Modeling human behavior in strategic settings

Increasingly, electronic interactions between individuals are mediated by specialized algorithms. One might hope to optimize the relevant algorithms for various objectives. An aspect of online platforms that complicates such optimization is that the interactions are often strategic: many agents are involved, all with their own distinct goals and priorities, and the outcomes for each agent depend both on their own actions, and upon the actions of the other agents. My thesis is that human behavior can be predicted effectively in a wide range of strategic settings by a single model that synthesizes known deviations from economic rationality. In particular, I claim that such a model can predict human behavior better than the standard economic models. Economic mechanisms are currently designed under behavioral assumptions (i.e., full rationality) that are known to be unrealistic. A mechanism designed based on a more accurate model of behavior will be more able to achieve its goal. In the first part of the dissertation, we develop increasingly sophisticated data-driven models to predict human behavior in strategic settings. We begin by applying machine learning techniques to compare many existing models from behavioral game theory on a large body of experimental data. We then construct a new family of models called quantal cognitive hierarchy (QCH), which have even better predictive performance than the best of the existing models. We extend this model with a richer notion of nonstrategic behavior that takes into account features such as fairness, optimism, and pessimism, yielding further performance improvements. Finally, we perform some initial explorations into applying techniques from deep learning in order to automatically learn features of strategic settings that influence human behavior. A major motivation for modeling human strategic behavior is to improve the design of practical mechanisms for real-life settings. In the second part of the dissertation, we study an applied strategic setting (peer grading), beginning with an analysis of the question of how to optimally apply teaching assistant resources to incentivize students to grade each others' work accurately. We then report empirical results from using a variant of this system in a real-life undergraduate class.Science, Faculty ofComputer Science, Department ofGraduat