How Robust Is the Wisdom of the Crowds?

We introduce the study of adversarial effects on wisdom of the crowd phenomena. In particular, we examine the ability of an adversary to influence a social network so that the majority of nodes are convinced by a falsehood, using its power to influence a certain fraction, µ 0:5. When we examine expander graphs as well as random graphs we prove such bounds even for stronger adversaries, who are able to pick and choose not only who the experts are, but also which ones of them would communicate the wrong values, as long as their proportion is 1 - p. Furthermore, we study different propagation models and their effects on the feasibility of obtaining the true value for different adversary types.

[1]  David P. Myatt,et al.  Innovation Diffusion in Heterogeneous Populations: Contagion, Social Influence, and Social Learning , 2009 .

[2]  D. North.  Competing Technologies , Increasing Returns , and Lock-In by Historical Events , 1994 .

[3]  L. Blume.  The Statistical Mechanics of Strategic Interaction , 1993 .

[4]  Jimeng Sun,et al.  Social influence analysis in large-scale networks , 2009, KDD.

[5]  S. Bikhchandani,et al.  You have printed the following article : A Theory of Fads , Fashion , Custom , and Cultural Change as Informational Cascades , 2007 .

[6]  Matthew Richardson,et al.  Mining the network value of customers , 2001, KDD '01.

[7]  Elchanan Mossel,et al.  Majority dynamics and aggregation of information in social networks , 2012, Autonomous Agents and Multi-Agent Systems.

[8]  Jon Kleinberg,et al.  Maximizing the spread of influence through a social network , 2003, KDD '03.

[9]  Éva Tardos,et al.  Maximizing the Spread of Influence through a Social Network , 2015, Theory Comput..

[10]  M. Degroot.  Reaching a Consensus , 1974 .

[11]  A. Banerjee,et al.  A Simple Model of Herd Behavior , 1992 .

[12]  Simone Campanoni.  Competition , 1866, Nature.

[13]  Apresentação Newton Paulo Bueno.  W. Brian Arthur - Competing technologies, increasing returns, and lock-in by historical events , 2010 .

[14]  D. Watts,et al.  Social Influence, Binary Decisions and Collective Dynamics , 2008 .

[15]  Noga Alon,et al.  The Probabilistic Method , 2015, Fundamentals of Ramsey Theory.

[16]  Noga Alon,et al.  Sequential voting with externalities: herding in social networks , 2012, EC '12.

[17]  Keri K. Stephens,et al.  Rogers' diffusion of innovations , 2008 .

[18]  Redaktionen.  THE REVIEW OF ECONOMIC STUDIES , 1960 .

[19]  Duncan J Watts,et al.  A simple model of global cascades on random networks , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[20]  Nikhil R. Devanur,et al.  Reaching Consensus via Non-Bayesian Asynchronous Learning in Social Networks , 2014, APPROX-RANDOM.

[21]  C. Shapiro,et al.  Network Externalities, Competition, and Compatibility , 1985 .

[22]  E. Rogers,et al.  Diffusion of Innovations , 1964 .

[23]  Glenn Ellison.  Learning, Local Interaction, and Coordination , 1993 .

[24]  Vijay Mahajan,et al.  New Product Diffusion Models in Marketing: A Review and Directions for Research: , 1990 .

[25]  Adam Tauman Kalai,et al.  Trust-based recommendation systems: an axiomatic approach , 2008, WWW.

[26]  Jacob Goldenberg,et al.  Talk of the Network: A Complex Systems Look at the Underlying Process of Word-of-Mouth , 2001 .

[27]  Matthew Richardson,et al.  Mining knowledge-sharing sites for viral marketing , 2002, KDD.

[28]  Peter H. Reingen,et al.  Social Ties and Word-of-Mouth Referral Behavior , 1987 .

[29]  Simon Lawton-Smith,et al.  Increasing returns. , 2005, Mental health today.

[30]  Eyton,et al.  The Diffusion of Innovations in Social Networks , 2002 .

[31]  Mark S. Granovetter.  The Strength of Weak Ties , 1973, American Journal of Sociology.

[32]  Masanori Takezawa,et al.  Centrality in sociocognitive networks and social influence : An illustration in a group decision-making context , 1997 .