  This paper considers a network of agents, where each agent is assumed to take
actions optimally with respect to a predefined payoff function involving the
latest actions of the agent's neighbors. Neighborhood relationships stem from
payoff functions rather than actual communication channels between the agents.
A principal is tasked to optimize the network's performance by controlling the
information available to each agent with regard to other agents' latest
actions. The information control by the principal is done via a partial
observability approach, which comprises a static partitioning of agents into
blocks and making the mean of agents' latest actions within each block publicly
available. While the problem setup is general in terms of the payoff functions
and the network's performance metric, this paper has a narrower focus to
illuminate the problem and how it can be addressed in practice. In particular,
the performance metric is assumed to be a function of the steady-state behavior
of the agents. After conducting a comprehensive steady-state analysis of the
network, an efficient algorithm finding optimal partitions with respect to
various performance metrics is presented and validated via numerical examples.
