Finite-time influence systems and the Wisdom of Crowd effect

Recent contributions have studied how an influence system may affect the wisdom of crowd phenomenon. In the so-called naive learning setting, a crowd of individuals holds opinions that are statistically independent estimates of an unknown parameter; the crowd is wise when the average opinion converges to the true parameter in the limit of infinitely many individuals. Unfortunately, even starting from wise initial opinions, a crowd subject to certain influence systems may lose its wisdom. It is of great interest to characterize when an influence system preserves the crowd wisdom effect. In this paper we introduce and characterize numerous wisdom preservation properties of the basic French-DeGroot influence system model. Instead of requiring complete convergence to consensus as in the previous naive learning model by Golub and Jackson, we study finite-time executions of the French-DeGroot influence process and establish in this novel context the notion of prominent families (as a group of individuals with outsize influence). Surprisingly, finite-time wisdom preservation of the influence system is strictly distinct from its infinite-time version. We provide a comprehensive treatment of various finite-time wisdom preservation notions, counterexamples to meaningful conjectures, and a complete characterization of equal-neighbor influence systems.

[1]  James Surowiecki The wisdom of crowds: Why the many are smarter than the few and how collective wisdom shapes business, economies, societies, and nations Doubleday Books. , 2004 .

[2]  Matthew O. Jackson,et al.  Naïve Learning in Social Networks and the Wisdom of Crowds , 2010 .

[3]  Ali Jadbabaie,et al.  Non-Bayesian Social Learning , 2011, Games Econ. Behav..

[4]  Damon Centola,et al.  Network dynamics of social influence in the wisdom of crowds , 2017, Proceedings of the National Academy of Sciences.

[5]  Asuman E. Ozdaglar,et al.  Opinion Fluctuations and Disagreement in Social Networks , 2010, Math. Oper. Res..

[6]  Albert,et al.  Emergence of scaling in random networks , 1999, Science.

[7]  J. French,et al.  A formal theory of social power. , 1956, Psychological review.

[8]  F. Galton Vox Populi , 1907, Nature.

[9]  B. Bollobás The evolution of random graphs , 1984 .

[10]  Noah E. Friedkin,et al.  Social Influence Network Theory: A Sociological Examination of Small Group Dynamics , 2011 .

[11]  P. Erdos,et al.  On the evolution of random graphs , 1984 .

[12]  Noah E. Friedkin,et al.  Theoretical Foundations for Centrality Measures , 1991, American Journal of Sociology.

[13]  Roberto Tempo,et al.  A Tutorial on Modeling and Analysis of Dynamic Social Networks. Part II , 2018, Annu. Rev. Control..

[14]  R. Durrett Random Graph Dynamics: References , 2006 .

[15]  Béla Bollobás,et al.  The degree sequence of a scale‐free random graph process , 2001, Random Struct. Algorithms.

[16]  D. Helbing,et al.  How social influence can undermine the wisdom of crowd effect , 2011, Proceedings of the National Academy of Sciences.

[17]  Joel H. Spencer,et al.  Connectivity Transitions in Networks with Super-Linear Preferential Attachment , 2005, Internet Math..

[18]  Elizabeth L. Wilmer,et al.  Markov Chains and Mixing Times , 2008 .

[19]  P. Bonacich Factoring and weighting approaches to status scores and clique identification , 1972 .

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

[21]  Ilan Lobel,et al.  BAYESIAN LEARNING IN SOCIAL NETWORKS , 2008 .