Networks of Human Behavior: Trading, Mobility, and Consumption

From financial markets to cities to the consumer economy, the systems that organize modern collective life are structured less by the categories used to describe them than by the behavioral networks that bind them. This dissertation reconstructs those networks from large-scale digital-trace data and uses them as the inferential substrate for three coordinated problems: identifying the mechanisms that distort social learning on trading platforms (Aim~1), recovering causal effects of urban policies under mobility-mediated interference (Aim~2), and quantifying how brand-specific shocks propagate through shared customers to neighbors' revenue and equity returns (Aim~3). Making these networks visible is the precondition for an empirical science of human collective life --- and for the institutional, policy, and platform decisions that increasingly depend on understanding it.