Abstract: Systems and methods for optimizing data-driven decision-making in multi-agent systems are described. The system may construct an equilibrium concept to capture multi-layer and/or k-level depth reasoning by agents. The system may determine best-response type conjectures for agents to interact with one another. In some examples, the system may include a machine or an algorithm interacting with a strategic agent (e.g., a human or an entity). The system may include methods for: (1) data-driven estimation and/or learning of conjectures and associated depth; and (2) data-driven design of algorithmic mechanisms for exploring the conjectural equilibrium (CE) space by influencing the strategic agent(s) behaviors through adaptively adjusting and estimating deployed strategies.