Bargaining games such as the Ultimatum and Principal-Agent games are commonly applied in describing political interactions such as negotiations between Congress and the president or Congressional oversight of the bureaucracy. Analysts typically assume that actors know perfectly each other's payoffs or that there is some constant private information about payoffs about which one can learn over the course of play. However, real bargaining involves uncertainty about players' own payoffs, and often this uncertainty cannot be reduced during the game through learning. Accounting for this uncertainty provides a richer theoretical model, more reasonable behavioral predictions, and solves the statistical "zero-likelihood" problem.;This dissertation consists of three essays. The first explores how to model uncertainty in the Ultimatum game and applies this to issues of experimental design and analysis. The second motivates the design and presents the results of an experiment in which subjects participate in the Ultimatum game, where the design and analysis depend on how uncertainty is modeled. The third extends the program by developing a strategic statistical model of the Principal-Agent game.
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