Using Incentives to Obtain Truthful Information

Faltings, Boi

doi:10.1007/978-3-642-29966-7_1

Cited by 3 publications

(3 citation statements)

References 11 publications

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“…In fact, ensured by the well-known revelation principle [12], it is without loss of generality to consider only truthful mechanisms. 2 This is also the reason why we can restrict attention to truthful mechanism design throughout the paper.…”

Section: A Mechanism Design Approachmentioning

confidence: 99%

“…Considering that in most e-commerce websites, this number is rather large, this is not an unrealistic assumption; in fact our guarantees will hold approximately with very small approximation error for any large number of discrete impressions. 2 The revelation principle states that any objective implementable in dominant strategies can be implemented by a truthful mechanism. Other commonly used names for truthfulness are incentivecompatibility or staregy-proofness.…”

Section: A Mechanism Design Approachmentioning

confidence: 99%

“…Mechanism design approaches have been employed in the past in recommendation systems, but most of them [5,7,8,2] are concerned with how to design reputation systems that incentivize buyers to report honest and constructive feedbacks rather than considering sellers as the selfish participants. As a notable exception, Zhang et al [22] consider both strategic buyers and sellers, but their model incorporates a social network-type graph and reputation systems for both buyers and sellers and is, in most regards, quite different from ours.…”

Section: A Mechanism Design Approachmentioning

confidence: 99%

See 2 more Smart Citations

Mechanism Design for Personalized Recommender Systems

Cai

Filos-Ratsikas

Liu

et al. 2016

Proceedings of the 10th ACM Conference on Recommender Systems

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Strategic behaviour from sellers on e-commerce websites, such as faking transactions and manipulating the recommendation scores through artificial reviews, have been among the most notorious obstacles that prevent websites from maximizing the efficiency of their recommendations. Previous approaches have focused almost exclusively on machine learning-related techniques to detect and penalize such behaviour. In this paper, we tackle the problem from a different perspective, using the approach of the field of mechanism design. We put forward a game model tailored for the setting at hand and aim to construct truthful mechanisms, i.e. mechanisms that do not provide incentives for dishonest reputation-augmenting actions, that guarantee good recommendations in the worst-case. For the setting with two agents, we propose a truthful mechanism that is optimal in terms of social efficiency. For the general case of m agents, we prove both lower and upper bound results on the effciency of truthful mechanisms and propose truthful mechanisms that yield significantly better results, when compared to an existing mechanism from a leading e-commerce site on real data.

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Section: A Mechanism Design Approachmentioning

confidence: 99%

Section: A Mechanism Design Approachmentioning

confidence: 99%

Section: A Mechanism Design Approachmentioning

confidence: 99%

See 1 more Smart Citation

Mechanism Design for Personalized Recommender Systems

Cai

Filos-Ratsikas

Liu

et al. 2016

Proceedings of the 10th ACM Conference on Recommender Systems

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Toward Self-monitoring Smart Cities: the OpenSense2 Approach

Calbimonte

Eberle

Aberer

2016

Informatik Spektrum

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The sustained growth of urban settlements in the last years has had an inherent impact on the environment and the quality of life of their inhabitants. In order to support sustainability and improve quality of life in this context, we advocate the fostering of ICT-empowered initiatives that allow citizens to self-monitor their environment and assess the quality of the resources in their surroundings. More concretely, we present the case of such a self-monitoring Smart City platform for estimating the air quality in urban environments at high resolution and large scale. Our approach is a combination of mobile and human sensing that exploits both dedicated and participatory monitoring. We identify the main challenges in such a crowdsensing scenario for Smart Cities, and in particular we analyze issues related to scalability, accuracy, accessibility, privacy, and discoverability, among others. Moreover, we show that our approach has the potential to empower citizens to diagnose their environment using mobile and portable sensing devices, combining their personal data with a public higher accuracy air quality network.

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Feedback Elicitation Mechanism Design

2019

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Peer-to-peer sharing economy such as Uber and Airbnb has played an essential role in the global economy. Different from the traditional rental economy, sharing economy does not guarantee standard services for its users and the service quality is largely monitored by the users via their evaluations/feedbacks of the service. However, in the existing sharing economy markets, users are not incentivized to offer their true feedbacks to the platforms, and they are often incentivized to misreport. Therefore, our goal is to design reward mechanisms that can incentivize the users to report they are true feedbacks. The existing work has considered the problem when the feedbacks are binary (e.g., good or bad), but the feedbacks in practice are usually n-ary. Therefore, we propose a novel framework to design reward mechanisms which incentivize users to report their feedbacks truthfully when the feedbacks are n-ary. This is achieved by designing a proper reward scheme such that reporting feedback truthfully is a Nash equilibrium given that the cost of the platform is minimized. We form the problem as a linear program, which takes incentivizing users to report truthfully as constraints and minimizes the cost to the platform. The linear program is solved via its dual problem. Under a proper condition, we get a special solution structure of the linear program. This special structure allows us to derive a general solution form which does not need to solve the linear program in a brute force manner and the computation complexity is significantly reduced.

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Using Incentives to Obtain Truthful Information

Cited by 3 publications

References 11 publications

Mechanism Design for Personalized Recommender Systems

Mechanism Design for Personalized Recommender Systems

Toward Self-monitoring Smart Cities: the OpenSense2 Approach

Feedback Elicitation Mechanism Design

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