On the role of evangelism in consensus formation: a simulation approach

PurposeOpinions continuously evolve in society. While conservative ideas may get replaced by a new one, some views remain immutable. Opinion formation and innovation diffusion have witnessed lots of attention in the last decade due to its widespread applicability in the diverse domain of science and technology. We analyse these scenarios in which interactions at the micro level results in the changes in opinions at the macro level in a population of predefined ideological groups.MethodsWe use the Bass model, otherwise well known for understanding innovation diffusion phenomena, to compute adoption probabilities of three opinion states-zealot, extremists and moderates. Thereafter, we employ cellular automata to explore the emergence of opinions through local and overlapped interactions between agents (people). NetLogo environment has been used to develop an agent-based model, simulating different ideological scenarios.ResultsSimulation results validate a critical proportion of committed individuals as a plausible basis for ideological shifts in societies. The analysis elucidates upon the role of moderates in the population and emergence of varying opinions. The results further delineate the role of evangelism through social and non-social methods in propagating views.ConclusionThe results obtained from these simulations endorse the conclusions reported in previous studies regarding the role of a critical zealot population, and the preponderance of non-social influence. We, however, use two-phase opinion model with different experimental settings. Additionally, we examine global observable, such as entropy of the system to reveal common patterns of adoption in the views and evenness of population after reaching a consensus.

[1]  Rainer Hegselmann.  Understanding Social Dynamics: The Cellular Automata Approach , 1995, Social Science Microsimulation.

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

[3]  C. Deming,et al.  Topographical and Temporal Diversity of the Human Skin Microbiome , 2009, Science.

[4]  James N. Druckman,et al.  Framing Public Opinion in Competitive Democracies , 2007, American Political Science Review.

[5]  Michael J. North,et al.  Complex adaptive systems modeling with Repast Simphony , 2013, Complex Adapt. Syst. Model..

[6]  S. Wolfram.  Statistical mechanics of cellular automata , 1983 .

[7]  Yun Liu,et al.  AN OPINION FORMATION MODEL WITH TWO STAGES , 2007 .

[8]  Katarzyna Sznajd-Weron,et al.  Opinion evolution in closed community , 2000, cond-mat/0101130.

[9]  David B. Fogel,et al.  An introduction to simulated evolutionary optimization , 1994, IEEE Trans. Neural Networks.

[10]  Carlo H. R. Heip,et al.  A New Index Measuring Evenness , 1974, Journal of the Marine Biological Association of the United Kingdom.

[11]  Misuk Lee,et al.  A study on the relationship between technology diffusion and new product diffusion , 2010 .

[12]  Master Gardener,et al.  Mathematical games: the fantastic combinations of john conway's new solitaire game "life , 1970 .

[13]  Uri Wilensky,et al.  NetLogo: A simple environment for modeling complexity , 2014 .

[14]  Ignacio Marín,et al.  Deciphering Network Community Structure by Surprise , 2011, PloS one.

[15]  Xiaogang Jin,et al.  Diversity of multilayer networks and its impact on collaborating epidemics. , 2014, Physical review. E, Statistical, nonlinear, and soft matter physics.

[16]  Jacob Goldenberg,et al.  Cellular automata modeling of resistance to innovations: Effects and solutions , 2004 .

[17]  J. Schwartz,et al.  Theory of Self-Reproducing Automata , 1967 .

[18]  Jonathan Ozik,et al.  Complex adaptive systems modeling with Repast , 2013 .

[19]  L. Rendell,et al.  How copying affects the amount, evenness and persistence of cultural knowledge: insights from the social learning strategies tournament , 2011, Philosophical Transactions of the Royal Society B: Biological Sciences.

[20]  Donald R. Lehmann,et al.  A Meta-Analysis of Applications of Diffusion Models , 1990 .

[21]  Jacob Goldenberg,et al.  Using Complex Systems Analysis to Advance Marketing Theory Development , 2001 .

[22]  Qipeng Liu,et al.  Opinion dynamics with similarity-based random neighbors , 2013, Scientific Reports.

[23]  L. Dagum,et al.  OpenMP: an industry standard API for shared-memory programming , 1998 .

[24]  M. Hill.  Diversity and Evenness: A Unifying Notation and Its Consequences , 1973 .

[25]  Anna Papush,et al.  Encouraging moderation: clues from a simple model of ideological conflict. , 2012, Physical review letters.

[26]  Fei Xiong,et al.  Correlation between information diffusion and opinion evolution on social media , 2014 .

[27]  F. Bass.  The Relationship between Diffusion Rates, Experience Curves, and Demand Elasticities for Consumer Durable Technological Innovations , 1980 .

[28]  M. Delorme,et al.  Cellular automata : a parallel model , 1999 .

[29]  Gadi Fibich,et al.  A Comparison of Stochastic Cellular Automata Diffusion with the Bass Diffusion Model , 2010 .

[30]  Muaz A. Niazi,et al.  Complex adaptive communication networks and environments: Part 1 , 2013, Simul..

[31]  Frank M. Bass,et al.  A New Product Growth for Model Consumer Durables , 2004, Manag. Sci..

[32]  Semra Gündüç.  The role of fanatics in consensus formation , 2015 .

[33]  R. Durrett,et al.  The Importance of Being Discrete (and Spatial) , 1994 .

[34]  Michael Wooldridge,et al.  Introduction to multiagent systems , 2001 .