Maximal Information Propagation via Lotteries

Jing Chen, Bo Li

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

Abstract

Propagating information to more people through their friends is becoming an increasingly important technology used in domains such as blockchain, advertising, and social media. To incentivize people to broadcast the information, the designer may use a monetary rewarding scheme, which specifies who gets how much, to compensate for the propagation. Several properties are desirable for the rewarding scheme, such as budget feasible, individually rational, incentive compatible and Sybil-proof. In this work, we design a free market with lotteries, where every participant can decide by herself how much of the reward she wants to withhold before propagating to others. We show that in the free market, the participants have a strong incentive to maximally propagate the information and all the above properties are satisfied automatically.

Original languageEnglish
Title of host publicationWeb and Internet Economics - 17th International Conference, WINE 2021, Proceedings
EditorsMichal Feldman, Hu Fu, Inbal Talgam-Cohen
PublisherSpringer Science and Business Media Deutschland GmbH
Pages486-503
Number of pages18
ISBN (Print)9783030946753
DOIs
Publication statusPublished - Oct 2021
Event17th International Conference on Web and Internet Economics, WINE 2021 - Virtual, Online
Duration: 14 Dec 202117 Dec 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13112 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference17th International Conference on Web and Internet Economics, WINE 2021
CityVirtual, Online
Period14/12/2117/12/21

Keywords

  • Free market design
  • Information propagation
  • Nash equilibrium

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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