Loss aversion is one of the most widely used concepts in behavioral economics. We conduct a large-scale interdisciplinary meta-analysis, to systematically accumulate knowledge from numerous empirical estimates of the loss aversion coefficient reported during the past couple of decades. We...

We measure individual-level loss aversion using three incentivized, representative surveys of the U.S. population (combined N = 3,000). We find that around 50% of the U.S. population is loss tolerant, with many participants accepting negative-expected-value gambles. This is counter to earlier...

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....

We examine 220 estimates of the present-bias parameter from 28 articles using the Convex Time Budget protocol. The literature shows that people are on average present biased, but the estimates exhibit substantial heterogeneity across studies. There is evidence of modest selective reporting in...

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....

Willingness to pay (WTP) and willingness to accept (WTA) a monetary amount for a lottery should be closely related. In data from an incentivized survey of a representative sample of 3,000 U.S. adults, we find that WTA and WTP for a lottery are, at best, weakly correlated. Across all respondents,...

We use four incentivized representative surveys to study the endowment effect for lotteries in 4,000 U.S. adults. We replicate the standard finding of an endowment effect-the divergence between Willingness to Accept (WTA) and Willingness to Pay (WTP), but document three new findings. First, we...

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...