When Coordination Is Avoidable: A Monotonicity Analysis of Organizational Tasks

TL;DR

This paper introduces a monotonicity-based criterion to determine when coordination is necessary in organizational and multi-agent AI tasks, potentially reducing unnecessary coordination costs.

Contribution

It formalizes the link between task interdependence and monotonicity, providing a decision rule and empirical validation across workflows and AI tasks.

Findings

74% of workflows are monotonic, reducing coordination needs.

42% of O*NET tasks are monotonic, implying significant unnecessary coordination.

Up to 24-57% of coordination costs could be avoided based on monotonicity.

Abstract

Organizations devote substantial resources to coordination, yet which tasks actually require it for correctness remains unclear. The problem is acute in multi-agent 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 non-monotonic, 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 APQC workflows and (with a calibrated LLM) 13,417 O*NET tasks, and illustrate it in multi-agent AI simulations. Under our decompositions, 74% of workflows and 42% of O*NET tasks are monotonic, implying that up to 24-57% of coordination spending is unnecessary for correctness.