  This work considers a novel information design problem and studies how the
craft of payoff-relevant environmental signals solely can influence the
behaviors of intelligent agents. The agents' strategic interactions are
captured by a Markov game, in which each agent first selects one external
signal from multiple signal sources as additional payoff-relevant information
and then takes an action. There is a rational information designer (principal)
who possesses one signal source and aims to influence the equilibrium behaviors
of the agents by designing the information structure of her signals sent to the
agents. We propose a direct information design approach that incentivizes each
agent to select the signal sent by the principal, such that the design process
avoids the predictions of the agents' strategic selection behaviors. We then
introduce the design protocol given a goal of the designer which we refer to as
obedient implementability (OIL) and characterize the OIL in a class of obedient
sequential Markov perfect equilibria (O-SMPE). A design regime is proposed
based on an approach which we refer to as the fixed-point alignment that
incentivizes the agents to choose the signal sent by the principal, guarantees
that the agents' policy profile of taking actions is the policy component of an
O-SMPE and the principal's goal is achieved. We then formulate the principal's
optimal goal selection problem in terms of information design and characterize
the optimization problem by minimizing the fixed-point misalignments. The
proposed approach can be applied to elicit desired behaviors of multi-agent
systems in competing as well as cooperating settings and be extended to
heterogeneous stochastic games in the complete- and the incomplete-information
environments.

Multiagent Systems;Artificial Intelligence