research-article

Mechanisms for multi-level marketing

Authors:

Moshe Tennenholtz,

Aviv ZoharAuthors Info & Claims

EC '11: Proceedings of the 12th ACM conference on Electronic commerce

Pages 209 - 218

https://doi.org/10.1145/1993574.1993606

Published: 05 June 2011 Publication History

Abstract

Multi-level marketing is a marketing approach that motivates its participants to promote a certain product among their friends. The popularity of this approach increases due to the accessibility of modern social networks, however, it existed in one form or the other long before the Internet age began (the infamous Pyramid scheme that dates back at least a century is in fact a special case of multi-level marketing). This paper lays foundations for the study of reward mechanisms in multi-level marketing within social networks. We provide a set of desired properties for such mechanisms and show that they are uniquely satisfied by geometric reward mechanisms. The resilience of mechanisms to false-name manipulations is also considered; while geometric reward mechanisms fail against such manipulations, we exhibit other mechanisms which are false-name-proof.

References

[1]

DARPA network challenge. http://balloon.media.mit.edu/mit/rules/.

[2]

T. Ågotnes, W. van der Hoek, M. Tennenholtz, and M. Wooldridge. Power in normative systems. In AAMAS, pages 145--152, 2009.

Digital Library

[3]

D. Arthur, R. Motwani, A. Sharma, and Y. Xu. Pricing strategies for viral marketing on social networks. In Proceedings of the 5th International Workshop on Internet and Network Economics, WINE '09, pages 101--112, Berlin, Heidelberg, 2009. Springer-Verlag.

Digital Library

[4]

Y. Bachrach and J. S. Rosenschein. Computing the banzhaf power index in network flow games. In AAMAS, 2007.

Digital Library

[5]

N. Chen. On the approximability of influence in social networks. SIAM J. Discret. Math., 23:1400--1415, September 2009.

Digital Library

[6]

A. Cheng and E. Friedman. Sybilproof reputation mechanisms. In Proceedings of the 2005 ACM SIGCOMM workshop on Economics of peer-to-peer systems, P2PECON '05, pages 128--132, New York, NY, USA, 2005. ACM.

Digital Library

[7]

J. K. David Kempe and E. Tardos. Maximizing the spread of influence in a social network. In 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 137--146, 2003.

Digital Library

[8]

P. Domingos and M. Richardson. Mining the network value of customers. In Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining, KDD '01, pages 57--66, New York, NY, USA, 2001. ACM.

Digital Library

[9]

M. Granovetter. Threshold models of collective behavior. American Journal of Sociology, 83:1420--1443, 1978.

[10]

J. Hartline, V. Mirrokni, and M. Sundararajan. Optimal marketing strategies over social networks. In Proceeding of the 17th international conference on World Wide Web, WWW '08, pages 189--198, New York, NY, USA, 2008. ACM.

Digital Library

[11]

J. Kleinberg. Cascading behavior in networks: Algorithmic and economic issues. In E. T. V. V. N. Nisan, T. Roughgarden, editor, Algorithmic Game Theory. Cambridge University Press, 2007.

[12]

J. M. Kleinberg and P. Raghavan. Query incentive networks. In FOCS, pages 132--141, 2005.

Digital Library

[13]

N. Megiddo. Computational complexity of the game theory approach to cost allocation for a tree. Mathematics of Operations Research, 3(3):189--196, 1978.

Digital Library

[14]

S. Morris. Contagion. Review of Economic Studies, 67:137--146, 2003.

[15]

T. Schelling. Micromotives and Macrobehavior. Norton, London: Penguin, 1978.

[16]

Y. Shoham and M. Tennenholtz. On the emergence of social conventions: Modeling, analysis, and simulations. Artif. Intell., 94(1--2):139--166, 1997.

Digital Library

[17]

M. Tennenholtz. Convention Evolution in Organizations and Markets. Computational and Mathematical Organization Thoery, 2(4):259--283, 1996.

[18]

T. Todo, A. Iwasaki, M. Yokoo, and Y. Sakurai. Characterizing false-name-proof allocation rules in combinatorial auctions. In Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 1, AAMAS '09, pages 265--272, Richland, SC, 2009. International Foundation for Autonomous Agents and Multiagent Systems.

Digital Library

[19]

L. Wagman and V. Conitzer. Optimal false-name-proof voting rules with costly voting. In Proceedings of the 23rd national conference on Artificial intelligence - Volume 1, pages 190--195. AAAI Press, 2008.

Digital Library

[20]

P. Young. Individual Strategy and Social Structure: An Evolutionary Theory of Institutions. Princeton University Press, 1998.

Cited By

Bhattacharyya RDave PDey PNath SDastani MSichman JAlechina NDignum V(2024)Optimal Referral Auction DesignProceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems10.5555/3635637.3662867(198-206)Online publication date: 6-May-2024
https://dl.acm.org/doi/10.5555/3635637.3662867
Radhakrishnan RAithal P(2024)Review Based Research Topic Identification and Analysis on Multi-Level Marketing BusinessInternational Journal of Applied Engineering and Management Letters10.47992/IJAEML.2581.7000.0224(74-112)Online publication date: 13-Apr-2024
https://doi.org/10.47992/IJAEML.2581.7000.0224
Li BHao DChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Core-Competitiveness in Partially Observable Networked MarketProceedings of the ACM Web Conference 202410.1145/3589334.3645555(156-166)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645555
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Index Terms

Mechanisms for multi-level marketing
1. Applied computing
  1. Law, social and behavioral sciences
    1. Economics

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Published In

cover image ACM Conferences

EC '11: Proceedings of the 12th ACM conference on Electronic commerce

June 2011

384 pages

ISBN:9781450302616

DOI:10.1145/1993574

General Chair:
Yoav Shoham
Stanford University, USA
,
Program Chairs:
Yan Chen
University of Michigan, USA
,
Tim Roughgarden
Stanford University, USA

Copyright © 2011 ACM.

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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SIGecom: Special Interest Group on Economics and Computation

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 05 June 2011

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EC '11

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SIGecom

EC '11: ACM Conference on Electronic Commerce

June 5 - 9, 2011

California, San Jose, USA

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Overall Acceptance Rate 664 of 2,389 submissions, 28%

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Cited By

Bhattacharyya RDave PDey PNath SDastani MSichman JAlechina NDignum V(2024)Optimal Referral Auction DesignProceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems10.5555/3635637.3662867(198-206)Online publication date: 6-May-2024
https://dl.acm.org/doi/10.5555/3635637.3662867
Radhakrishnan RAithal P(2024)Review Based Research Topic Identification and Analysis on Multi-Level Marketing BusinessInternational Journal of Applied Engineering and Management Letters10.47992/IJAEML.2581.7000.0224(74-112)Online publication date: 13-Apr-2024
https://doi.org/10.47992/IJAEML.2581.7000.0224
Li BHao DChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Core-Competitiveness in Partially Observable Networked MarketProceedings of the ACM Web Conference 202410.1145/3589334.3645555(156-166)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645555
Li BHao DZhao D(2024)Diffusion auction design with transaction costsAutonomous Agents and Multi-Agent Systems10.1007/s10458-023-09631-838:1Online publication date: 1-Jun-2024
https://dl.acm.org/doi/10.1007/s10458-023-09631-8
Antler Y(2023)Multilevel marketing: Pyramid‐shaped schemes or exploitative scams?Theoretical Economics10.3982/TE489018:2(633-668)Online publication date: 2023
https://doi.org/10.3982/TE4890
Dehez P(2023)Sharing a collective probability of successMathematical Social Sciences10.1016/j.mathsocsci.2023.03.006123(122-127)Online publication date: May-2023
https://doi.org/10.1016/j.mathsocsci.2023.03.006
Jeong SLee J(2023)The Groupwise-Pivotal Referral Auction: Core-selecting referral strategy-proof mechanismGames and Economic Behavior10.1016/j.geb.2023.11.012Online publication date: Nov-2023
https://doi.org/10.1016/j.geb.2023.11.012
Banaeian Far SRajabzadeh Asaar MHaghbin A(2023)A privacy-preserving framework for blockchain-based multi-level marketingComputers & Industrial Engineering10.1016/j.cie.2023.109095177(109095)Online publication date: Mar-2023
https://doi.org/10.1016/j.cie.2023.109095
Ge XZhang XZhao D(2023)Maximal Information Propagation with Limited ResourcesDistributed Artificial Intelligence10.1007/978-3-031-25549-6_4(45-59)Online publication date: 22-Mar-2023
https://doi.org/10.1007/978-3-031-25549-6_4
Wang YAbo Khamis MNgo HPichler RSuciu DIves ZBonifati AEl Abbadi A(2022)Optimizing Recursive Queries with Progam SynthesisProceedings of the 2022 International Conference on Management of Data10.1145/3514221.3517827(79-93)Online publication date: 10-Jun-2022
https://dl.acm.org/doi/10.1145/3514221.3517827
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