Simulations are increasingly employed to evaluate alternative planning strategies. With the number of model parameters and their interdependencies grow challenges regarding the validation and calibration of simulation systems. A major challenge lies in calibrating models that include emergent phenomena, as is a frequently stated feature of agent-based simulations. Here heterogeneous groups of agents are directly modeled to enable the consideration of agents' impact on the planning solution and its success. Difficulties in calibration arise as the system's behavior emerges from agents' individual decisions and actions, which cannot be fully observed. In the following we present a novel approach for calibrating agent input parameter values of agent-based simulations for decision support. The approach is based on a game-theoretic model representing an approximation of the dynamic simulation system. Its performance is tested in a meta-simulation framework on the example of a market simulation with a monopolist supplier. The meta-simulation setting allows us to compare calibration results to actual input parameters. The presented work provides an outline for efficient calibration of agent-based simulations in general using a game-theoretic model.