Success-driven opinion formation determines social tensions

Abstract

Polarization is common in politics and public opinion. It is believed to be shaped by media as well as ideologies, and often incited by misinformation. However, little is known about the microscopic dynamics behind polarization and the resulting social tensions. By coupling opinion formation with the strategy selection in different social dilemmas, we reveal how success at an individual level transforms to global consensus or lack thereof. When defection carries with it the fear of punishment in the absence of greed, as in the stag-hunt game, opinion fragmentation is the smallest. Conversely, if defection promises a higher payoff and also evokes greed, like in the prisoner's dilemma and snowdrift game, consensus is more difficult to attain. Our research thus challenges the top-down narrative of social tensions, showing they might originate from fundamental principles at individual level, like the desire to prevail in pairwise evolutionary comparisons.

Keywords: Computational mathematics; Decision science; Social sciences.

Graphical abstract

Figure 1

Number of clusters of opinions for two extreme social tensions (fear in SH and greed in SD) Panel of sensitivity analysis on $\left(ϵ,\beta \right)$ for two different games (SH and SD). A higher fragmentation is shown on the right heatmap (SD) when β and ϵ are sufficiently high to inject success-driven dynamics in the model.

Figure 2

Increase in number of clusters of opinions when comparing different social tensions (S and T values of the game) Panel of sensitivity analysis on $\left(ϵ,\beta \right)$ showing increase in fragmentation when comparing two pairs of games (PD w.r.t. SH, and SD w.r.t. PD). Both heat-maps show a clear increase in the number of opinion clusters when values of S and T are higher and, therefore, a greedy and fearless population is defined.

Figure 3

Evolution of opinions for different social tensions Opinions’ evolution for three parameters combinations of the game (SH with $S=-1,T=0$, PD with $S=-0.1,T=1.1$, and SD with $S=1,T=2$) until time-step 500 (although simulations are run until $5,000$ steps). Values for success-driven OD model are $ϵ=0.7,\beta =5$. In a fearful population (SH), agents achieve a consensus; but in a fearless and greedy population (SD), fragmentation of opinions is high.

Figure 4

Snapshots of the same social network structure at time-steps $t=0$ and $t=500$ showing the evolution of opinions for each node for SD (top) and SH (bottom) Nodes’ diameter is related to their degree while nodes’ color is the opinion value $o_i$. Main parameters for OD model are $ϵ=0.7,\beta =5$. At $t=500$, diversity in opinions (i.e., fragmentation) is shown for the nodes when playing SD ($S=1,T=2$). In contrast, all the nodes reach consensus when game is SH ($S=-1,T=0$).

Figure 5

Number of clusters of opinions when S and T parameters change Sensitivity analysis on $\left(S,T\right)$ for different confidence levels $ϵ=\left\{0.2,0.3\right\}$ and intensity of selection $\beta =\left\{0.5,1,2,5\right\}$. One can see a higher opinion fragmentation when S and T increases (toward top-right corner of the heat-maps). When success-driven importance increases (higher β), opinion is also more fragmented.