Beyond Byzantine: An Organizational Consensus Algorithm for Self-Interested Agents Under Information Asymmetry

2026-07-30

AI Summary

This work addresses the limitations of traditional consensus protocols, which rely on a binary assumption of honest or Byzantine nodes and fail to capture the strategic, self-interested behavior of departmental agents within organizations under information asymmetry. To overcome this, the paper proposes an Organizational Consensus Algorithm (OCA) that models inter-departmental coordination as a dynamic game of incomplete information. OCA integrates internal token staking, an anomaly-triggered challenge mechanism, and confidence-weighted consensus rules to incentivize truthful reporting and mitigate structural bias. By incorporating insights from mechanism design, the algorithm achieves efficient decision-making despite asymmetric information. Simulation results demonstrate that OCA significantly reduces coordination overhead and improves information reporting rates across varying organizational scales, while guaranteeing bounded welfare loss—thereby validating its effectiveness and scalability.

Abstract

Traditional distributed consensus protocols classify nodes as either honest-but-faulty or actively malicious (Byzantine). However, in organizational structures, departmental agents rarely fit this binary. Instead, they exhibit bounded rationality and self-interested preferences while operating under asymmetric information. This paper presents the Organizational Consensus Algorithm (OCA), a mechanism design framework tailored for internal negotiation and decision coordination. OCA models inter-departmental conflict as an incomplete information dynamic game, integrating internal token staking, an exception-triggered challenge mechanism, and confidence-weighted consensus rules. Rather than enforcing instantaneous total ordering, OCA leverages a retrospective penalty system driven by delayed verifiable outcomes to deter structural bias and reduce exhaustive coordination overhead. A Python simulation prototype was developed to evaluate OCA. Across independent trials with varying organizational scales, OCA reports lower coordination overhead, higher informative reporting rates, and bounded welfare loss in noisy environments. Crucially, these results remain conditional on the stated simulation model and do not by themselves establish a general truthful equilibrium.

Problem

• Byzantine consensus
• self-interested agents
• information asymmetry
• organizational coordination
• bounded rationality

Innovation

• Organizational Consensus Algorithm
• information asymmetry
• mechanism design
• bounded rationality
• delayed verifiable outcomes