Beyond the Matrix: Experimental Approaches to Studying Social-Ecological Systems

Studying social-ecological systems, in which agents interact with each other and their environment is a challenging but important task. In such systems, the environment shapes the agents' experience and actions, and in turn collective action of agents changes social and physical aspects of the environment. Experimental and computational approaches to studying complex social behaviors and processes have come a long way since the 1950s. However, emphasis on directly mapping the paradigms that are most computationally convenient (matrix games) to their direct analogs in the laboratory may have impoverished experimental design. Modern artificial intelligence (AI) techniques provide new avenues to model complex social worlds, preserving more of their characteristics. These techniques can be fed back to the laboratory where they help to design experiments in more complex social situations without compromising their tractability for computational modeling. This novel approach can help researchers bring together insights from human cognition, sustainability, and AI, to tackle real world problems of social ecological systems such as climate change, pandemics, and conflict resolution.