The first chapter, joint with Dr. Shachar Kariv and Professor Erkut Ozbay, compares thepredictive performance of a standard economic model to a variety of machine learning models by presenting nearly 1,000 subjects with a consumer decision problem – the selection of a bundle of contingent commodities from a budget set. Our dataset allows us to compare predictions at the individual level and relate them to the consistency of individual decisions with revealed preference axioms. Using dual measures of completeness and restrictiveness from Fudenberg et al. (2022a,b), we show that the economic model outperforms all machine learning models, with a wider margin as choices align more with an underlying preference ordering. The second chapter, joint with Professor Emel Filiz-Ozbay and Erkut Ozbay, empirically investigates the consideration and choice functions behaviors of individuals under uncertainty. Our design elicits these functions by repeating the decisions repeatedly questioning subjects in a rich lottery domain and, hence, allows subjects to reveal their stochastic or deterministic consideration and choice. Since most subjects act stochastically in both consideration and choice decisions, we focus on testing well-known axioms defined for such behavior. Our analysis includes individual-level testing of the logit model (Brady and Rehbeck (2016)), and the axioms of monotonic attention (Cattaneo et al. (2020)), and attention overload (Cattaneo et al. (2021)) for the consideration data. For the choice data, we test properties including the independence of irrelevant alternatives (Luce (1959)), regularity (Block et al. (1959)) and consistency with the attribute rule (Gul et al. (2014)). The third chapter, joint with Dr. Shachar Kariv and Professor Erkut Ozbay, extends work frmo the first chapter. We make use of rich individual-level data sets from three budgetary choice environments. The environments provide a strong test of both the intra-economic model comparisons, as well as a comparison between economic models and machine learning models. Overall, we find that the extension from two goods to three goods does not greatly reduce completeness, but does greatly increase the restrictiveness. Both standard and behavioral economic models see larger increases in restrictiveness compared to machine learning models, and a lower drop in completeness when moving from two goods to three goods. Surprisingly, there is no additional drop in completeness when moving from choice under risk to choice under ambiguity in this environment; the completeness and restrictiveness scores of all models are nearly identical across the two domains, and the minor differences that are present favor models under ambiguity. We interpret these results as favorable for standard economic models in rich choice environments: absent external factors, economic models with one parameter detailing risk preferences are sufficient to capture individual-level behavior of choice under risk and choice under ambiguity. Additionally, these models are more restrictive than machine learning models; along with the high completeness, this result indicates that the assumptions of EUT and SEU capture the regularities in choice under risk and ambiguity.