Myopia drives reckless behavior in response to over-taxation

Abstract

Governments use taxes to discourage undesired behaviors and encourage desired ones. One target of such interventions is reckless behavior, such as texting while driving, which in most cases is harmless but sometimes leads to catastrophic outcomes. Past research has demonstrated how interventions can backfire when the tax on one reckless behavior is set too high whereas other less attractive reckless actions remain untaxed. In the context of experience-based decisions, this undesirable outcome arises from people behaving as if they underweighted rare events, which according to a popular theoretical account can result from basing decisions on a small, random sample of past experiences. Here, we reevaluate the adverse effect of overtaxation using an alternative account focused on recency. We show that a reinforcement-learning model that weights recently observed outcomes more strongly than than those observed in the past can provide an equally good account of people’s behavior. Furthermore, we show that there exist two groups of individuals who show qualitatively distinct patterns of behavior in response to the experience of catastrophic outcomes. We conclude that targeted interventions tailored for a small group of myopic individuals who disregard catastrophic outcomes soon after they have been experienced can be nearly as effective as an omnibus intervention based on taxation that affects everyone.

The impact of experience on the tendency to accept recommended defaults

“…The first states that people tend to rely on the most recent past experiences. Spektor and Wulff (2021) show that, in certain settings, the aggregate choice rates can be captured by the reliance on small sample hypothesis even if only a minority of the decision-makers are "myopic" and exhibit a strong recency effect. This account can be quantified with models that assume (1) sequential adjustment of choice propensities and (2) large between-individual differences in adjustment speed.…”

On the descriptive value of the reliance on small-samples assumption

“…The apparent inconsistency between the evidence in favor of sequential adjustment models and the superiority of sampling models in the competitions has been previously explained in two different ways. The first explanation rests on the fact that because of the competitions' focus on predicting aggregate choice rates, the underlying processes that produce the choice rates can be misrepresented (Birnbaum, 2011;Regenwetter & Robinson, 2017;Spektor & Wulff, 2021;Wulff & van den Bos, 2018;Chen et al, 2021). Thus, it is possible that while individuals actually rely on an efficient sequential adjustment process with an individual-specific adjustment speed, on the aggregate this process is obscured.…”

The Future of Decisions From Experience: Connecting Real-World Decision Problems to Cognitive Processes

“…To accomplish this goal, we will demonstrate the importance of DfE independently from DfD and highlight the relevance of DfE for real-life decision-making. For example, studies using the DfE methodology and tasks have examined real-world problems, including terrorist attacks ( Yechiam, Barron, & Erev, 2005 ); emotional states ( Frey et al, 2014 ); taxation, punishment, law enforcement, and safety enhancement ( Spektor & Wulff, 2021 ; Teodorescu et al, 2021 ; Yakobi et al, 2020 ); pandemics and COVID-19 ( Erev et al, 2020 ; Plonsky et al, 2021 ); aging ( Frey et al, 2015 ); and clinical settings and populations ( Teodorescu & Erev, 2014 ; Yechiam, Busemeyer, et al, 2005 ).…”