Coherence-Weighted Ranking (CWR): Designing Incentive-Correcting Algorithms to Reward Coherence Over Chaos in Digital Information Systems

Modern algorithms largely decide what people see by prioritizing immediate reactions such as clicks, comments, and shares. While effective for capturing attention, this approach unintentionally amplifies content that generates confusion, outrage, and instability, while undervaluing material that is clear, thoughtful, and useful over time. This paper introduces Coherence-Weighted Ranking (CWR), a ranking framework that corrects this incentive distortion by treating predictable chaos as a cost rather than a feature. Instead of judging truth or ideology, CWR adjusts amplification based on structural coherence, long-horizon user satisfaction, and friction signals that indicate breakdown rather than value. By shifting what systems reward—without censoring or restricting speech—CWR offers a practical, deployable method for improving digital information environments across social media, news, education, and AI-driven systems, demonstrating that healthier outcomes emerge when clarity and durability are incentivized over short-term reactivity.