Epistemic Filtering and Collective Hallucination: A Jury Theorem for Confidence-Calibrated Agents
arXiv:2602.22413v1 Announce Type: new Abstract: We investigate the collective accuracy of heterogeneous agents who learn to estimate their own reliability over time and selectively abstain from voting. While classical epistemic voting results, such as the \textit{Condorcet Jury Theorem} (CJT), assume fixed participation, real-world aggregation often benefits from allowing agents to say ``I don't know.'' We propose a probabilistic framework where agents engage in a \textit{calibration} phase, updating beliefs about their own fixed competence, before facing a final confidence gate that determines whether to vote or abstain. We derive a non-asymptotic lower bound on the group's success probability and prove that this \textit{selective participation} generalizes the asymptotic guarantees of the CJT to a sequential, confidence-gated setting. Empirically, we validate these bounds via Monte Carlo simulations. While our results are general, we discuss their potential application to AI safety,
arXiv:2602.22413v1 Announce Type: new Abstract: We investigate the collective accuracy of heterogeneous agents who learn to estimate their own reliability over time and selectively abstain from voting. While classical epistemic voting results, such as the \textit{Condorcet Jury Theorem} (CJT), assume fixed participation, real-world aggregation often benefits from allowing agents to say ``I don't know.'' We propose a probabilistic framework where agents engage in a \textit{calibration} phase, updating beliefs about their own fixed competence, before facing a final confidence gate that determines whether to vote or abstain. We derive a non-asymptotic lower bound on the group's success probability and prove that this \textit{selective participation} generalizes the asymptotic guarantees of the CJT to a sequential, confidence-gated setting. Empirically, we validate these bounds via Monte Carlo simulations. While our results are general, we discuss their potential application to AI safety, outlining how this framework can mitigate \textit{hallucinations} in collective LLM decision-making.
Executive Summary
This article proposes a novel framework for collective decision-making by heterogeneous agents who learn to estimate their reliability over time and selectively abstain from voting. By introducing a calibration phase and confidence gate, the authors derive a non-asymptotic lower bound on the group's success probability, generalizing the asymptotic guarantees of the Condorcet Jury Theorem. The framework has potential applications in AI safety, mitigating hallucinations in collective LLM decision-making. Monte Carlo simulations validate the proposed bounds, demonstrating the framework's effectiveness. The article contributes to the literature on epistemic voting and collective decision-making, offering new insights into the importance of selective participation and confidence calibration in achieving accurate group outcomes.
Key Points
- ▸ The authors propose a probabilistic framework for collective decision-making by heterogeneous agents who learn to estimate their reliability over time.
- ▸ The framework introduces a calibration phase and confidence gate, enabling selective participation and improving group accuracy.
- ▸ The authors derive a non-asymptotic lower bound on the group's success probability, generalizing the asymptotic guarantees of the Condorcet Jury Theorem.
Merits
Strength
The article contributes to the literature on epistemic voting and collective decision-making, offering new insights into the importance of selective participation and confidence calibration in achieving accurate group outcomes.
Strength
The proposed framework has potential applications in AI safety, mitigating hallucinations in collective LLM decision-making.
Demerits
Limitation
The article assumes that agents can accurately estimate their own reliability over time, which may not be a realistic assumption in practice.
Limitation
The proposed framework may be computationally intensive, requiring significant computational resources for large-scale applications.
Expert Commentary
The article represents a significant advancement in the field of collective decision-making, offering a novel framework that addresses the challenges of heterogeneous agents and selective participation. The proposed framework has far-reaching implications for AI safety, policy-making, and governance. However, as with any novel framework, there are limitations and potential challenges that must be addressed. For example, the assumption that agents can accurately estimate their own reliability over time may not be realistic, and the proposed framework may be computationally intensive. Nevertheless, the article's contributions to the literature on epistemic voting and collective decision-making are substantial, and its implications for AI safety and policy-making are significant.
Recommendations
- ✓ Future research should focus on developing more realistic models of agent behavior and reliability estimation, as well as exploring the computational efficiency of the proposed framework.
- ✓ The article's findings have implications for policy-making and governance, and policymakers should consider the importance of selective participation and confidence calibration in achieving accurate group outcomes.
