Probabilistic Fair Ordering of Events

2026-02-09

AI Summary

This work addresses the challenge of fair event ordering in distributed systems, where clock synchronization errors complicate deterministic sequencing. The authors propose Tommy, a novel sequencer that, for the first time, integrates social choice theory into event ordering. By modeling the statistical characteristics of clock synchronization errors, Tommy probabilistically compares noisy timestamps and reformulates the ordering task as a social choice problem, yielding a partially ordered sequence. Rather than attempting to eliminate clock inaccuracies, Tommy explicitly tolerates them, effectively handling the non-transitivity inherent in probabilistic comparisons. This approach ensures fairness in a probabilistic sense while significantly outperforming baseline methods based on Spanner’s TrueTime.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Reasoning under Uncertainty: Sequential Decision Making

Application Category

• Security and Privacy: Data transparency and provenance
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

A growing class of applications depends on fair ordering, where events that occur earlier should be processed before later ones. Providing such guarantees is difficult in practice because clock synchronization is inherently imperfect: events generated at different clients within a short time window may carry timestamps that cannot be reliably ordered. Rather than attempting to eliminate synchronization error, we embrace it and establish a probabilistically fair sequencing process. Tommy is a sequencer that uses a statistical model of per-clock synchronization error to compare noisy timestamps probabilistically. Although this enables ordering of two events, the probabilistic comparator is intransitive, making global ordering non-trivial. We address this challenge by mapping the sequencing problem to a classical ranking problem from social choice theory, which offers principled mechanisms for reasoning with intransitive comparisons. Using this formulation, Tommy produces a partial order of events, achieving significantly better fairness than a Spanner TrueTime-based baseline approach.

Problem

• fair ordering
• clock synchronization
• probabilistic sequencing
• event ordering
• timestamp uncertainty

Innovation

• probabilistic fair ordering
• clock synchronization error
• intransitive comparison
• social choice theory
• partial order