Organization Design for Complex Worlds

2026-07-18

AI Summary

This study investigates how horizontal complexity—the variation in actions required across similar tasks—affects optimal organizational team size, aiming to balance central coordination with local adaptability. By formally characterizing horizontal complexity for the first time and integrating Gaussian process modeling of local task states with spectral analysis of complexity structure, the authors develop an organizational grouping optimization model. This framework reveals that task variations unabsorbed by headquarters drive teams to expand, whereas variations effectively managed at headquarters induce team downsizing. The work establishes a quantitative relationship between horizontal complexity and optimal team size, offering a theoretically grounded and structured approach to organizational design in complex environments.

Abstract

I study the role of \emph{horizontal complexity} -- defined as the variation in actions that similar tasks require -- in organization design. A continuum of workers each choose an action to adapt to a local state that follows a Gaussian process across locations. Headquarters can group workers into \emph{teams}, simplifying the attention problem it faces in coordinating across the organization, at the cost of inhibiting adaptation. Tools from spectral theory identify how horizontal complexity affects team size: roughly speaking, variation left unresolved by headquarters expands teams, while variation concentrated along dimensions that headquarters absorbs through attention shrinks them.

Problem

• horizontal complexity
• organization design
• team size
• coordination
• adaptation

Innovation

• horizontal complexity
• organization design
• spectral theory
• team size
• Gaussian process