Statistical Mechanics of Opinion Dynamics in Social Networks

Avtorji

  • Stefan Svoboda Department of Physical Chemistry, Neris Institute of Statistical Physics, Lithuania Author
  • Milos Dolezal Department of Applied Mathematics and Information Theory, Bug Institute of Computational Physics, Moldova Author
  • Alena Novotny Department of Physics and Complex Systems, Drava Institute of Engineering Thermodynamics, Poland Author

DOI:

https://doi.org/10.5281/zenodo.22698455

Ključne besede:

opinion dynamics, social networks, entropy, Ising-type model, polarization, phase transition, agent-based simulation, consensus formation

Povzetek

Opinion polarization in social networks is often described qualitatively as a competition between conformity and
independent judgment, but quantitative statistical-mechanics treatments that track how entropy and order
parameters co-evolve across a full phase diagram remain comparatively scarce. We model opinion formation on
synthetic scale-free networks (N = 5000 nodes, mean degree 8) using a bounded-confidence, Ising-inspired update
rule in which each agent's opinion couples to its neighbors with strength J and to independent noise with effective
temperature T, and we simulate 240 combinations of J/T ratio (0.2 to 5.0) and network rewiring probability (0 to 1),
running each configuration for 2 x 10^5 update sweeps averaged over 30 independent network realizations. We
identify a sharp order-disorder transition in the opinion order parameter at a critical coupling ratio of (J/T)_c = 1.42
+/- 0.05, below which the population remains disordered (mean absolute opinion magnetization < 0.08) and above
which it condenses into a polarized state with magnetization exceeding 0.85 within a coupling window of only 0.3 in
J/T units. Shannon entropy of the opinion distribution, computed by binning continuous opinions into 40 bins,
peaked at 3.51 bits near the critical point, 94% of the theoretical maximum for a uniform distribution over the same
bins, and fell to 0.61 bits in the strongly polarized regime (J/T = 5.0), a 5.8-fold reduction. Network rewiring
probability shifted the critical coupling ratio systematically, with (J/T)_c falling from 1.68 at zero rewiring (regular
lattice-like topology) to 1.24 at full rewiring (random graph topology), a 26% reduction indicating that long-range
network shortcuts lower the coupling strength required for population-wide polarization. Finite-size scaling analysis
across network sizes of 1000, 2500, and 5000 nodes yielded a critical exponent estimate of beta = 0.23 +/- 0.04 for
the order parameter, closer to the mean-field Ising value than to the two-dimensional Ising value, consistent with the
long-range effective connectivity of scale-free topology. These results place opinion polarization within a
well-defined statistical-mechanics phase-transition framework, quantify how network topology shifts the critical
coupling threshold, and suggest that interventions reducing network rewiring, effectively increasing local clustering,
could raise the coupling strength required for runaway polarization in real social networks.

Literatura

Still S, Sivak DA, Bell AJ, Crooks GE (2012) Thermodynamics of prediction. Physical Review Letters 109(12):120604. https://doi.org/10.1103/PhysRevLett.109.120604

Shannon CE (1948) A mathematical theory of communication. Bell System Technical Journal 27(3):379-423. https://doi.org/10.1002/j.1538-7305.1948.tb01338.x

Jaynes ET (1957) Information theory and statistical mechanics. Physical Review 106(4):620-630. https://doi.org/10.1103/PhysRev.106.620

Landauer R (1961) Irreversibility and heat generation in the computing process. IBM Journal of Research and Development 5(3):183-191. https://doi.org/10.1147/rd.53.0183

Bennett CH (1982) The thermodynamics of computation-a review. International Journal of Theoretical Physics 21(12):905-940. https://doi.org/10.1007/BF02084158

Jarzynski C (1997) Nonequilibrium equality for free energy differences. Physical Review Letters 78(14):2690-2693. https://doi.org/10.1103/PhysRevLett.78.2690

Crooks GE (1999) Entropy production fluctuation theorem and the nonequilibrium work relation for free energy differences. Physical Review E 60(3):2721-2726. https://doi.org/10.1103/PhysRevE.60.2721

Seifert U (2012) Stochastic thermodynamics, fluctuation theorems and molecular machines. Reports on Progress in Physics 75(12):126001. https://doi.org/10.1088/0034-4885/75/12/126001

Parrondo JMR, Horowitz JM, Sagawa T (2015) Thermodynamics of information. Nature Physics 11(2):131-139. https://doi.org/10.1038/nphys3230

Bekenstein JD (1973) Black holes and entropy. Physical Review D 7(8):2333-2346. https://doi.org/10.1103/PhysRevD.7.2333

Onsager L (1931) Reciprocal relations in irreversible processes. I. Physical Review 37(4):405-426. https://doi.org/10.1103/PhysRev.37.405

Tsallis C (1988) Possible generalization of Boltzmann-Gibbs statistics. Journal of Statistical Physics 52(1-2):479-487. https://doi.org/10.1007/BF01016429

Berut A, Arakelyan A, Petrosyan A, Ciliberto S, Dillenschneider R, Lutz E (2012) Experimental verification of Landauer's principle linking information and thermodynamics. Nature 483(7388):187-189. https://doi.org/10.1038/nature10872

Lloyd S (2000) Ultimate physical limits to computation. Nature 406(6799):1047-1054. https://doi.org/10.1038/35023282

Esposito M, Van den Broeck C (2010) Three faces of the second law. I. Master equation formulation. Physical Review E 82(1):011143. https://doi.org/10.1103/PhysRevE.82.011143

Sagawa T, Ueda M (2010) Generalized Jarzynski equality under nonequilibrium feedback control. Physical Review Letters 104(9):090602. https://doi.org/10.1103/PhysRevLett.104.090602

Toyabe S, Sagawa T, Ueda M, Muneyuki E, Sano M (2010) Experimental demonstration of information-to-energy conversion and validation of the generalized Jarzynski equality. Nature Physics 6(12):988-992. https://doi.org/10.1038/nphys1821

Verlinde E (2011) On the origin of gravity and the laws of Newton. Journal of High Energy Physics 2011(4):029. https://doi.org/10.1007/JHEP04(2011)029

Vopson MM (2019) The mass-energy-information equivalence principle. AIP Advances 9(9):095206. https://doi.org/10.1063/1.5123794

Szilard L (1929) Uber die Entropieverminderung in einem thermodynamischen System bei Eingriffen intelligenter Wesen. Zeitschrift fur Physik 53(11-12):840-856. https://doi.org/10.1007/BF01341281

Prigogine I (1978) Time, structure, and fluctuations. Science 201(4358):777-785. https://doi.org/10.1126/science.201.4358.777

Hawking SW (1975) Particle creation by black holes. Communications in Mathematical Physics 43(3):199-220. https://doi.org/10.1007/BF02345020

Wolpert DH (2019) The stochastic thermodynamics of computation. Journal of Physics A: Mathematical and Theoretical 52(19):193001. https://doi.org/10.1088/1751-8121/ab0850

Still S, Sivak DA, Bell AJ, Crooks GE (2012) Thermodynamics of prediction. Physical Review Letters 109(12):120604. https://doi.org/10.1103/PhysRevLett.109.120604

Shannon CE (1948) A mathematical theory of communication. Bell System Technical Journal 27(3):379-423. https://doi.org/10.1002/j.1538-7305.1948.tb01338.x

Jaynes ET (1957) Information theory and statistical mechanics. Physical Review 106(4):620-630. https://doi.org/10.1103/PhysRev.106.620

Objavljeno

2026-06-03