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Pairwise

Uses participants’ relative preferences between pairs of projects to compute an allocation of funds across the full set.

Pairwise allocation leverages relative preferences to streamline the cognitively taxing process of allocating resources among many projects. Participants compare pairs of projects (e.g., “A is better than B”) rather than evaluating all projects simultaneously or assigning absolute scores. These pairwise comparisons are aggregated into a graph structure where preferences “flow” through the network until reaching a steady-state global ranking. This global ranking determines how funds are distributed among the projects. Unlike traditional allocation mechanisms, pairwise preference simplifies decision-making and enables more granular and nuanced evaluations, especially for large communities.

First proposed in 2018 by Daniel Kronovet, Aron Fischer, and Jack du Rose from Colony. Pairwise was initially conceptualized as a variation of Google’s PageRank algorithm adapted for capital allocation. In 2023-2024, General Magic implemented the model for Optimism’s Retroactive Public Goods Funding (RetroPGF) rounds, iterating its design over several cycles.

Advantages

  • Cognitive Simplicity: Reduces mental load by breaking down complex allocation decisions into simple binary choices.
  • Preference Discovery: Helps surface high-quality but lesser-known projects by enabling direct comparisons regardless of reputation.
  • Signal Quality: Produces more nuanced distributions by focusing on relative value rather than absolute scores

Limitations & Risks

  • Conversion Challenge: Translating relative preferences into absolute allocations can feel opaque and reduce participant trust in outcomes.
  • Decision Volume: The minimum number of required comparisons for the outcome to represent global preference is not always clear.

Design Considerations

  • Evaluators Assignment: Balance workload and result accuracy. Options include random sampling, assigning evaluators a subset of choices, reputation-based weighting, prioritizing experienced evaluators, and quota-based assignments, capping evaluations per participant.
  • Tiered Sorting: Reduce decision volume while preserving accuracy. Consider pre-filtering, where low-signal projects are grouped separately before detailed comparisons, progressive ranking, allowing evaluators to refine rankings in multiple passes, and confidence thresholds, skipping redundant comparisons when rankings stabilize.
  • Information-Maximizing Pairs: Design adaptive pairing algorithms that prioritize showing project combinations that will yield the most valuable preference data.

Resources

Examples

General Magic's Optimism RetroPGF Rounds

The mechanism allowed Optimism badgeholders to compare projects in pairs, creating an intuitive way to signal preferences. Through these rounds, participants evaluated projects based on their relative merit rather than absolute ratings, which reduced cognitive effort and improved voter engagement. The system’s use was extended across rounds, with improvements such as more structured lists and better participant feedback loops to refine the allocation process further.

dOrg PairDrop

Pairdrop is a tool for running pairwise rounds. It assigns each voter a random sample of funding targets in pairs, asking participants to select the more deserving option from each pair. Behind the scenes, Pairdrop uses the BudgetBox algorithm, originally developed by Colony, to construct a preference graph and compute a normalized funding distribution across all targets. This approach ensures intelligent fund allocation even with limited voter turnout. Pairdrop also includes a Voter Scoring Script that integrates on and offchain data to weight votes based on participant activity.