The celebrated Vickrey-Clarke-Grove(VCG) mechanism induces selfish agents to behave truthfully by paying them a premium. In the process, it may end up paying more than the actual cost to the agents. For the minimum spanning tree problem, if the market is competitive, one can show that VCG never pays too much. On the other hand, for the shortest s-t path problem, Archer and Tardos [5] showed that VCG can overpay by a factor of Ω(n). A natural question that arises then is: For what problems does VCG overpay by a lot? We quantify this notion of overpayment, and show that the class of instances for which VCG never overpays is a natural generalization of matroids, that we call frugoids. We then give some sufficient conditions to upper bound and lower bound the overpayment in other cases, and apply these to several important combinatorial problems. We also relate the overpayment in an suitable model to the locality ratio of a natural local search procedure.

The price of truth: Frugality in truthful mechanisms

We study the problem of finding a recommendation for an uninformed user in a social network by weighting and aggregating the opinions offered by the informed users in the network. In social networks, an informed user may try to manipulate the recommendation by performing a false-name manipulation, wherein the user submits multiple opinions through fake accounts. To that end, we impose a no harm axiom: false-name manipulations by a user should not reduce the weight of other users in the network. We show that this axiom has deep connections to false-name-proofness. While it is impossible to design a mechanism that is best for every network subject to this axiom, we propose an intuitive mechanism LEGIT+, and show that it is uniquely optimized for small networks. Using real-world datasets, we show that our mechanism performs very well compared to two baseline mechanisms in a number of metrics, even on large networks.

False-Name-Proof Recommendations in Social Networks.

Recently, machine learning has become popular in various fields like healthcare, smart transportation, network, and big data. However, the labelled training dataset, which is one of the most core of machine learning, cannot meet the requirements of quantity, quality, and diversity due to the limitation of data sources. Crowdsourcing systems based on mobile computing seem to address the bottlenecks faced by machine learning due to their unique advantages; i.e., crowdsourcing can make professional and nonprofessional participate in the collection and annotation process, which can greatly improve the quantity of the training dataset. Additionally, distributed blockchain technology can be embedded into crowdsourcing systems to make it transparent, secure, traceable, and decentralized. Moreover, truth discovery algorithm can improve the accuracy of annotation. Reasonable incentive mechanism will attract many workers to provide plenty of dataset. In this paper, we review studies applying mobile crowdsourcing to training dataset collection and annotation. In addition, after reviewing researches on blockchain or incentive mechanism, we propose a new possible combination of machine learning and crowdsourcing systems.

https://downloads.hindawi.com/journals/wcmc/2022/7033626.pdf

Blockchain-Based Crowdsourcing Makes Training Dataset of Machine Learning No Longer Be in Short Supply

A large number of problems in business and consumer analytics have input graphs or networks. These mathematical entities have a long standing tradition in discrete applied mathematics and computer science. In many cases, they are the most natural means to represent some type of relationships in data. Consequently, a large number of solution methods based on heuristics and exact algorithms exist for problems that have graphs and/or networks as part of their input. While the number of possible applications of these techniques is not limited to problems in business and customer analytics, we have chosen to present some of them in a survey that would allow newcomers to the field of data science to create some familiarity with the key questions that motivate the area. We have also provided a survey on recent applications and new algorithmic approaches for data analytics. In addition we discuss issues related to the computational complexity of some problems associated with them. Other chapters of this section complement the discussion in this chapter with specific examples of interest or that could motivate new novel research direction and application.

Business Network Analytics: From Graphs to Supernetworks.

Crowdsourcing is an effective tool to allocate tasks among workers to obtain a cumulative outcome. Algorithmic game theory is widely used as a powerful tool to ensure the service quality of a crowdsourcing campaign. By this paper, we consider a more general optimization objective for the budget-free crowdsourcer, profit maximization, where profit is defined as the difference between the benefit obtained by crowdsourcer and payments to workers. Based on the framework of random sampling and profit extraction, we proposed a strategy-proof profit-oriented mechanism for our problem, which also satisfies computational tractability and individual rationality and has a performance guarantee. We also extend the profit extract algorithm to the online case through a two-stage sampling. Also, we study the setting in which workers are not trusted, and untrustworthy workers would infer others’ true type. For untrustworthy workers, we introduce a differentially private mechanism, which also has desired properties. Finally, we will conduct numerical simulations to show the effectiveness of our proposed profit maximization mechanisms. By this work, we enrich the class of competitive auctions by considering a more general optimization objective and a more general demand valuation function.

/pdf/truthful-profit-maximization-mechanisms-for-mobile-27tcpw4x.pdf

Truthful Profit Maximization Mechanisms for Mobile Crowdsourcing

Through past decades, there has been extensive research on algorithmic game theory techniques for solving multicast routing problem in the networks: if one can incentivize a subset of edges to connect all base stations in the networks, where edges are owned by strategic agents, which subset should be selected so that the total cost of edges is minimized? To solve this problem, the celebrated Vickrey-Clarke-Groves(VCG) mechanism is applied, which pays a premium to incentivize the edges to reveal their cost truthfully. However, as discovered in this paper, VCG mechanism for network construction may suffer from a new cheating pattern, named false-name bidding, where bidder can gain profit by submitting bids under multiple fictitious name. Such cheating can undermine efficiency and increase the expenditure of the auctioneer. To overcome this hurdle, we propose the core-selecting multicast routing mechanism. The number of the core constraints are too large for computing and we reduce the number of core constraints through several ways to make the computation possible and apply Vickrey-nearest pricing rule for incentivizing the bidders. Experiments are conducted on real-life networks to show that the payment of our mechanism is lower than the payment of VCG mechanism.

A False-Name-Proof Protocol for Multicast Routing Auctions

Crowdsourcing is a new paradigm which divides work between participants to achieve a cumulative result. To achieve good service quality for a crowdsourcing system, incentive mechanisms are necessary to attract more user participation. Most of existing mechanisms apply only for the crowdsourcing scenario where the platform user will employ the workers to perform certain tasks to maximize one's utility with budget constraint. However, the budget is not fixed in practice, and the final goal for the platform user is to achieve profit maximization. In this paper, we consider a more general optimization objective for the budget-free platform user, profit maximizing, i.e., the difference between her utility and the total reward to the participants. We study the problem of how to maximize the profit in a crowdsourcing activity where the platform user's proceeds is a symmetric submodular demand valuation function and the users' cost information are prior-free. Based on the framework of random sampling and profit extraction, we propose a mechanism which is computationally tractable, truthful, individually rational and constant-factor competitive to the optimal profit omniscient single-price auction in a fixed market. We also extend the profit extract algorithm to the online case. By this work, we enrich the class of competitive auctions by considering a more general optimization objective and a more general demand valuation function in both the offline and online platform for the crowdsourcing system.

A Truthful Profit-Oriented Mechanism for Mobile Crowdsensing

Cellular traffic offloading is nowadays an important problem in mobile networking. Since the offloading resource owners (agents) are self-interested and have private costs, it is highly challenging to design procurement mechanisms that motivate agents to reveal their true costs and achieve guaranteed performance under the constraint of a strict budget. In this paper, we model cellular traffic offloading as a multi-unit budget feasible procurement auction design problem with diminishing return valuations. We design a novel greedy-based randomized mechanism, and prove it is budget-feasible, truthful, individually rational and a $(3+2ln N)$-approximation, where N is the total number of available resource units. We also propose a deterministic mechanism which achieves $(2+ln N + \sqrt2+3ln N +ln^2N )$ - approximation. We prove no budget-feasible and truthful mechanism can do better than $ln N$-approximation in our setting, thus our mechanism approaches the optimal to a constant factor. In addition to solving the cellular traffic offloading problem, our work successfully extends solvable valuation class of greedy-based multi-unit budget-feasible mechanism with performance guarantees from the concave-additive valuations to more general local diminishing return valuations.

Multi-unit Budget Feasible Mechanisms for Cellular Traffic Offloading

Cellular traffic offloading is nowadays an important problem in mobile networking. We model it as a procurement problem where each agent sells multi-units of a homogeneous item with privately known capacity and unit cost, and the auctioneer's demand valuation function is symmetric submodular. Based on the framework of random sampling and profit extraction, we aim to design a prior-free mechanism which guarantees a profit competitive to the omniscient single-price auction. However, the symmetric submodular demand valuation function and 2-parameter setting present new challenges. By adopting the highest feasible clear price, we successfully design a truthful profit extractor, and then we propose a mechanism which is proved to be truthful, individually rational and constant-factor competitive in a fixed market.

/pdf/a-multi-unit-profit-competitive-mechanism-for-cellular-55bt2wg0bq.pdf

Yu Qiao

Papers

A False-Name-Proof Protocol for Multicast Routing Auctions

A Truthful Profit-Oriented Mechanism for Mobile Crowdsensing

Multi-unit Budget Feasible Mechanisms for Cellular Traffic Offloading

Truthful Profit Maximization Mechanisms for Mobile Crowdsourcing

A Multi-Unit Profit Competitive Mechanism for Cellular Traffic Offloading