AI 中文总结
针对信息不对称下自利智能体的组织架构问题，提出组织共识算法（OCA），通过机制设计框架在Python模拟中验证其可降低协调开销、提升信息报告率。
AI 中文摘要
传统分布式共识协议将节点分为诚实故障型或主动恶意型（拜占庭节点），但在组织架构中，部门智能体很少符合这种二元划分，而是在信息不对称下表现出有限理性和自利偏好。本文提出组织共识算法（Organizational Consensus Algorithm, OCA），一种专为内部协商与决策协调设计的机制设计框架。OCA将部门间冲突建模为不完全信息动态博弈，整合内部代币质押、异常触发的挑战机制以及置信度加权共识规则。OCA不强制即时全序，而是利用延迟可验证结果驱动的追溯惩罚机制，以遏制结构性偏见并降低 exhaustive 协调开销。开发了Python模拟原型对OCA进行评估，在不同组织规模的独立试验中，OCA展现出更低的协调开销、更高的信息报告率，以及在噪声环境下受限的福利损失。关键在于，这些结果仅在所述模拟模型条件下成立，本身并未确立一般真实均衡。
英文摘要
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.
Comments9 pages, 6 figures
