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

TL;DR

This paper models binary decision-making in DAO governance councils, proposing a mechanism that aggregates expert beliefs via linear opinion pools, ensuring incentive compatibility and correct classification under certain conditions.

Contribution

It introduces a formal information structure and a smart contract implementable mechanism for belief aggregation in DAO decision-making, with proven incentive properties.

Findings

The mechanism achieves correct classification when experts' beliefs are sufficiently aligned.

Pooling weights are derived endogenously from equilibrium strategies.

Experts cannot profitably deviate if they are unaligned, ensuring robustness.

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.