Gonzalo Arrieta

A large body of work documents that complexity affects individuals’ choices, but the literature has remained mostly agnostic about why. We provide direct evidence that individuals use fundamentally different choice processes for complex and simple decisions. We hypothesize that individuals resort to “procedures”—cognitively simpler choice processes that we characterize as being easier to describe to another person—as the complexity of the decision environment increases. We test our hypothesis using two experiments, one with choices over lotteries and one with choices over charities. We exogenously vary the complexity of the decision environment and measure the describability of choice processes by how well another individual can replicate the decision-maker’s choices given the decision-maker’s description of how they chose. We find strong support for our hypothesis: Both of our experiments show that individuals’ choice processes are more describable in complex choice environments, which we interpret as evidence that decision-making becomes more procedural as complexity increases. We show that procedural decision-makers choose more consistently and exhibit fewer dominance violations, though we remain agnostic about the causal effect of procedures on decision quality. Additional secondary evidence suggests that procedural decision-making is a choice simplification that reduces the cognitive costs of decision-making. 

We seek to understand how the labor market decisions of the family adjust in response to plausibly exogenous health shocks. Family members might work less to provide caregiving, or work more in response to medical expenditures and loss of income by the ill individual. We use records of emergency department (ED) visits and hospitalizations to empirically determine the size of these effects. Using ED events we find evidence of intra-family insurance. By exploring how insurance varies by the severity of the health shock, we find that family labor supply responses decrease as the caregiving need increases. 

The dominant approach to welfare, revealed preference, is restricted to settings where the individual knows their preferences have been fulfilled. We use a choosing-for-others framework to experimentally study welfare when what the individual believes differs from what is actually true. 42% of participants see welfare as independent of beliefs; 22% see welfare as exclusively determined by beliefs; and 29% care about both beliefs and reality. Furthermore, the average participant values accurate beliefs. While there is large heterogeneity, our results suggest most people support the idea that welfare goes beyond beliefs, which can inform media regulation, informational policies, and government communication.

Screening decisions can be crucial in various situations, such as hiring workers, selling insurance, or designing policies. If a policy encourages uniform behavior, different people behave similarly, which makes learning about them from their behavior impossible. Choosing whether to screen can be difficult because it requires recognizing and trading off the benefits of information with the costs of getting it. We investigate whether people solve this trade-off optimally and what causes their mistakes. We design an online experiment that simulates a hiring scenario with an initial trial task. Participants make two decisions: first, they select a trial task, which can reveal candidates' quality at a small cost, and then choose which candidate to hire. We show that most participants choose the suboptimal task that does not reveal the candidates' quality, and this mistake persists even with experience and feedback. We test for the mechanisms and show that insufficient screening is driven by the failures to anticipate inference and to plan the full strategy.