Advancing Theoretical Understanding and Methodological Capacity in Psychology Through Social Simulation

Social simulations offer transferable assets, especially for early career scholars including PhD students. For instance, programming proficiency, particularly in Python, provides valuable preparation for industry careers in data science, technology, and consulting. Moreover, mastering one programming language significantly accelerates learning additional languages, creating a foundation for lifelong technical development. In addition, social simulations (agent-based simulations) are applied in a range of contexts because they can simulate the complex interactions between individuals and observe how system-level patterns emerge from their behaviours.

Both in academic and industry careers, social simulations are increasingly valuable. Despite this potential, social simulations and programming remain absent from most psychology training programmes. This gap leaves many students and early career scholars unable to address questions requiring computational approaches, limiting the field’s methodological diversity and theoretical reach. In addition, this limits their career options. Addressing this skills deficit represents a crucial investment for psychology and the employability of psychology graduates.

This project has two objectives. First, it aims to develop theoretical and methodological expertise in social simulations by acquiring advanced skills in social learning models and applying them to investigate how group-bonded indirect reciprocity drives the evolution of ingroup favouritism through simulations and experiments. Second, it seeks to build capacity in the field by disseminating social simulation methods to PhD students and early-career researchers, thereby fostering a broader community of scholars equipped to use these approaches in psychological research.