Human decision-making involves navigating unbounded spaces of possible goals, subgoals, and action sequences. Yet, computational models typically assume pre-defined option sets. This creates a critical gap between the algorithms developed in cognitive science research on decision-making and the open nature of real-world decisions. We propose that option generation in open-ended settings operates as a search through structured decision space. Drawing on foraging theory, we hypothesized that option generation follows Lévy flight distributions, a pattern observed in both spatial foraging and memory retrieval. We found that the inter-generation time between consecutive responses in open-ended option generation problems approximated a Lévy distribution, while semantic distances demonstrated properties of heavy-tailed distributions. These findings reveal connections between action planning, information search, and memory retrieval, suggesting shared computational principles in how humans explore unbounded decision spaces.