Strategy-Proof and Non-Wasteful Multi-Unit Auction via Social Network

Auctions via social network, pioneered by Li et al. (2017), have been attracting considerable attention in the literature of mechanism design for auctions. However, no known mechanism has satisfied strategy-proofness, non-deficit, non-wastefulness, and individual rationality for the multi-unit unit-demand auction, except for some naïve ones. In this paper, we first propose a mechanism that satisfies all the above properties. We then make a comprehensive comparison with two naïve mechanisms, showing that the proposed mechanism dominates them in social surplus, seller's revenue, and incentive of buyers for truth-telling. We also analyze the characteristics of the social surplus and the revenue achieved by the proposed mechanism, including the constant approximability of the worst-case efficiency loss and the complexity of optimizing revenue from the seller's perspective.

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Stability, Strategy-Proofness, and Cumulative Offer Mechanisms

The Review of Economic Studies ◽

10.1093/restud/rdaa052 ◽

2020 ◽

Author(s):

John William Hatfield ◽

Scott Duke Kominers ◽

Alexander Westkamp

Keyword(s):

Choice Function ◽

The Other ◽

Choice Functions ◽

Strategy Proof ◽

Unit Demand ◽

Unique Mechanism ◽

Strategy Proofness

Abstract We characterize when a stable and strategy-proof mechanism is guaranteed to exist in the setting of many-to-one matching with contracts. We introduce three novel conditions—observable substitutability, observable size monotonicity, and non-manipulability via contractual terms—and show that when these conditions are satisfied, the cumulative offer mechanism is the unique mechanism that is stable and strategy-proof (for workers). Moreover, we show that our three conditions are, in a sense, necessary: If the choice function of some firm fails any of our three conditions, we can construct unit-demand choice functions for the other firms such that no stable and strategy-proof mechanism exists. Thus, our results provide a rationale for the ubiquity of cumulative offer mechanisms in practice.

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Election Manipulation on Social Networks: Seeding, Edge Removal, Edge Addition

Journal of Artificial Intelligence Research ◽

10.1613/jair.1.12826 ◽

2021 ◽

Vol 71 ◽

pp. 1049-1090

Author(s):

Matteo Castiglioni ◽

Diodato Ferraioli ◽

Nicola Gatti ◽

Giulia Landriani

Keyword(s):

Social Network ◽

Social Influence ◽

Optimal Solution ◽

Worst Case ◽

Edge Removal ◽

Independent Cascade Model ◽

The Social ◽

Special Cases ◽

Wide Range ◽

Local Modification

We focus on the election manipulation problem through social influence, where a manipulator exploits a social network to make her most preferred candidate win an election. Influence is due to information in favor of and/or against one or multiple candidates, sent by seeds and spreading through the network according to the independent cascade model. We provide a comprehensive theoretical study of the election control problem, investigating two forms of manipulations: seeding to buy influencers given a social network and removing or adding edges in the social network given the set of the seeds and the information sent. In particular, we study a wide range of cases distinguishing in the number of candidates or the kind of information spread over the network. Our main result shows that the election manipulation problem is not affordable in the worst-case, even when one accepts to get an approximation of the optimal margin of victory, except for the case of seeding when the number of hard-to-manipulate voters is not too large, and the number of uncertain voters is not too small, where we say that a voter that does not vote for the manipulator's candidate is hard-to-manipulate if there is no way to make her vote for this candidate, and uncertain otherwise. We also provide some results showing the hardness of the problems in special cases. More precisely, in the case of seeding, we show that the manipulation is hard even if the graph is a line and that a large class of algorithms, including most of the approaches recently adopted for social-influence problems (e.g., greedy, degree centrality, PageRank, VoteRank), fails to compute a bounded approximation even on elementary networks, such as undirected graphs with every node having a degree at most two or directed trees. In the case of edge removal or addition, our hardness results also apply to election manipulation when the manipulator has an unlimited budget, being allowed to remove or add an arbitrary number of edges, and to the basic case of social influence maximization/minimization in the restricted case of finite budget. Interestingly, our hardness results for seeding and edge removal/addition still hold in a re-optimization variant, where the manipulator already knows an optimal solution to the problem and computes a new solution once a local modification occurs, e.g., the removal/addition of a single edge.

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The social network

Skin & Allergy News ◽

10.1016/s0037-6337(13)70045-7 ◽

2013 ◽

Vol 44 (2) ◽

pp. 22

Author(s):

ALAN ROCKOFF

Keyword(s):

Social Network ◽

The Social

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The Multilevel p2 Model

Methodology ◽

10.1027/1614-2241.2.1.42 ◽

2006 ◽

Vol 2 (1) ◽

pp. 42-47 ◽

Cited By ~ 47

Author(s):

Bonne J. H. Zijlstra ◽

Marijtje A. J. van Duijn ◽

Tom A. B. Snijders

Keyword(s):

Social Network ◽

Network Data ◽

Mcmc Algorithm ◽

Independent Observation ◽

Single Observation ◽

Multiple Networks ◽

Social Network Data ◽

Multiple Observations ◽

Practical Support ◽

The Social

The p 2 model is a random effects model with covariates for the analysis of binary directed social network data coming from a single observation of a social network. Here, a multilevel variant of the p 2 model is proposed for the case of multiple observations of social networks, for example, in a sample of schools. The multilevel p 2 model defines an identical p 2 model for each independent observation of the social network, where parameters are allowed to vary across the multiple networks. The multilevel p 2 model is estimated with a Bayesian Markov Chain Monte Carlo (MCMC) algorithm that was implemented in free software for the statistical analysis of complete social network data, called StOCNET. The new model is illustrated with a study on the received practical support by Dutch high school pupils of different ethnic backgrounds.

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ПРАГМАТИЧНИЙ ПОТЕНЦІАЛ НАТИВНОЇ РЕКЛАМИ: ФОРМИ, ТРЕНДИ (НА ПРИКЛАДІ НАТИВНОГО КОНТЕНТУ В СОЦІАЛЬНІЙ МЕРЕЖІ FACEBOOK ЩОДО БРЕНДА ФАКУЛЬТЕТУ ЖУРНАЛІСТИКИ ЗНУ)

State and Regions. Series: Social Communications ◽

10.32840/cpu2219-8741/2020.1(41).18 ◽

2020 ◽

pp. 113

Author(s):

V. Kovpak ◽

N. Trotsenko

Keyword(s):

Social Network ◽

Professional Training ◽

Educational Process ◽

Communication Strategy ◽

Student Centered ◽

Teacher Student ◽

The Social ◽

National University ◽

High Level ◽

The Impact

<div>The article analyzes the peculiarities of the format of native advertising in the media space, its pragmatic potential (in particular, on the example of native content in the social network Facebook by the brand of the journalism department of ZNU), highlights the types and trends of native advertising. The following research methods were used to achieve the purpose of intelligence: descriptive (content content, including various examples), comparative (content presentation options) and typological (types, trends of native advertising, in particular, cross-media as an opportunity to submit content in different formats (video, audio, photos, text, infographics, etc.)), content analysis method using Internet services (using Popsters service). And the native code for analytics was the page of the journalism department of Zaporizhzhya National University on the social network Facebook. After all, the brand of the journalism department of Zaporozhye National University in 2019 celebrates its 15th anniversary. The brand vector is its value component and professional training with balanced distribution of theoretical and practical blocks (seven practices), student-centered (democratic interaction and high-level teacher-student dialogue) and integration into Ukrainian and world educational process (participation in grant programs).</div>And advertising on social networks is also a kind of native content, which does not appear in special blocks, and is organically inscribed on one page or another and unobtrusively offers, just remembering the product as if «to the word». Popsters service functionality, which evaluates an account (or linked accounts of one person) for 35 parameters, but the main three areas: reach or influence, or how many users evaluate, comment on the recording; true reach – the number of people affected; network score – an assessment of the audience’s response to the impact, or how far the network information diverges (how many share information on this page).Key words: nativeness, native advertising, branded content, special project, communication strategy.

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Emotive Trending And Tracking of Tweets

International Journal of Advanced Research in Computer Science and Software Engineering ◽

10.23956/ijarcsse.v7i11.463 ◽

2017 ◽

Vol 7 (11) ◽

pp. 25

Author(s):

Sanjay Chhataru Gupta

Keyword(s):

Social Networks ◽

Social Media ◽

Social Network ◽

The Social ◽

The World ◽

Complex Events

Popularity of the social media and the amount of importance given by an individual to social media has significantly increased in last few years. As more and more people become part of the social networks like Twitter, Facebook, information which flows through the social network, can potentially give us good understanding about what is happening around in our locality, state, nation or even in the world. The conceptual motive behind the project is to develop a system which analyses about a topic searched on Twitter. It is designed to assist Information Analysts in understanding and exploring complex events as they unfold in the world. The system tracks changes in emotions over events, signalling possible flashpoints or abatement. For each trending topic, the system also shows a sentiment graph showing how positive and negative sentiments are trending as the topic is getting trended.

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