Information Design for Adaptive Organizations

This paper examines the optimal design of information sharing in organizations. Organizational performance depends on agents adapting to uncertain external environments while coordinating their actions, where coordination incentives and synergies are modeled as graphs (networks). The equilibrium strategies and the principal's objective function are summarized using Laplacian matrices of these graphs. I formulate a Bayesian persuasion problem to determine the optimal public signal and show that it comprises a set of statistics on local states, necessarily including their average, which serves as the organizational goal. When the principal benefits equally from the coordination of any two agents, the choice of disclosed statistics is based on the Laplacian eigenvectors and eigenvalues of the incentive graph. The algebraic connectivity (the second smallest Laplacian eigenvalue) determines the condition for full revelation, while the Laplacian spectral radius (the largest Laplacian eigenvalue) establishes the condition for minimum transparency, where only the average state is disclosed.