Using Gamified Experiments to Tame Complexity: the case of the Schelling Model of Segregation

Abstract

This study employs gamified experiments to investigate and refine the Schelling Model of Segregation, a framework that demonstrates how individual preferences can lead to systemic segregation. Using a movement selection algorithm derived from a board game adaptation of the classical Schelling Model, the research examines player strategies aimed at minimizing segregation and maximizing happiness within a controlled environment. Rooted in greedy optimization, the model balances these objectives through a tunable parameter. Empirical data from gameplay is analyzed using Approximate Bayesian Computation, providing insights into player strategies and their alignment with systemic outcomes. The findings highlight the potential of gamification as a tool for engaging with complex social phenomena, enhancing agent-based models, and fostering participatory approaches in the study of emergent behaviors. This dual-layered framework incorporates collective decision-making into micro-macro models, addressing critiques of oversimplification and expanding their utility in educational and policy contexts.