Naturalness as the Measure: Aligned Incentives

Laozi repeatedly praises naturalness—things working according to their nature—as the mark of wise governance. Applied to algorithmic politics, this suggests that sustainable systems align incentives with human nature rather than fighting it. Most algorithmic problems arise from misalignment: platforms incentivize engagement but humans want meaning; systems optimize for growth but communities want stability; algorithms prioritize speed but politics requires deliberation. Fighting these tensions requires constant enforcement. A Taoist approach would redesign incentives toward alignment: platforms that reward depth over breadth, algorithms that strengthen genuine community bonds, systems where moderator wellbeing follows from healthy discourse rather than exhausting moderation labor. This isn't naive—it requires understanding what humans naturally want and building toward it. The insight is that naturalness isn't anarchy; it means designing with rather than against human nature. When algorithmic systems feel forced and exhausting, they're misaligned. When they feel effortless, they've achieved wu wei. This principle suggests that sustainable algorithmic politics emerges from understanding what democratic discourse naturally needs and architecting systems that enable it as a byproduct of normal operation.