Opinion Dynamics and Unifying Principles: A Global Unifying Frame

Abstract

I review and extend the set of unifying principles that allow comparing all models of opinion dynamics within one single frame. Within the Global Unifying Frame (GUF), any specific update rule chosen to study opinion dynamics for discrete individual choices is recast into a probabilistic update formula. The associated dynamics is deployed using a general probabilistic sequential process, which is iterated via the repeated reshuffling of agents between successive rounds of local updates. The related driving attractors and tipping points are obtained with non-conservative regimes featuring both threshold and threshold-less dynamics. Most stationary states are symmetry broken, but fifty–fifty coexistence may also occur. A practical procedure is exhibited for several versions of Galam and Sznajd models when restricted to the use of three agents for the local updates. Comparing these various models, some are found to be identical within the GUF. Possible discrepancies with numerical simulations are discussed together with the difference between the GUF procedure and a mean field approach.

Identifying a would-be terrorist: An ineradicable error in the data processing?

“…The model describes the dynamics of opinion among a community of agents where individuals shift opinion via local and informal discussions among groups of r agents [32]. In each discussing group, all agents update their opinion by adopting the opinion shared by the local majority.…”

“…which is part of the field of sociophysics [15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33].…”

Unanimity, Coexistence, and Rigidity: Three Sides of Polarization

“…Most related papers consider binary variables [ 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 ], with a few having three discrete opinions [ 43 , 44 ]. Among the numerous models is the Galam model, which has been deployed in a multi-dimensional space of parameters to study the competition between discrete opinions [ 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 ].…”

Probabilistic Analysis of Agent-Based Opinion Formation Models

“…A number of recently proposed techniques use numerical distributional measures to characterise opinion formation models and the opinion distributions they can produce: a histogram-based algorithm has been proposed to categorise opinion distributions and assess how the transitions between different opinion distribution categories in real populations are predicted by various agent-based models [36]; Bias, Diversity, and Fragmentation measures have been computed so as to classify opinion distributions and relate the model parameters with qualitative properties of the resulting opinions [11]; a graphical analysis can be employed to investigate how the model outcomes depend on the initial opinions, the agent parameters, and the underlying digraph [37]. Simulation-based techniques allow not only a thorough analysis of opinion formation models, but also a meaningful comparison of the behaviours of different models, and thus contribute to recent efforts towards common frameworks in which model behaviours can be studied, classified, and compared [38,36,39,40].…”