We study binary decision-making in governance councils of Decentralized Autonomous Organizations (DAOs), where experts choose between two alternatives on behalf of the organization. We introduce an information structure model for such councils and formalize desired properties in blockchain governance. We propose a mechanism assuming an evaluation tool that ex-post returns a boolean indicating success or failure, implementable via smart contracts. Experts hold two types of private information: idiosyncratic preferences over alternatives and subjective beliefs about which is more likely to benefit the organization. The designer's objective is to select the best alternative by aggregating expert beliefs, framed as a classification problem. The mechanism collects preferences and computes monetary transfers accordingly, then applies additional transfers contingent on the boolean outcome. For aligned experts, the mechanism is dominant strategy incentive compatible. For unaligned experts, we prove a Safe Deviation property: no expert can profitably deviate toward an alternative they believe is less likely to succeed. Our main result decomposes the sum of reports into idiosyncratic noise and a linearly pooled belief signal whose sign matches the designer's optimal decision. The pooling weights arise endogenously from equilibrium strategies, and correct classification is achieved whenever the per-expert budget exceeds a threshold that decreases as experts' beliefs converge.

翻译：本文研究去中心化自治组织（DAO）治理委员会中的二元决策问题，专家代表组织在两个备选方案之间进行选择。我们为这类委员会构建了信息结构模型，并形式化定义了区块链治理中的理想属性。我们提出了一种机制，假设存在一个事后返回布尔值（表示成功或失败）的评估工具，该工具可通过智能合约实现。专家持有两类私人信息：对备选方案的特殊偏好，以及关于哪个方案更有利于组织的主观信念。设计者的目标是通过聚合专家信念选择最优方案，这被形式化为一个分类问题。该机制收集偏好并据此计算货币转移，随后根据布尔结果实施额外转移。对于与组织利益一致的专家，该机制满足占优策略激励相容性。对于利益不一致的专家，我们证明了安全偏离性质：任何专家不可能通过偏向其认为成功率较低的方案来获利。我们的主要结果将专家报告的总和分解为特殊噪声项与线性池化信念信号，该信号的符号与设计者的最优决策一致。池化权重由均衡策略内生成，当每位专家的预算超过一个随专家信念趋同而递减的阈值时，即可实现正确分类。