This article theoretically and empirically analyses behaviour in penny auctions, a relatively new auction mechanism. As in the US dollars or war-of-attrition, players in penny auctions commit higher non-refundable costs as the auction continues and only win if all other players stop bidding. I first show that, in any equilibria that does not end immediately, players bid probabilistically such that the expected profit from every bid is zero. Then, using two large data sets covering 166, 000 auctions, I calculate that average profit margins actually exceed 50%. To explain this deviation, I incorporate a sunk-cost fallacy into the theoretical model to generate a set of predictions about hazard rates and player behaviour, which I confirm empirically. While players do (slowly) learn to correct this bias and there are few obvious barriers to competition, activity in the market is rising and concentration remains relatively high.

We experimentally investigate the time-inconsistent taste for immediate gratification and future-preference misprediction. Across 7 weeks, 100 participants choose the number of unpleasant transcription tasks given various wages to complete immediately and at different future dates. Participants preferred 10–12% fewer tasks in the present compared to any future date, leading to an estimated $β $ of 0.83. Comparing predictions with actual immediate-work choices provides evidence against substantial sophistication, with estimates implying that participants understand no more than 24% of their present bias. Finally, we find evidence of “projection bias”: participants wished to complete 4–12% fewer tasks when decisions were elicited right after completing tasks rather than before.

When a Bayesian learns new information and changes her beliefs, she must on average become concomitantly more certain about the state of the world. Consequently, it is rare for a Bayesian to frequently shift beliefs substantially while remaining relatively uncertain, or, conversely, become very confident with relatively little belief movement. We formalize this intuition by developing specific measures of movement and uncertainty reduction given a Bayesian’s changing beliefs over time, showing that these measures are equal in expectation and creating consequent statistical tests for Bayesianess. We then show connections between these two core concepts and four common psychological biases, suggesting that the test might be particularly good at detecting these biases. We provide support for this conclusion by simulating the performance of our test and other martingale tests. Finally, we apply our test to data sets of individual, algorithmic, and market beliefs.

We derive bounds on the rational variation in option‐implied beliefs about market returns. While risk preferences distort option‐implied (or risk‐neutral ) beliefs away from subjective beliefs, one can nonetheless bound risk‐neutral belief movement under a general assumption on the stochastic discount factor. The resulting test requires no knowledge of the objective distribution and allows significantly more flexibility in discount rates than standard volatility tests. Implementing our test empirically using index options, we find that there is so much movement in risk‐neutral beliefs that the bounds are routinely violated. Our results imply significant excess index‐price volatility.

In this article, we examine the effect of “choice fatigue” on decision making. We exploit a natural experiment in which voters face the same contest at different ballot positions due to differences in the number of local issues on their ballot. Facing more decisions before a given contest significantly increases the tendency to abstain or rely on decision shortcuts, such as voting for the status quo or the first-listed candidate. We estimate that, without choice fatigue, abstentions would decrease by 8%, and 6% of the propositions in our data set would have passed rather than failed.

When people receive new information, sometimes they revise their beliefs too much, and sometimes too little. We show that a key driver of whether people overinfer or underinfer is the strength of the information. Based on a model in which people know which direction to update in, but not exactly how much to update, we hypothesize that people will overinfer from weak signals and underinfer from strong signals. We then test this hypothesis across four different environments: abstract experiments, a naturalistic experiment, sports betting markets, and financial markets. In each environment, our consistent and robust finding is overinference from weak signals and underinference from strong signals. Our framework and findings can help harmonize apparently contradictory results from the experimental and empirical literatures.

Experimental tests of dynamically inconsistent time preferences have largely relied on choices over time-dated monetary rewards. Several recent studies have failed to find the standard patterns of present bias. However, such monetary studies contain often-discussed confounds. In this article, we sidestep these confounds and investigate choices over consumption (real effort) in a longitudinal experiment. We pair this effort study with a companion monetary discounting study. We confirm very limited time inconsistency in monetary choices. However, subjects show considerably more present bias in effort. Furthermore, present bias in the allocation of work has predictive power for demand of a meaningfully binding commitment device. Therefore our findings validate a key implication of models of dynamic inconsistency, with corresponding policy implications.

People may fail to recall and use information they already know when making decisions. We empirically investigate whether such “retrieval failures” distort consumption-smoothing behavior among Zambian farmers, who derive their income from one annual harvest and spend it over the course of the year. We document that individuals underestimate upcoming spending by 50%, creating scope for undersaving. To improve recall, we randomize an intervention that prompts people to think through their future expenses associatively in categories—without providing any external information or guidance. Treated individuals increase “remembered” expenses by 36%–60%; as predicted by the memory literature, effects are concentrated among small, irregular, and stochastic items. Immediate spending drops and, six weeks after the intervention, treated households hold 15% higher savings. They subsequently enter the hungry season—the final months of the year when consumption typically declines sharply—with one additional month of savings, leading to a flatter spending profile over the year. Households use the increased savings to self-finance additional farm investment, resulting in a 9% increase in the next year’s crop revenue. We replicate the intervention’s effect on beliefs among low-income Americans, suggesting that retrieval failures generalize across settings and populations.