This master’s thesis examines how players face ill-structured problems by interacting with a food systems game. The research investigates how a specific game design can nudge players to investigate multiple solutions to an ill-structured problem, rather than relying on a single approach. This work deciphers players’ epistemic development (i.e., the ability to reshape prior knowledge and construct newer ones, Sanchez, 2022) through their exploration of the game’s topic complexity. The study employs quantitative methods such as simple and multiple linear regression, correlation, clustering, and dynamic time series analysis to uncover patterns in play behavior. The analyses culminate in creating a complexity index that reflects players' epistemic development and their effectiveness in navigating complex scenarios within the game. This methodological framework provides a detailed set of indicators for each session, enabling the identification of epistemic development through the exploration of the game’s representation of food system complexity.