Expertise is a reliable cue for accuracy – experts are often correct in their judgments and opinions. However, the opposite is not necessarily the case – ignorant judges are not guaranteed to err. Specifically, in a question with a dichotomous response option, an ignorant responder has a 50% chance of being correct. In five studies, we show that people fail to understand this, and that they overgeneralize a sound heuristic (expertise signals accuracy) to cases where it does not apply (lack of expertise does not imply error). These studies show that people 1) tend to think that the responses of an ignorant person to dichotomous-response questions are more likely to be incorrect than correct, and 2) they tend to respond the opposite of what the ignorant person responded. This research also shows that this bias is at least partially intuitive in nature, as it manifests more clearly in quick gut responses than in slow careful responses. Still, it is not completely corrected upon careful deliberation. Implications are discussed for rationality and epistemic vigilance.

Bayesian statistics offers a normative description for how a person should combine their original beliefs (i.e., their priors) in light of new evidence (i.e., the likelihood). Previous research suggests that people tend to under-weight both their prior (base rate neglect) and the likelihood (conservatism), although this varies by individual and situation. Yet this work generally elicits people’s knowledge as single point estimates (e.g., x has a 5% probability of occurring) rather than as a full distribution. Here we demonstrate the utility of eliciting and fitting full distributions when studying these questions. Across three experiments, we found substantial variation in the extent to which people showed base rate neglect and conservatism, which our method allowed us to measure for the first time simultaneously at the level of the individual. While most people tended to disregard the base rate, they did so less when the prior was made explicit. Although many individuals were conservative, there was no apparent systematic relationship between base rate neglect and conservatism within each individual. We suggest that this method shows great potential for studying human probabilistic reasoning.

Previous research has focused on studying the endowment effect for transactions that take place in the present. Many real-world transactions, however, are delayed into the future (i.e., people agree to buy or sell, but the actual transaction does not materialize until a later time). Here we investigate how transaction timing affects the endowment effect. In five studies, we show that the endowment effect systematically increases as transactions are delayed into the future. Specifically, buying prices significantly decrease as the transaction is delayed, while selling prices remain constant, resulting in an amplified endowment effect (Experiment 1). This pattern is not produced by a discounting of the money involved in the transaction (Experiment 2), and it holds across different types of items (Experiment 3). We also show that the phenomenon cannot be explained by sellers anticipating becoming increasingly attached to the items over time (Experiment 4). Finally, we demonstrate that this increased endowment effect in the future holds in the field, in the context of a real market and with real transactions (Experiment 5).

Loss aversion, the argument that losses are given more weight than gains, has been recently shown to be absent in small losses. However, a series of studies by Mrkva et al. (2020) appear to demonstrate the existence of loss aversion even for smaller losses. We re-ran Mrkva et al.’s decision tasks after removing features of the task that differentiated losses from the gains, particularly asymmetries in sizes of gains and losses, an increasing order of losses, and status quo effects. The results show that we replicate Mrkva et al.’s (2020) findings in their original paradigm with online participants, yet in five studies where gains and losses were symmetrically presented in random order (n = 2,001), we find no loss aversion for small amounts, with loss aversion surfacing very weakly only for average losses of $40 (mean λ = 1.16). We do find loss aversion for higher amounts such as $100 (mean λ = 1.54) though it is not as extreme as previously reported. Furthermore, we find weak correlation between the endowment effect and loss aversion, with the former effect existing simultaneously with no loss aversion. Thus, when items are presented symmetrically, significant loss aversion emerges only for large losses, suggesting that it cannot be argued that (all) “losses loom larger than gains.”

Experience is the best teacher. Yet, in the context of repeated decisions, experience was found to trigger deviations from maximization in the direction of underweighting of rare events. Evaluations of alternative explanations for this bias led to contradicting conclusions. Studies that focused on the aggregate choice rates, including a series of choice prediction competitions, favored the assumption that this bias reflects reliance on small samples. In contrast, studies that focused on individual decisions suggest that the bias reflects a strong myopic tendency by a significant minority of participants. The current analysis clarifies the apparent inconsistency by reanalyzing a data set that previously led to contradicting conclusions. Our analysis suggests that the apparent inconsistency reflects the differing focus of the cognitive models. Specifically, sequential adjustment models (that assume sensitivity to the payoffs’ weighted averages) tend to find support for the hypothesis that the deviations from maximization are a product of strong positive recency (a form of myopia). Conversely, models assuming random sampling of past experiences tend to find support to the hypothesis that the deviations reflect reliance on small samples. We propose that the debate should be resolved by using the assumptions that provide better predictions. Applying this solution to the data set we analyzed shows that the random sampling assumption outperforms the weighted average assumption both when predicting the aggregate choice rates and when predicting the individual decisions.

Existing evidence from laboratory experiments finds that a match is likely to increase charitable donations by more than a theoretically equivalent rebate. A number of explanations have been proposed for this in the literature. One idea, which has never been tested, is that people consider a match to be more generous, because unlike the rebate, there is no reward for making a donation in the match setting. We design a survey to determine whether people do consider matches more generous than rebates, and probe the reasons subjects give for their answers. We find that a significant number of people do consider rebates less generous because of the reward associated with donations in such a setting.

In a controlled laboratory experiment we investigate whether time pressure influences voting decisions, and in particular the degree of strategic (insincere) voting. We find that participants under time constraints are more sincere when using the widely-employed Plurality Voting method. That is, time pressure might reduce strategic voting and hence misrepresentation of preferences. However, there are no effects for Approval Voting, in line with arguments that this method provides no incentives for strategic voting.

Previous literature and conventional wisdom have led researchers to believe that boredom increases economic risk taking, but the evidence in support of this conclusion is limited and has important shortcomings. In four experiments (including more than 1,300 subjects), we systematically studied the effects of boredom on economic risk preferences. Across different risk elicitation tasks, boredom inductions, incentive schemes, subject pools, and using both reduced form and structural analyses, we consistently failed to find an effect of boredom on risky decisions. Our results disprove that boredom leads to even small increments in risk taking in one-shot elicitation tasks, and small to medium increases in multiple-choice elicitations. These findings question an important established belief, contribute to better understand the consequences of boredom, and have substantive implications for experiments on economic decision making.

Ethnographers have recorded many instances of tokens donated as gifts to attract new partners or strengthen ties to existing ones. We study whether gifts are an effective pledge of the donor’s trustworthiness through an experiment modeled on the trust game. We vary whether the trustee can send a token before the trustor decides whether to transfer money; whether one of the tokens is rendered salient through experimental manipulations (a vote or an incentive-compatible rule of purchase for the tokens); and whether the subjects interact repeatedly or are randomly re-matched in each round. Tokens are frequently sent in all studies in which tokens are available, but repeated interaction, rather than gifts, is the leading behavioral driver in our data. In the studies with random pairs, trustors send significantly more points when the trustee has sent a token. Subjects in a fixed matching achieve comparable levels of trust and trustworthiness in the studies with and without tokens. The trustee’s decision to send a token is not predictive of the amount the trustee returns to the trustor. A token is used more sparingly whenever salient — a novel instance of endogenous value creation in the lab.

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.