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Quantifying Bias & Redundancy in Complex Social & Biological Systems



3:30pm - 4:30pm ET


Meeting ID: 949 5727 1331
Passcode: 367411

The behavior of complex social and biological systems is constrained by emergent interactions between multiple decision-makers. Here, I discuss several data-driven projects to quantify the impact of bias and redundancy on the potential behavior of individual elements in art, science, and genetics. First, I introduce two investigations of systemic bias, which reflect how institutional structures can limit the development of professional careers based on gender. By mapping a network of artist exhibition careers, we reveal echo-chambers, which restrict exhibition opportunities for some artists and explain gender differences in access to the art auction market. Similar longitudinal career data in science suggest that significant gender differences in scientific productivity and impact are largely explained by different publishing career lengths and dropout rates, not bibliometric performance. Taken together, this quantitative perspective of gender inequality in science and art suggests that it is not enough to just increase the participation of women, we must break down the "glass fences," which systematically impede future options based on gender.

I then shift to the analysis of discrete multivariate systems in biology, and introduce an experimentally-validated mathematical framework that synthesizes both structure and dynamics in a weighted network representation. The resulting measures of canalization demonstrate the ubiquity of redundancy in biological models and provide a novel tool to increase causal explainability and control of biochemical systems. Finally, I discuss how data-driven frameworks can inform the design of platforms to promote technological innovation, policy diffusion, and collaboration.

About the Speaker(s)

Alexander Gates
Associate Research Scientist, Northeastern U

Alexander Gates is an associate research scientist at the Center for Complex Networks Research, Northeastern University. His research explores how interconnectedness shapes the social, scientific, and business world around us. Through a combination of tools and techniques from data science, network science, and computational social science, Alex studies the interplay between the temporal dynamics of individuals and the emergent structural patterns of societies.

Before arriving at Northeastern, Alex received a joint PhD degree in informatics (complex systems track) and cognitive science from Indiana University, Bloomington, an MSc from Kings College London in complex systems modeling and a BA in mathematics from Cornell University.


For more information, contact Ashley Parker at