I want it now so you cannot have it later: the role of impulsive choices in competitive environments - Howatt, Brian (Kansas State University, 2023)
People commonly prefer smaller-sooner rewards over larger-later rewards, referred to as impulsive choice. Although impulsive choices are strongly associated with negative behaviors, such as poor diet and exercise habits, substance abuse and gambling, larger delayed rewards are more uncertain than smaller immediate rewards regarding if and when they are delivered. The threat of losing out on delayed rewards may then motivate individuals to shift their preferences towards more immediately available goals. One source of uncertainty for delayed rewards is competition, where multiple individuals exclusively pursue limited resources. The impact of competitors may result in individuals selecting more impulsive choices to ensure a relatively greater acquisition of important resources compared to competitors, despite the environment originally incentivizing waiting. Therefore, the purpose of the current dissertation is to study the degree to which competition-based uncertainty increases impulsive choices above and beyond environmental uncertainty. To test this hypothesis, I conducted two experience-based decision-making experiments that manipulated various dimensions of competition that could potentially control participants’ waiting behaviors and performance, such as the visibility of a competitor and the visibility of the competitor’s cumulative rewards. Overall, the results provided weak evidence that competition influenced participants’ impulsive choices above environmental uncertainty, specifically by slightly increasing their likelihood of obtaining rewards rather than their tendency to select impulsive choices. These findings are more consistent with an economic perspective of choice behavior than an evolutionary perspective, and I discuss the theoretical and methodological implications of this novel line of research.
Keywords: Intertemporal choice; Competition; Dynamic decision making; Evolution; Bayesian; Video game