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 is based on revealed preferences and thus 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 to be true differs from what is actually true. We find substantial heterogeneity. About 40% of participants see welfare as independent of beliefs; 10% see welfare impact only via beliefs; and 50% exhibit mixed behavior. Our results suggest most people support the idea that welfare goes beyond awareness, which may inform media regulation, informational policies, and government communication. 

Effective policymaking requires balancing the need for desirable outcomes with the ability to learn valuable information. However, when policies promote uniform behavior, they can hinder the ability to infer information from people's actions. We propose that individuals may select suboptimal policies because they fail to consider the effects of inference. To test this hypothesis, we conduct an online experiment that simulates a hiring scenario with an initial trial task. Participants make two decisions: selecting a trial task and then choosing which candidate to hire. The majority of participants opt for the suboptimal task that does not reveal the candidates' quality. This leads to suboptimal hires and lower payoffs because these participants do not know which candidate is better. Our findings suggest that the primary mechanism driving this behavior is the failure to anticipate inference. Our study underscores the significance of accounting for the effects of inference when designing policies.