic sensor

Farokhi

2019

International Journal of Control

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A dynamic estimation and control problem with a strategic sensor is considered. The strategic sensor may provide corrupted messages about the state measurements of a discrete-time linear time-invariant dynamical system to the system operator (or the controller). The system operator then uses this information to construct an estimate of the state of the system (and perhaps private variables of the sensor). The estimate is used to control the system to achieve the operator's desired objective. The problem is formulated as a game, which might be conflicting to that of the strategic sensor. An equilibrium of the game is computed and its properties are investigated.

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An incentive-based approach to distributed estimation with strategic sensors

Cited by 13 publications

References 8 publications

Statistical estimation with strategic data sources in competitive settings

Statistical estimation with strategic data sources in competitive settings

Incentivizing Truthful Reporting from Strategic Sensors in Dynamical Systems

Feedback control using a strategic sensor

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