Synthetic Epistemic Communities: Collective Calibration and Trust in Multi-Agent Systems

The reliability of artificial intelligence systems depends not only on the calibration of individual agents but also on the mechanisms by which multiple agents exchange and align information. Prior work has shown that synthetic agents can maintain internal models of confidence and adaptively calibrate these estimates under stress. Yet, resilience at the individual level does not guarantee reliability in collective settings where agents must share, contest, and trust each other’s reports. In this study, we investigate the dynamics of synthetic epistemic communities: multi-agent systems in which communication, incentives, and governance shape epistemic performance. Through simulation experiments, we examine cooperative information sharing, the destabilizing effects of misaligned incentives, and the corrective role of auditing protocols. We evaluate these dynamics across standard calibration metrics (accuracy, ECE, MCE, Brier score, AUROC) and propose measures of collective reliability. Results show that while communication generally improves group performance, epistemic degradation emerges rapidly when agents are incentivized to exaggerate confidence. Auditing mechanisms partially restore reliability, highlighting the importance of structural safeguards in epistemic networks. These findings suggest that collective calibration and governance are necessary complements to individual resilience, and that multi-agent epistemics provide a critical testbed for advancing the study of machine metacognition and proto-consciousness.