Credibility Governance: A Social Mechanism for Collective Self-Correction under Weak Truth Signals

TL;DR

Across settings with initial majority misalignment, observation noise and contamination, and misinformation shocks, CG outperforms vote-based, stake-weighted, and no-governance baselines, yielding faster recovery to the true state, reduced lock-in and path dependence, and improved robustness under adversarial pressure.

Abstract

Online platforms increasingly rely on opinion aggregation to allocate real-world attention and resources, yet common signals such as engagement votes or capital-weighted commitments are easy to amplify and often track visibility rather than reliability. This makes collective judgments brittle under weak truth signals, noisy or delayed feedback, early popularity surges, and strategic manipulation. We propose Credibility Governance (CG), a mechanism that reallocates influence by learning which agents and viewpoints consistently track evolving public evidence. CG maintains dynamic credibility scores for both agents and opinions, updates opinion influence via credibility-weighted endorsements, and updates agent credibility based on the long-run performance of the opinions they support, rewarding early and persistent alignment with emerging evidence while filtering short-lived noise. We evaluate CG in POLIS, a socio-physical simulation environment that models coupled belief dynamics and downstream feedback under uncertainty. Across settings with initial majority misalignment, observation noise and contamination, and misinformation shocks, CG outperforms vote-based, stake-weighted, and no-governance baselines, yielding faster recovery to the true state, reduced lock-in and path dependence, and improved robustness under adversarial pressure. Our implementation and experimental scripts are publicly available at https://github.com/Wanying-He/Credibility_Governance.

Figures (13)

• Figure 1: Comparison of the four governance mechanisms. (1) NG: Agents observe physical signals $\tilde{\pi}_k^{t}$. (2) SM: NG + social signal $\Theta_k^{t}$ in observation. (3) WS: SM + agent influence $w_i^t$ updated by $\Delta \pi_k^t$. (4) CG: WS + agent influence $w_i^t$ updated by $\Theta_k^{t}$.
• Figure 2: H1, system dynamics. Panel (a) shows convergence from an initially false-majority state. Panel (b) shows recovery after a misinformation shock (shaded window). CG reaches higher support for the true topic and recovers faster after shocks than WS, SM, and NG.
• Figure 3: H2, mechanism pathway. Panel (a) tracks the share of influence held by truth-aligned agents. Panel (b) shows the social signal of the true topic $\Theta_{\text{true}}$. Panel (c) shows cumulative physical progress $\pi_{\text{true}}$, with background phases indicating exploration, acceleration, and saturation. CG reallocates influence toward truth-aligned agents, strengthens the social signal for the true topic, and compounds into faster physical progress.
• Figure 4: H3, ablations. Each panel compares support for the true topic in Full CG against an ablated variant: (a) no credibility updates, (b) no anti-bubble penalty, (c) no early-mover bonus, and (d) reward base $\Delta\Theta_k \rightarrow \Delta\pi_k$. Removing any component degrades performance, and replacing $\Delta\Theta$ with $\Delta\pi$ most strongly destabilizes convergence.
• Figure 5: Sensitivity to Population Size. Comparison of CG's convergence for N=50, N=100 (Main Experiment), and N=300.