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：我们研究去中心化自治组织（DAO）治理委员会中的二元决策问题，其中专家代表组织在两个备选方案中进行选择。我们为这类委员会建立了信息结构模型，并形式化了区块链治理中的理想属性。我们提出一种机制，该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：对备选方案的特异性偏好，以及关于哪个方案更可能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来选择最佳方案，这被表述为一个分类问题。该机制收集偏好并据此计算货币转移支付，然后根据布尔结果进行额外的转移支付。对于利益一致的专家，该机制是占优策略激励相容的。对于利益不一致的专家，我们证明了安全偏离属性：没有专家能够通过转向他们相信成功概率更低的方案而获利。我们的主要结果表明，报告的加总结果可分解为特异性噪声和线性池化信念信号，该信号的符号与设计者的最优决策相匹配。池化权重由均衡策略内生产生，并且当每位专家的预算超过某个阈值时（该阈值随着专家信念趋同而降低），即可实现正确分类。