Grand societal issues such as climate change and technological disruption challenge all industry sectors, including tourism. To cope with these challenges, new sustainable business models that not only rely on data-driven technologies but also require new ways of collaboration beyond disciplines and sectors by facilitating the overall conception of transdisciplinarity are essential. One potential way to combine all these requirements is computational social sciences. As a discipline-crossing approach, it should be anchored within tourism education to train the future workforce and experts necessary to realize the needed transformation. Thus, this study explores the status quo of tourism curricula in higher educational institutions in Austria through the lens of computational social sciences. In doing so, a set of core modules of computational social sciences content was developed as an analytical framework. The results show that there is still a significant gap between the demands of the tourism industry and the offered educational programs in Austria. The article concludes with insights on how to close the existing gap and some suggestions for possible foundational steps to support the transformation.

Decision makers in the tourism sector deal with various issues and need high-quality information to support their decisions. We propose a data-centric approach that analyses historical point of interest (POI) check-in data to determine parameters for an Agent Based Model (ABM). ABM simulation is then run multiple times to simulate possible outcomes in terms of the tourist flow. We have tested the proposed approach on the city of Salzburg using check-in data from Salzburg Card users across 29 POIs. These data were used to parameterize the ABM model with the number of people, the number of POIs a person visits per day, and the preference for selecting POIs to visit. The simulation was performed in GAMA ABM platform and the spatial environment was based on buildings and roads from OpenStreetMap (OSM). Simulation for the duration of 1 day has been repeated 50 times to generate POI visiting patterns. The simulation results have been compared to the ground truth data for the same day and they show that the approach can recreate the long-term pattern of POI visits, but has over-estimated several POIs that had lower visitor counts on that specific day.

Agent-based modelling (ABM) is a computer-based system to simulate the interactions, relationships and behavior of individual agents in a defined spatial context. Due to its stochastic and heterogenic nature, the method has the potential to represent the complexity of the tourism system with a broad range of possible applications. In the context of visitor flow management for instance, ABM can function as a possible decision support tool for policy makers to better understand and evaluate the dynamics of future scenarios and proposed policy changes towards a more economically, socially and environmentally resilient tourism development altogether. The following paper discusses the potential and implications of agent-based models in tourism research with a complex system approach in regard to ABM’s inherent elements of agents, interactions and environment. It introduces the planned application of ABM in an ongoing project dealing with visitor flows in an urban as well as rural destination context and draws up possible implications for sustainable tourism development.