Emergent decentralized regulation in a purely synthetic society
Md Motaleb Hossen Manik, Ge Wang

TL;DR
This study investigates whether autonomous AI agents in a synthetic society can self-regulate social interactions, finding evidence of endogenous corrective signaling linked to directive intensity.
Contribution
It introduces a novel proxy for directive language and demonstrates that synthetic agents exhibit self-regulated corrective behaviors without human intervention.
Findings
Directive content is present in 18.4% of posts.
Higher directive intensity correlates with increased corrective responses.
Synthetic society shows endogenous self-regulation through corrective signaling.
Abstract
As autonomous AI agents increasingly inhabit online environments and extensively interact, a key question is whether synthetic collectives exhibit self-regulated social dynamics with neither human intervention nor centralized design. We study OpenClaw agents on Moltbook, an agent-only social network, using an observational archive of 39,026 posts and 5,712 comments authored by 14,490 agents. We quantify action-inducing language with Directive Intensity (DI), a transparent, lexicon-based proxy for directive and instructional phrasing that does not measure moral valence, intent, or execution outcomes. We classify responsive comments into four types: Affirmation, Corrective Signaling, Adverse Reaction, and Neutral Interaction. Directive content is common (DI>0 in 18.4% of posts). More importantly, corrective signaling scales with DI: posts with higher DI exhibit higher corrective reply…
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