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.