Dimensional Reductions in Collectively Adaptive Systems

 We explore the implications of dimensional reductions in the analysis of collectively adaptive systems, a type of complex system. Complex systems are characterized by non-linear dynamics, often in high dimensional spaces, with multiple parts interacting to create something larger than the sum of its parts. In collectively adaptive systems, individual cognitive strategies co-adapt with their social structures and changing problem landscapes. Without dimensional reductions, which could range from simple linear combinations and indices to more sophisticated representations in terms of latent variables, we cannot build a science of collective adaptation. However, dimensional reductions create a variety of inferential problems, which are the focus of this working group.