doi: 10.3934/nhm.2021006

Opinion fitness and convergence to consensus in homogeneous and heterogeneous populations

1. 

Departamento de Ecuaciones Diferenciales y Análisis Numérico, Facultad de Matemáticas, C/ Tarfia s/n, and Instituto de Matemáticas Antonio de Castro Brzezicki, Edificio Celestino Mutis, Avda. de la Reina Mercedes s/n, Universidad de Sevilla, Campus de Reina Mercedes, (41012) Sevilla, Spain

2. 

IMAS UBA-CONICET and Departamento de Matemática, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Av Cantilo s/n, Ciudad Universitaria, (1428) Buenos Aires, Argentina

Received  August 2020 Revised  November 2020 Published  February 2021

Fund Project: This work was partially supported by Universidad de Buenos Aires under grants 20020150100154BA and 20020130100283BA, by ANPCyT PICT2012 0153 and PICT2014-1771, CONICET (Argentina) PIP 11220150100032CO and 5478/1438. J.P. Pinasco and N. Saintier are members of CONICET, Argentina. M. Pérez-Llanos thanks to Junta de Andalucía FQM-131, Spain

In this work we study the formation of consensus in homogeneous and heterogeneous populations, and the effect of attractiveness or fitness of the opinions. We derive the corresponding kinetic equations, analyze the long time behavior of their solutions, and characterize the consensus opinion.

Citation: Mayte Pérez-Llanos, Juan Pablo Pinasco, Nicolas Saintier. Opinion fitness and convergence to consensus in homogeneous and heterogeneous populations. Networks & Heterogeneous Media, doi: 10.3934/nhm.2021006
References:
[1]

G. Aletti, A. K. Naimzada and G. Naldi., Mathematics and physics applications in sociodynamics simulation: The case of opinion formation and diffusion, Mathematical Modeling of Collective Behavior in Socio-Economic and Life Sciences, Birkhäuser Boston, (2010), 203–221. doi: 10.1007/978-0-8176-4946-3_8.  Google Scholar

[2]

G. AlettiG. Naldi and G. Toscani, First-order continuous models of opinion formation, SIAM J. Appl. Math., 67 (2007), 837-853.  doi: 10.1137/060658679.  Google Scholar

[3]

S. E. Asch, Opinions and social pressure, Scientific American, 193 (1955), 31-35.  doi: 10.1038/scientificamerican1155-31.  Google Scholar

[4]

R. B. Ash, Real Analysis and Probability, Probability and Mathematical Statistics, Academic Press, New York-London, 1972.  Google Scholar

[5]

P. Balenzuela, J. P. Pinasco and V. Semeshenko, The undecided have the key: Interaction driven opinion dynamics in a three state model, PLoS ONE, 10 (2016), e0139572, 1–21. Google Scholar

[6]

B. O. BaumgaertnerR. C. Tyson and S. M. Krone, Opinion strength influences the spatial dynamics of opinion formation, The Journal of Mathematical Sociology, 40 (2016), 207-218.  doi: 10.1080/0022250X.2016.1205049.  Google Scholar

[7]

B. O. Baumgaertner, P. A. Fetros, R. C. Tyson and S. M. Krone, Spatial Opinion Dynamics and the Efects of Two Types of Mixing, Phys Rev E., 98 (2018), 022310. Google Scholar

[8]

N. Bellomo, Modeling Complex Living Systems A Kinetic Theory and Stochastic Game Approach, Birkhauser, 2008.  Google Scholar

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N. Bellomo, G. Ajmone Marsan and A. Tosin, Complex Systems and Society. Modeling and Simulation, SpringerBriefs in Mathematics, 2013. doi: 10.1007/978-1-4614-7242-1.  Google Scholar

[10]

K. C. Border and C. D. Aliprantis, Infinite Dimensional Analysis - A Hitchhiker's Guide, 3rd Edition, Springer, 2006.  Google Scholar

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E. Burnstein and A. Vinokur, What a person thinks upon learning he has chosen differently from others: Nice evidence for the persuasive-arguments explanation of choice shifts, Journal of Experimental Social Psychology, 11 (1975), 412-426.  doi: 10.1016/0022-1031(75)90045-1.  Google Scholar

[12]

J. A. CañizoJ. A. Carrillo and J. Rosado, A well-posedness theory in measures for some kinetic models of collective motion, Mathematical Models and Methods in Applied Sciences, 21 (2011), 515-539.  doi: 10.1142/S0218202511005131.  Google Scholar

[13]

R. B. Cialdini and M. R. Trost, Social influence: Social norms, conformity and compliance, The Handbook of Social Psychology, McGraw-Hill, (1998), 151–192. Google Scholar

[14]

F. Cucker and S. Smale, Emergent behavior in flocks, IEEE Transactions on Automatic Control, 52 (2007), 852-862.  doi: 10.1109/TAC.2007.895842.  Google Scholar

[15]

G. DeffuantD. NeauF. Amblard and G. Weisbuch, Mixing beliefs among interacting agents, Advances in Complex Systems, 3 (2000), 87-98.  doi: 10.1142/S0219525900000078.  Google Scholar

[16]

P. Embrechts and M. Hofert, A note on generalized inverse, Mathematical Methods of Operations Research, 77 (2013), 423-432.  doi: 10.1007/s00186-013-0436-7.  Google Scholar

[17]

N. E. Friedkin and E. C. Johnsen, Social influence and opinions, Journal of Mathematical Sociology, 15 (1990), 193-206.  doi: 10.1080/0022250X.1990.9990069.  Google Scholar

[18]

T. Fujimoto, A simple model of consensus formation, Okayama Economic Review, 31 (1999), 95-100.   Google Scholar

[19]

S. Galam, Sociophysics: A Physicist's Modeling of Psycho-Political Phenomena, , Springer Science & Business Media, 2012. doi: 10.1007/978-1-4614-2032-3.  Google Scholar

[20]

R. Hegselmann and U. Krause, Opinion dynamics and bounded confidence: Models, analysis and simulation, Journal of Artificial Societies and Social Simulation, 5 (2002). Google Scholar

[21]

R. A. Holley and T. M. Liggett, Ergodic theorems for weakly interacting infinite systems and the voter model, The Annals of Probability, 3 (1975), 643-663.  doi: 10.1214/aop/1176996306.  Google Scholar

[22]

C. La Rocca, L. A. Braunstein and F. Vázquez, The influence of persuasion in opinion formation and polarization, Europhys. Letters, 106 (2014), 40004. doi: 10.1209/0295-5075/106/40004.  Google Scholar

[23]

B. Latané, The psychology of social impact, American Psychologist, 36 (1981), 343-356.   Google Scholar

[24]

J. Lorenz, Continuous opinion dynamics under bounded confidence: A survey, International Journal of Modern Physics C, 18 (2007), 1819-1838.  doi: 10.1142/S0129183107011789.  Google Scholar

[25]

M. Mäs and A. Flache, Differentiation without distancing. Explaining bi-polarization of opinions without negative influence, PloS one, 8 (2013), e74516. Google Scholar

[26]

P. Milgrom and I. Segal, Envelope theorems for arbitrary choice sets, Econometrica, 70 (2002), 583-601.  doi: 10.1111/1468-0262.00296.  Google Scholar

[27]

R. Ochrombel, Simulation of Sznajd sociophysics model with convincing single opinions, International Journal of Modern Physics C, 12 (2001), 1091-1092.  doi: 10.1142/S0129183101002346.  Google Scholar

[28] L. Pareschi and G. Toscani, Interacting Multiagent Systems: Kinetic Equations and Monte Carlo Methods, Oxford University Press, Oxford, 2014.   Google Scholar
[29]

L. PedrazaJ. P. Pinasco and N. Saintier, Measure-valued opinion dynamics, M3AS: Mathematical Models and Methods in Applied Sciences, 30 (2020), 225-260.  doi: 10.1142/S0218202520500062.  Google Scholar

[30]

M. Pérez-Llanos, J. P. Pinasco and N. Saintier, Opinion attractiveness and its effect in opinion formation models, Phys. A, 559 (2020), 125017, 9 pp. doi: 10.1016/j.physa.2020.125017.  Google Scholar

[31]

M. Pŕez-LlanosJ. P. PinascoN. Saintier and A. Silva, Opinion formation models with heterogeneous persuasion and zealotry, SIAM Journal on Mathematical Analysis, 50 (2018), 4812-4837.  doi: 10.1137/17M1152784.  Google Scholar

[32]

F. Vazquez, N. Saintier and J. P. Pinasco, The role of voting intention in public opinion polarization, Phys. Rev. E, 101 (2020), 012101, 13pp.  Google Scholar

[33]

N. Saintier, J. P. Pinasco and F. Vazquez, A model for a phase transition between political mono-polarization and bi-polarization, Chaos: An Interdisciplinary Journal of Nonlinear Science, 30 (2020), 063146, 17 pp. doi: 10.1063/5.0004996.  Google Scholar

[34]

J. P. PinascoV. Semeshenko and P. Balenzuela, Modeling opinion dynamics: Theoretical analysis and continuous approximation, Chaos, Solitons & Fractals, 98 (2017), 210-215.  doi: 10.1016/j.chaos.2017.03.033.  Google Scholar

[35]

F. Slanina and H. Lavicka, Analytical results for the Sznajd model of opinion formation, The European Physical Journal B, 35 (2003) 279–288. doi: 10.1140/epjb/e2003-00278-0.  Google Scholar

[36] D. W. Stroock, Probability Theory, An Analytic View, Cambridge University Press, 1993.   Google Scholar
[37]

K. Sznajd-Weron and J. Sznajd, Opinion evolution in closed community, International Journal of Modern Physics - C, 11 (2000), 1157-1165.  doi: 10.1142/S0129183100000936.  Google Scholar

[38]

G. Toscani, Kinetic models of opinion formation, Communications in Mathematical Sciences, 4 (2006), 481-496.  doi: 10.4310/CMS.2006.v4.n3.a1.  Google Scholar

[39]

C. Villani, Topics in optimal transportation, Grad.Studies in Math., American Mathematical Soc., (2003). doi: 10.1090/gsm/058.  Google Scholar

show all references

References:
[1]

G. Aletti, A. K. Naimzada and G. Naldi., Mathematics and physics applications in sociodynamics simulation: The case of opinion formation and diffusion, Mathematical Modeling of Collective Behavior in Socio-Economic and Life Sciences, Birkhäuser Boston, (2010), 203–221. doi: 10.1007/978-0-8176-4946-3_8.  Google Scholar

[2]

G. AlettiG. Naldi and G. Toscani, First-order continuous models of opinion formation, SIAM J. Appl. Math., 67 (2007), 837-853.  doi: 10.1137/060658679.  Google Scholar

[3]

S. E. Asch, Opinions and social pressure, Scientific American, 193 (1955), 31-35.  doi: 10.1038/scientificamerican1155-31.  Google Scholar

[4]

R. B. Ash, Real Analysis and Probability, Probability and Mathematical Statistics, Academic Press, New York-London, 1972.  Google Scholar

[5]

P. Balenzuela, J. P. Pinasco and V. Semeshenko, The undecided have the key: Interaction driven opinion dynamics in a three state model, PLoS ONE, 10 (2016), e0139572, 1–21. Google Scholar

[6]

B. O. BaumgaertnerR. C. Tyson and S. M. Krone, Opinion strength influences the spatial dynamics of opinion formation, The Journal of Mathematical Sociology, 40 (2016), 207-218.  doi: 10.1080/0022250X.2016.1205049.  Google Scholar

[7]

B. O. Baumgaertner, P. A. Fetros, R. C. Tyson and S. M. Krone, Spatial Opinion Dynamics and the Efects of Two Types of Mixing, Phys Rev E., 98 (2018), 022310. Google Scholar

[8]

N. Bellomo, Modeling Complex Living Systems A Kinetic Theory and Stochastic Game Approach, Birkhauser, 2008.  Google Scholar

[9]

N. Bellomo, G. Ajmone Marsan and A. Tosin, Complex Systems and Society. Modeling and Simulation, SpringerBriefs in Mathematics, 2013. doi: 10.1007/978-1-4614-7242-1.  Google Scholar

[10]

K. C. Border and C. D. Aliprantis, Infinite Dimensional Analysis - A Hitchhiker's Guide, 3rd Edition, Springer, 2006.  Google Scholar

[11]

E. Burnstein and A. Vinokur, What a person thinks upon learning he has chosen differently from others: Nice evidence for the persuasive-arguments explanation of choice shifts, Journal of Experimental Social Psychology, 11 (1975), 412-426.  doi: 10.1016/0022-1031(75)90045-1.  Google Scholar

[12]

J. A. CañizoJ. A. Carrillo and J. Rosado, A well-posedness theory in measures for some kinetic models of collective motion, Mathematical Models and Methods in Applied Sciences, 21 (2011), 515-539.  doi: 10.1142/S0218202511005131.  Google Scholar

[13]

R. B. Cialdini and M. R. Trost, Social influence: Social norms, conformity and compliance, The Handbook of Social Psychology, McGraw-Hill, (1998), 151–192. Google Scholar

[14]

F. Cucker and S. Smale, Emergent behavior in flocks, IEEE Transactions on Automatic Control, 52 (2007), 852-862.  doi: 10.1109/TAC.2007.895842.  Google Scholar

[15]

G. DeffuantD. NeauF. Amblard and G. Weisbuch, Mixing beliefs among interacting agents, Advances in Complex Systems, 3 (2000), 87-98.  doi: 10.1142/S0219525900000078.  Google Scholar

[16]

P. Embrechts and M. Hofert, A note on generalized inverse, Mathematical Methods of Operations Research, 77 (2013), 423-432.  doi: 10.1007/s00186-013-0436-7.  Google Scholar

[17]

N. E. Friedkin and E. C. Johnsen, Social influence and opinions, Journal of Mathematical Sociology, 15 (1990), 193-206.  doi: 10.1080/0022250X.1990.9990069.  Google Scholar

[18]

T. Fujimoto, A simple model of consensus formation, Okayama Economic Review, 31 (1999), 95-100.   Google Scholar

[19]

S. Galam, Sociophysics: A Physicist's Modeling of Psycho-Political Phenomena, , Springer Science & Business Media, 2012. doi: 10.1007/978-1-4614-2032-3.  Google Scholar

[20]

R. Hegselmann and U. Krause, Opinion dynamics and bounded confidence: Models, analysis and simulation, Journal of Artificial Societies and Social Simulation, 5 (2002). Google Scholar

[21]

R. A. Holley and T. M. Liggett, Ergodic theorems for weakly interacting infinite systems and the voter model, The Annals of Probability, 3 (1975), 643-663.  doi: 10.1214/aop/1176996306.  Google Scholar

[22]

C. La Rocca, L. A. Braunstein and F. Vázquez, The influence of persuasion in opinion formation and polarization, Europhys. Letters, 106 (2014), 40004. doi: 10.1209/0295-5075/106/40004.  Google Scholar

[23]

B. Latané, The psychology of social impact, American Psychologist, 36 (1981), 343-356.   Google Scholar

[24]

J. Lorenz, Continuous opinion dynamics under bounded confidence: A survey, International Journal of Modern Physics C, 18 (2007), 1819-1838.  doi: 10.1142/S0129183107011789.  Google Scholar

[25]

M. Mäs and A. Flache, Differentiation without distancing. Explaining bi-polarization of opinions without negative influence, PloS one, 8 (2013), e74516. Google Scholar

[26]

P. Milgrom and I. Segal, Envelope theorems for arbitrary choice sets, Econometrica, 70 (2002), 583-601.  doi: 10.1111/1468-0262.00296.  Google Scholar

[27]

R. Ochrombel, Simulation of Sznajd sociophysics model with convincing single opinions, International Journal of Modern Physics C, 12 (2001), 1091-1092.  doi: 10.1142/S0129183101002346.  Google Scholar

[28] L. Pareschi and G. Toscani, Interacting Multiagent Systems: Kinetic Equations and Monte Carlo Methods, Oxford University Press, Oxford, 2014.   Google Scholar
[29]

L. PedrazaJ. P. Pinasco and N. Saintier, Measure-valued opinion dynamics, M3AS: Mathematical Models and Methods in Applied Sciences, 30 (2020), 225-260.  doi: 10.1142/S0218202520500062.  Google Scholar

[30]

M. Pérez-Llanos, J. P. Pinasco and N. Saintier, Opinion attractiveness and its effect in opinion formation models, Phys. A, 559 (2020), 125017, 9 pp. doi: 10.1016/j.physa.2020.125017.  Google Scholar

[31]

M. Pŕez-LlanosJ. P. PinascoN. Saintier and A. Silva, Opinion formation models with heterogeneous persuasion and zealotry, SIAM Journal on Mathematical Analysis, 50 (2018), 4812-4837.  doi: 10.1137/17M1152784.  Google Scholar

[32]

F. Vazquez, N. Saintier and J. P. Pinasco, The role of voting intention in public opinion polarization, Phys. Rev. E, 101 (2020), 012101, 13pp.  Google Scholar

[33]

N. Saintier, J. P. Pinasco and F. Vazquez, A model for a phase transition between political mono-polarization and bi-polarization, Chaos: An Interdisciplinary Journal of Nonlinear Science, 30 (2020), 063146, 17 pp. doi: 10.1063/5.0004996.  Google Scholar

[34]

J. P. PinascoV. Semeshenko and P. Balenzuela, Modeling opinion dynamics: Theoretical analysis and continuous approximation, Chaos, Solitons & Fractals, 98 (2017), 210-215.  doi: 10.1016/j.chaos.2017.03.033.  Google Scholar

[35]

F. Slanina and H. Lavicka, Analytical results for the Sznajd model of opinion formation, The European Physical Journal B, 35 (2003) 279–288. doi: 10.1140/epjb/e2003-00278-0.  Google Scholar

[36] D. W. Stroock, Probability Theory, An Analytic View, Cambridge University Press, 1993.   Google Scholar
[37]

K. Sznajd-Weron and J. Sznajd, Opinion evolution in closed community, International Journal of Modern Physics - C, 11 (2000), 1157-1165.  doi: 10.1142/S0129183100000936.  Google Scholar

[38]

G. Toscani, Kinetic models of opinion formation, Communications in Mathematical Sciences, 4 (2006), 481-496.  doi: 10.4310/CMS.2006.v4.n3.a1.  Google Scholar

[39]

C. Villani, Topics in optimal transportation, Grad.Studies in Math., American Mathematical Soc., (2003). doi: 10.1090/gsm/058.  Google Scholar

Figure 1.  Evolution of the opinion of 10 agents (blue) from a homogeneous population of $ N = 1000 $ agents interacting according the interaction rule (16) studied in this paper (left) or the interaction rule (22) considered in [2,38] (right). The red dashed line indicates the theoretical limit opinion in both cases ($ m_\infty\approx -0.35 $ for interaction rule (16), (left), and $ \tilde m_\infty\approx 0.41 $ for interaction rule (22), (right)) The early evolution is shown in inset
Figure 2.  Evolution of $ \ln(\max\,w-\min\,w) $, where the $ \max $ and $ \min $ are taken on the support of the distribution of opinions, for interaction rule (16) studied in this paper (left), and interaction rule (22) considered in [2,38] (right)
Figure 3.  Evolution of the opinion of 10 agents (blue) from a population of $ N = 1000 $ agents with $ \alpha_0 = 2\% $ (left) and $ \alpha_0 = 60\% $ (right) stubborn agents. The red dashed line indicates the theoretical limit opinion $ m_\infty = 1/2 $, and the blue dotted lines the opinion of the stubborn agents (half with opinion $ 1/4 $ and the other half with opinion $ 3/4 $). The early evolution is shown in inset
Figure 4.  Evolution of the distribution of $ (w,q) $ among the non-stubborn population during one simulation ($ w $ in the horizontal axis and $ q $ in the vertical axis) with $ \lambda(w) = (w-0.5)^2 + \varepsilon $, $ \varepsilon = 0.01 $. From left to right and top to bottom, figures show the distribution of $ (w,q) $ at time $ 1,500,1000,1500,3000,5000,10000,15000,30000 $
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