We study the pattern of correlations across a large number of behavioral regularities, with the goal of creating an empirical basis for more comprehensive theories of decision-making. We elicit 21 behaviors using an incentivized survey on a representative sample (n = 1;000) of the U.S....

We introduce DOSE - Dynamically Optimized Sequential Experimentation - and use it to estimate individual-level loss aversion in a representative sample of the U.S. population (N = 2;000). DOSE elicitations are more accurate, more stable across time, and faster to administer than standard methods....

This paper proposes a decision-theoretic framework for experiment design. We model experimenters as ambiguity-averse decision-makers, who make trade-offs between subjective expected performance and robustness. This framework accounts for experimenters' preference for randomization, and clarifies...

We leverage a large-scale incentivized survey eliciting behaviors from (almost) an entire university student population, a representative sample of the U.S. population, and Amazon Mechanical Turk (MTurk) to address concerns about the external validity of experiments with student participants....

We introduce DOSE - Dynamically Optimized Sequential Experimentation - to elicit preference parameters. DOSE starts with a model of preferences and a prior over the parameters of that model, then dynamically chooses a customized question sequence for each participant according to an...