Madeline Reeve

Thesis Advisor: Dr. Heather Z. Brooks (Harvey Mudd College - Department of Mathematics)

Second Reader: Dr. Christopher E. Miles (University of Utah - Department of Mathematics)

Contact: mareeve@hmc.edu

Toward conditions for consensus of noisy bounded-confidence models

Models of opinion dynamics aim to describe the spread of opinions over time in a social network. However, many canonical models are deterministic and thus may fail to capture uncertainty present in social interactions. This work investigates the effect of adding noise into bounded-confidence models, a class of opinion dynamics models where agents are more likely to be influenced by opinions close to their own. In particular, we propose a noisy modification of the Deffuant–Weisbuch model. We prove that in this model all agents eventually adopt the same opinion; that is, our model converges to consensus.