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Introduction A Motivating Example Estimation Using First-Order Conditions Modeling Behavioral Heterogeneity Preference Heterogeneity Random Disturbances Combining Preference Heterogeneity and Random Disturbances Discreteness of Choice Data and the First-Order Condition Approach Estimation Using Discrete Choice Models Basic Random Utility Framework Random Utility Model with Preference Heterogeneity Design of Experiments and Value Function Heterogeneity Multiplicity of Preferences Uncertainty in Structural Models Structural Models with Known Probabilities Nonlinear Probability Weighting Structural Models with Subjective Probabilities Uncertainty about Actions and Payoffs Uncertainty about the Beliefs of Others Measurement of Beliefs and Expectations Endogeneity of Stated Beliefs Model Validation Conclusion Content of Online Appendix References Acknowledgments