Original
When coordination is avoidable: A monotonicity analysis of organizational tasks
Abstract
Organizations devote substantial resources to coordination, yet which tasks actually require it for correctness remains unclear. The problem is acute in multiagent AI systems, where coordination cost is directly measurable and can exceed the cost of the work itself. Distributed systems theory provides a precise criterion: Coordination is required when a task specification is nonmonotonic, meaning that as histories grow, new information can invalidate prior conclusions. Here we show that Thompson's classic taxonomy of interdependence maps to that criterion, yielding a decision rule for when coordination is required for correctness. We formalize the correspondence in a bridge theorem, apply the rule to 65 workflows from the American Productivity & Quality Center (APQC), and (with a calibrated large language model (LLM), 13,417 Occupational Information Network (O*NET tasks), and illustrate it in multiagent AI simulations. Under our decompositions, 74% of workflows and 42% of O*NET tasks are monotonic, implying that up to 24 to 57% of coordination spending is unnecessary for correctness.
中文
协调何时可以避免：组织任务的单调性分析
摘要
组织投入大量资源用于协调，但哪些任务实际上为了正确性而需要协调仍不明确。这一问题在多智能体AI系统中尤为突出，因为在那里协调成本可以直接测量，且可能超过工作本身的成本。分布式系统理论提供了一个精确标准：当任务规范是非单调的时，就需要协调；这意味着随着历史增长，新信息可能使先前的结论失效。本文表明，Thompson经典的相互依赖分类法可映射到该标准，从而给出一个判断何时为了正确性而需要协调的决策规则。我们用一个桥接定理形式化了这种对应关系，将该规则应用于美国生产力与质量中心（APQC）的65个工作流程，并借助经过校准的大语言模型（LLM）将其应用于13,417个职业信息网络（O*NET）任务，同时在多智能体AI模拟中加以说明。根据我们的分解，74%的工作流程和42%的O*NET任务是单调的，这表明高达24%至57%的协调支出对于正确性而言是不必要的。
关键词
协调、组织任务、单调性分析、多智能体系统、大语言模型、相互依赖、工作流程、分布式系统