Machine Learning Dynamics
Extending machine learning and natural language processing methods to learn the events and latent processes that drive coupled online-offline systems.
Psychology Models & Experiments
Advancing psychological models and lab experiments to test how prominent cognitive biases influence decision-making in coupled O2O systems.
Environmental & Behavioral Models
New behavioral models are needed to understand how the “physical interactions” between actors and environments generate offline and online events.
Continuum Models
Developing ODE and PDE mathematical models to understand the fundamental dynamics of coupled O2O systems.
Stochastic Models
Building parametric and non-parametric point process models to forecast the behavior of coupled O2O systems.
Causal Inference
Extend the potential outcomes framework to identify the average causal effects of spillover between online and offline domains.
About Us…
The O2O Research Group is a Multi-University Research Initiative (MURI) project sponsored by the US Air Force Office of Scientific Research.
O2O is shorthand for “online-to-offline” behavioral spillover. While O2O events seem to be growing in frequency and magnitude, the dynamics of how spillover operates and the causal mechanisms at play are poorly understood.
This project brings together anthropologists, psychologists, experimental design experts, computer scientists and mathematicians to tackle the challenging problems of learning the dynamics o and detecting causal pathways in coupled O2O systems.
This webpage is dedicated to describing and disseminating research by the O2O Research Group.
