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Evolutionary Learning Model of Social Networking Services with Diminishing Marginal Utility

Published: 23 April 2018 Publication History

Abstract

We propose a model of a social networking service (SNS) with diminishing marginal utility in the framework of evolutionary computing and present our investigation on the effect of diminishing marginal utility on the dominant structure of strategies in all agents. SNSs such as Twitter and Facebook have been growing rapidly, but why they are prospering is unknown. SNSs have the characteristics of a public goods game because they are maintained by users posting many articles that incur some cost and because users can also be free riders, who just read articles. Thus, a number of studies aimed at understanding the conditions or mechanisms that keep social media thriving theoretically by introducing the meta-rewards game, which is a variation of a public goods game. The meta-rewards games assume constant marginal utility, meaning that the rewards by receiving comments increase linearly according to the number of comments, but describing the psychological rewards of humans is often inappropriate. In this paper, we present our modification of the model using the diminishing marginal utility and our comparison of the experimental results with those of the original meta-rewards game. We demonstrate that the structure of dominant strategies of all agents in our game is quite different from that in the original meta-rewards game and is more reasonable to explain the users' behavior in SNSs because their efforts in SNSs are limited even if they have many friends.

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  • (2021)Modeling and analyzing users’ behavioral strategies with co-evolutionary processComputational Social Networks10.1186/s40649-021-00092-18:1Online publication date: 10-Mar-2021
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cover image ACM Other conferences
WWW '18: Companion Proceedings of the The Web Conference 2018
April 2018
2023 pages
ISBN:9781450356404
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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  • IW3C2: International World Wide Web Conference Committee

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International World Wide Web Conferences Steering Committee

Republic and Canton of Geneva, Switzerland

Publication History

Published: 23 April 2018

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Author Tags

  1. agent network
  2. cooperation
  3. evolutionary learning
  4. prisoner's dilemma
  5. social network systems

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  • Research-article

Funding Sources

  • JSPS KAKENHI

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WWW '18
Sponsor:
  • IW3C2
WWW '18: The Web Conference 2018
April 23 - 27, 2018
Lyon, France

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Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Cited By

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  • (2024)Incentivizing Proportional Fairness for Multi-Task Allocation in CrowdsensingIEEE Transactions on Services Computing10.1109/TSC.2023.332563617:3(990-1000)Online publication date: May-2024
  • (2021)Agent-Based Simulation to Measure the Effectiveness of Citizen Sensing Applications—The Case of Missing ChildrenApplied Sciences10.3390/app1114653011:14(6530)Online publication date: 15-Jul-2021
  • (2021)Modeling and analyzing users’ behavioral strategies with co-evolutionary processComputational Social Networks10.1186/s40649-021-00092-18:1Online publication date: 10-Mar-2021
  • (2021)A Green Stackelberg-game Incentive Mechanism for Multi-service Exchange in Mobile CrowdsensingACM Transactions on Internet Technology10.1145/342150622:2(1-29)Online publication date: 22-Oct-2021
  • (2020)Rewards Visualization System Promotes Information ProvisionAdvances in Artificial Intelligence10.1007/978-3-030-39878-1_6(55-65)Online publication date: 4-Feb-2020
  • (2019)Multiple world genetic algorithm to analyze individually advantageous behaviors in complex networksProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3319619.3321989(297-298)Online publication date: 13-Jul-2019
  • (2019)Multiple-World Genetic Algorithm to Identify Locally Reasonable Behaviors in Complex Social Networks2019 IEEE International Conference on Systems, Man and Cybernetics (SMC)10.1109/SMC.2019.8914277(3665-3672)Online publication date: 6-Oct-2019
  • (2019)A belief in rewards accelerates cooperation on consumer-generated mediaJournal of Computational Social Science10.1007/s42001-019-00049-53:1(19-31)Online publication date: 29-Jul-2019
  • (2019)Analysis of Diversity and Dynamics in Co-evolution of Cooperation in Social Networking ServicesComplex Networks and Their Applications VIII10.1007/978-3-030-36687-2_41(495-506)Online publication date: 26-Nov-2019

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