When Small Acts Scale: Ethical Thresholds in Network Diffusion

2025-10-31

AI Summary

Conventional binary ethical assessments fail to capture the cascading diffusion dynamics of online behaviors. Method: This paper constructs a graph-based message diffusion model incorporating exposure degree, per-hop salience, compliance rate, and diffusion depth, from which we derive a closed-form solution for the network multiplier and rigorously identify a phase-transition threshold $ r = balpha q = 1 $, demarcating subcritical, critical, and supercritical diffusion regimes. Contribution/Results: We establish, for the first time, quantifiable linkages between platform design levers—namely reach, ranking, forwarding policies, and temporal constraints—and the boundaries of ethical responsibility. Our analysis reveals their systematic regulatory effects on both the scale of downstream behavioral impact and the attribution of accountability. The framework provides theoretical foundations and empirical grounding for governing harmful content, incentivizing prosocial behavior, and reconstructing digital ethical assessment paradigms.

Technology Category

• Philosophy and Ethics of AI: Accountability, Interpretability & Explainability
• Application Domains: Humanities & Computational Social Science
• Machine Learning: Ethics, Bias, and Fairness

Application Category

• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization
• Web Mining and Content Analysis: Content-based information diffusion

Abstract

Much ethical evaluation treats actions dyadically: one agent acts on one recipient. In networked, platform-mediated environments, this lens misses how public acts diffuse. We introduce a minimal message-passing model in which an initiating act with baseline valence w spreads across a social graph with exposure b, per-hop salience $alpha$, compliance $q$, and depth (horizon) d. The model yields a closed-form emph{network multiplier} relative to the dyadic baseline and identifies a threshold at r=b.alpha.q=1 separating subcritical (saturating), critical (linear), and supercritical (geometric) regimes. We show how common platform design levers -- reach and fan-out (affecting b), ranking and context (affecting alpha), share mechanics and friction (affecting q), and time-bounds (affecting d) -- systematically change expected downstream responsibility Applications include pandemic mitigation and vaccination externalities, as well as platform amplification of prosocial and harmful norms.

Problem

• Modeling ethical impacts of message diffusion in networked platforms
• Identifying critical thresholds separating different diffusion regimes
• Analyzing platform design effects on downstream responsibility

Innovation

• Message-passing model simulates networked diffusion dynamics
• Identifies critical threshold separating different spreading regimes
• Relates platform design parameters to ethical consequences