In a microscopic setting, humans behave in rich and unexpected ways. In a macroscopic setting, however, distinctive patterns of group behavior emerge, leading statistical physicists to search for an underlying mechanism. In this talk, I will discuss mathematical models that may explain these macroscopic patterns in competing ideas in the hopes of discerning how group opinions form, which is important for understanding human behavior in a variety of contexts, from presidential elections to jury verdicts.

To do this, I first introduce a general contagion-like model for competing opinions that includes dynamic resistance to alternative opinions. I show that this model can describe candidate vote distributions, spatial vote correlations, and a slow approach to opinion consensus with sensible parameter values. This modeling framework can help determine which individual behaviors are key to producing the observed group behavior. The aforementioned empirical patterns, previously understood using distinct models, may be different aspects of human behavior that can be captured by a more unified model. I conclude with some initial work on extending mechanistic descriptions to juries, where my collaborators and I find unexpected statistical patterns that can lend insight into how group decisions emerge from complex individual interactions.