AI Insight
This study introduces a mathematical model of opinion dynamics that accounts for simultaneous trust and distrust in social relationships. The model shows that how people filter influence based on "net trust" and "uncertainty" creates four distinct behavioral regimes, leading to a counterintuitive hub-periphery reversal where highly connected individuals sometimes have less influence on collective beliefs when people avoid relational conflict. The effect was observed across synthetic networks and two empirical datasets, demonstrating that belief spread depends on both network structure and how people manage ambivalent relationships.
Why it matters
Understanding how trust-distrust ambivalence gates information flow could improve predictions of opinion formation in polarized societies and inform the design of social platforms. The finding that conflict-averse filtering can diminish influential nodes' impact challenges assumptions in social network analysis and misinformation modeling.
Understand the Science
⚠️ Preprint – Noch nicht peer-reviewed
Dieser Artikel wurde noch nicht von unabhängigen Experten begutachtet. Die Ergebnisse sind vorläufig und sollten mit Vorsicht interpretiert werden.
Abstract: In many social communities, individuals can simultaneously trust and distrust the same source, a feature standard opinion-dynamics models often ignore. We formalize this ambivalence with Gated Network Credence, in which each directed relationship encodes distinct trust and distrust assessments. These jointly determine “net trust” — the willingness to rely on a source — and “uncertainty” — the conflict between trust and distrust within the same relationship. Agents update beliefs only when net trust exceeds a threshold and uncertainty falls below another, yielding an effective influence graph whose topology drives long-run belief states. Sweeping both thresholds uncovers four regimes — Accommodating, Evaluative, Friction-averse, and Guarded — that differ in openness to trust and conflict. Under the modeled family of trust-distrust coupling and prestige-biased trust allocation, the model exhibits a hub-periphery reversal: in the Evaluative regime, high-degree agents contribute more strongly to the limiting belief, whereas in the Friction-averse regime, stringent uncertainty filtering can disproportionately remove hub-directed influence channels and reduce their contribution to the collective equilibrium. This conditional pattern recurs across synthetic network topologies and two empirical network evaluations. Our results show that belief dynamics depend not only on network structure but also on how relational ambivalence between trust and distrust gates interpersonal influence.