Complex Systems through the Lens of Data-Driven Models: Insights into Social Organizations, Collaborative Innovation, and Supply Chains

Abstract

In this talk, I will demonstrate how data-driven modeling can explain deviations between observations and expectations in complex systems. Specifically, I will discuss how simple pairwise interactions in social organizations can reveal structural imbalances and why perfectly balanced states are rarely observed. Next, I will examine how collaborations among groups of scientists and firms can either support or hinder innovation and present a unified modeling approach that can capture the various features of these systems. Finally, I will delve into supply chains and explore their structures and deviations from theoretical expectations. Throughout the talk, I will emphasize the importance of data and a bottom-up approach in comprehending complex systems and providing unique insights.