To make profitable investment decisions, investors must know and understand their risks. They can learn about these risks in different ways. Evidence suggests that investors who learn from a ‘risk tool’ simulator perceive financial risk more accurately, feel more informed and confident, and thus take on more financial risk. We attempt a conceptual replication of these findings, exploring whether they extend from kind to wicked environments and to investors with some investment experience. We conducted four studies online, amounting to 3,804 participants, and observe that neither the risk tool nor any of the other risk communication interventions lead to different risk taking or to different subjective representations of risk relative to a control condition in which participants received no intervention. We evaluated several moderators, but none could explain the absence of an intervention effect, suggesting that the effect of risk tools and other interventions could be limited.

Two preregistered web studies are presented that explore the impact of experience on the tendency to accept recommended defaults. In each of the 100 trials, participants (n = 180, n = 165) could accept a recommended default option or choose a less attractive prospect. The location of the options (left or right) was randomly determined before each trial. Both studies compared two conditions. Under Condition Dominant, the default option maximized participants’ payoff in all trials. Under Condition Protective, the default option protected the participants from rare losses and maximized expected return but decreased payoff in most trials. The results reveal a tendency to accept the default in Condition Dominant but the opposite tendency in Condition Protective. This pattern was predicted by assuming that in addition to promoting specific actions, the presentation of the default changes the set of feasible strategies, and choice between these strategies reflects reliance on small samples of past experiences.

Information search is key to making decision from experience: exploration permits people to learn about the statistical properties of choice options and thus to become aware of rare but potentially momentous decision consequences. This registered report investigates whether and how people differ when making decisions from experience in isolation versus under competitive pressure, which may have important implications for choice performance in different types of choice environments: in “kind” environments without any rare and extreme events, frugal search is sufficient to identify advantageous options. Conversely, in “wicked” environments with skewed outcome distributions, rare but important events will tend to be missed in frugal search. One theoretical view is that competitive pressure encourages efficiency and may thereby boost adaptive search in different environments. An alternative and more pessimistic view is that competitive pressure triggers agency-related concerns, leading to minimal search irrespective of the choice environment, and hence to inferior choice performance. Using a sampling game, the present study (N = 277) found that solitary search was not adaptive to different choice environments (M = 14 samples), leading to a high choice performance in a kind and in a moderately wicked environment, but somewhat lower performance in an extremely wicked environment. Competitive pressure substantially reduced search irrespective of the choice environment (M = 4 samples), thus negatively affecting overall choice performance. Yet, from the perspective of a cost-benefit framework, frugal search may be efficient under competitive pressure. In sum, this report extends research on decisions from experience by adopting an ecological perspective (i.e., systematically varying different choice environments) and by introducing a cost-benefit framework to evaluate solitary and competitive search — with the latter constituting a challenging problem for people in an increasingly connected world.

Experiments have suggested that decisions from experience differ from decisions from description. In experience-based decisions, the decision makers often fail to maximise their payoffs. Previous authors have ascribed the effect of underweighting of rare outcomes to this deviation from maximisation. In this paper, I re-examine and provide further analysis on the effect with an experiment that involves a series of simple binary choice gambles. In the current experiment, decisions that bear small consequences are repeated hundreds of times, feedback on the consequence of each decision is provided immediately, and decision outcomes are accumulated. The participants have to learn about the outcome distributions through sampling, as they are not explicitly provided with prior information on the payoff structure. The current results suggest that the “hot stove effect” is stronger than suggested by previous research and is as important as the payoff variability effect and the effect of underweighting of rare outcomes in analysing decisions from experience in which the features of gambles must be learned through a sampling process.

Decision makers weight small probabilities differently when sampling them and when seeing them stated. We disentangle to what extent the gap is due to how decision makers receive information (through description or experience), the literature’s prevailing focus, and what information they receive (population probabilities or sample frequencies), our novel explanation. The latter determines statistical confidence, the extent to which one can know that a choice is superior in expectation. Two lab studies, as well as a review of prior work, reveal sample decisions to respond to statistical confidence. More strongly, in fact, than decisions based on population probabilities, leading to higher payoffs in expectation. Our research thus not only offers a more robust method for identifying description-experience gaps. It also reveals how probability weighting in decisions based on samples — the typical format of real-world decisions — may actually come closer to an unbiased ideal than decisions based on fully specified probabilities — the format frequently used in decision science.

Research on small repeated decisions from experience suggests that people often behave as if they underweight rare events and choose the options that are frequently better. In a pandemic, this tendency implies complacency and reckless behavior. Furthermore, behavioral contagion exacerbates this problem. In two pre-registered experiments (Ntotal = 312), we validate these predictions and highlight a potential solution. Groups of participants played a repeated game in one of two versions. In the basic version, people clearly preferred the dangerous reckless behavior that was better most of the time over the safer responsible behavior. In the augmented version, we gave participants an additional alternative abstracting the use of an application that frequently saves time but can sometimes have high costs. This alternative was stochastically dominated by the responsible choice option and was thus normatively irrelevant to the decision participants made. Nevertheless, most participants chose the new (“irrelevant”) alternative, providing the first clear demonstration of underweighting of rare events in fully described social games. We discuss public policies that can make the responsible use of health applications better most of the time, thus helping them get traction despite being voluntary. In one field demonstration of this idea amid the COVID-19 pandemic, usage rates of a contact tracing application among nursing home employees more than tripled when using the app also started saving them a little time each day, and the high usage rates sustained over at least four weeks.

According to prospect theory, people overweight low probability events and underweight high probability events. Several recent papers (notably, Hertwig, Barron, Weber & Erev, 2004) have argued that although this pattern holds for “description-based” decisions, in which people are explicitly provided with probability distributions over potential outcomes, it is actually reversed in “experience-based” decisions, in which people must learn these distributions through sampling. We reanalyze the data of Hertwig et al. (2004) and present a replication to determine the extent to which their phenomenon can be attributed to sampling error (a statistical rather than psychological phenomenon) versus underestimation of rare events (i.e., judgmental bias) versus actual underweighting of judged probabilities. We find that the apparent reversal of prospect theory in decisions from experience can be attributed almost entirely to sampling error, and are consistent with prospect theory and the two-stage model of decision under uncertainty (Fox & Tversky, 1998).

When deciding for others based on explicitly described odds and outcomes, people often have different risk preferences for others than for themselves. In two pre-registered experiments, we examine risk preference for others where people learn about the odds and outcomes by experiencing them through sampling. In both experiments, on average, people were more risk averse for others than for themselves, but only when the risky option had a higher expected value. Furthermore, based on a separate set of choices, we classified people as pro- or anti-social. Only those people classified as anti-social were more risk averse for others, whereas those classified as prosocial chose similarly for themselves and others. When the uncertainty was removed, however, all participants exhibited less anti-social behavior. Together, these results suggest that anti-social motives contribute to the observed limited risk taking for others and that outcome uncertainty facilitates the expression of these motives.

According to normative decision-making theories, the composition of a choice setshould not affect people’s preferences regarding the different options.This assumption contrasts with decades of research that have identified multiplesituations in which this principle is violated, leading to context effects.Recently, research on context effects has been extended to the domain ofexperience-based choices, where it has been shown that forgone outcomes fromirrelevant alternatives affect preferences — an accentuation effect. Morespecifically, it has been shown that an option presented in a situation in whichits outcomes are salient across several trials is evaluated more positively thanin a context in which its outcomes are less salient. In the present study, weinvestigated whether irrelevant information affects preferences as much asrelevant information. In two experiments, individuals completed a learning taskwith partial feedback. We found that past outcomes from non-chosen options,which contain no relevant information at all, led to the same accentuationeffect as did counterfactual outcomes that provided new and relevantinformation. However, if the information is entirely irrelevant (from optionsthat could not have been chosen), individuals ignored it, thus ruling out apurely perceptual account of the accentuation effect. These results providefurther support for the influence of salience on learning and highlight thenecessity of mechanistic accounts in decision-making research.

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 goal of obtaining information to improve classification accuracy can strongly conflict with the goal of obtaining information for improving payoffs. Two environments with such a conflict were identified through computer optimization. Three subsequent experiments investigated people’s search behavior in these environments. Experiments 1 and 2 used a multiple-cue probabilistic category-learning task to convey environmental probabilities. In a subsequent search task subjects could query only a single feature before making a classification decision. The crucial manipulation concerned the search-task reward structure. The payoffs corresponded either to accuracy, with equal rewards associated with the two categories, or to an asymmetric payoff function, with different rewards associated with each category. In Experiment 1, in which learning-task feedback corresponded to the true category, people later preferentially searched the accuracy-maximizing feature, whether or not this would improve monetary rewards. In Experiment 2, an asymmetric reward structure was used during learning. Subjects searched the reward-maximizing feature when asymmetric payoffs were preserved in the search task. However, if search-task payoffs corresponded to accuracy, subjects preferentially searched a feature that was suboptimal for reward and accuracy alike. Importantly, this feature would have been most useful, under the learning-task payoff structure. Experiment 3 found that, if words and numbers are used to convey environmental probabilities, neither reward nor accuracy consistently predicts search. These findings emphasize the necessity of taking into account people’s goals and search-and-decision processes during learning, thereby challenging current models of information search.