Binary Decisions in DAOs: Accountability and Belief Aggregation via Linear Opinion Pools

2026-03-30

AI Summary

This study addresses the challenges of aggregating subjective expert beliefs and ensuring accountability in decentralized autonomous organization (DAO) governance. The authors propose a smart contract–based incentive mechanism that integrates a game-theoretic model with a linear opinion pool to dynamically compute monetary transfers based on elicited expert preferences and ex post decision outcomes. The core innovation lies in its endogenous generation of opinion weights and its safety-with-deviation property, which collectively steer the aggregated signal toward the true state even when experts exhibit biased preferences. Theoretical analysis demonstrates that correct classification is achievable whenever experts’ budgets exceed a threshold that diminishes as their beliefs converge. Moreover, under aligned expert interests, the mechanism satisfies dominant-strategy incentive compatibility.

Abstract

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.

Problem

• DAO governance
• binary decision-making
• belief aggregation
• incentive compatibility
• linear opinion pools

Innovation

• Linear Opinion Pools
• Incentive Compatibility
• Belief Aggregation
• DAO Governance
• Binary Decision-Making