UFAI

The uncertain future casts a long shadow over our choices. Countless real-world choices involve both risk (the outcome cannot be known with certainty) and delay (we have to wait before the risk is resolved), such as career, investment, and health decisions. Yet prior research has primarily studied risk or delay separately, rather than examining how they are related. The current project develops a unified theory of decision-making under both risk and delay—Affective Imagination Theory (AIT). According to AIT, the key question is: How do beliefs about our future emotions affect choices about uncertain future events? AIT considers two kinds of emotions—“consumption emotions” (pleasure and displeasure) after the outcome and “anticipatory emotions” (dread and savoring) while waiting. It models both their intensity (the vividness of imagining the future) and direction (dread as more powerful than savoring). AIT makes predictions that are theoretically novel and practically relevant. For example, it predicts that people will be more risk-avoidant for choices with delayed outcomes when losses are possible (e.g., investments), but more risk-seeking for choices with delayed outcomes when only gains are possible (e.g., lotteries). AIT’s core challenge and contribution is to combine rigorous formal models with advances in both cognition and emotion, yielding improved explanatory and predictive power over prior approaches. We use a wide array of methods to test AIT. We use both lab experiments and “experience sampling”—a method that gathers reported emotions from participants in the course of daily life—to study predicted and experienced emotions for delayed risky choices. Separately, we develop a method to estimate these psychological traits from lab data. We use these converging approaches to link individual differences in cognition to real-world decision-making. Finally, we will maximize practical impact by harnessing these insights to “nudge” our choices. It has been estimated that poor decisions—mostly about uncertain future events, such as health—underlie millions of deaths each year. We will not only identify strategies to improve individual decision-making, but to use our model to identify which individuals will be most helped by these strategies. Doing so can potentially encourage people to make better decisions about health (e.g., preventative testing), finances (e.g., saving for retirement), and business (e.g., taking calculated risks).