We investigate the role of visual attention in risky choice in a rich experimental dataset that includes eye-tracking data. We first show that attention is not reducible to individual and contextual variables, which explain only 20% of attentional variation. We then decompose attentional variation into individual average attention and trial-wise deviations of attention to capture different cognitive processes. Individual average attention varies by individual, and can proxy for individual preferences or goals (as in models of “rational inattention” or goal-directed attention). Trial-wise deviations of attention vary within subjects and depend on contextual factors (as in models of “salience” or stimulus-driven attention). We find that both types of attention predict behavior: average individual attention patterns are correlated with individual levels of loss aversion and capture part of this individual heterogeneity. Adding trial-wise deviations of attention further improves model fit. Our results show that a decomposition of attention into individual average attention and trial-wise deviations of attention can capture separable cognitive components of decision making and provides a useful tool for economists and researchers from related fields interested in decision-making and attention.

We present an experiment on the false consensus effect. Unlike previous experiments, we provide monetary incentives for revealing the actual estimation of others’ behavior. In each session and round, sixteen subjects make a choice between two options simultaneously. Then they estimate the choices of a randomly selected subgroup. For half of the rounds we provide information about other subjects’ choices. There we find no false consensus effect. At an aggregate level, subjects significantly underweight rather than overweight their choices. When we do not provide information, the presence of a false consensus effect cannot be detected.

The underpricing of initial public offerings (IPO) is a well-documented fact of empirical equity market research. Theories explain this underpricing with market imperfections. We study three empirically relevant IPO mechanisms under almost perfect market conditions in the laboratory: a stylized book building approach, a closed book auction, and an open book auction. We report underpricing in each of these IPO mechanisms. Uncertainty about the aftermarket behavior may partly explain IPO excess returns but underpricing persists even in the repeated setting where uncertainty is negligible and despite the equilibrium adjustment dynamics, that we observe in the data. The data reveal a market-wide impact of investors’ reluctance to sell in the aftermarket at a price below the offering price. We conclude that a behavioural bias similar to the disposition effect fosters IPO underpricing in our setting.

The highly popular belief that rent-control leads to an increase in the amount of affordable housing is in contradiction with ample empirical evidence and congruent theoretical explanations. It can therefore be qualified as a misconception. We present the results of a preregistered on-line experiment in which we study how to dispel this misconception using a refutational approach in two different formats, a video and a text. We find that the refutational video has a significantly higher positive impact on revising the misconception than a refutational text. This effect is driven by individuals who initially agreed with it and depart from it after the treatment. The refutational text, in turn, does not have a significant impact relative to a non-refutational text. Higher cognitive reflective ability is positively associated with revising beliefs in all interventions. Our research shows that visual communication effectively reduces the gap between scientific economic knowledge and the views of citizens.

Learning models predict that the relative speed at which players in a game adjust their behavior has a critical influence on long term behavior. In an ultimatum game, the prediction is that proposers learn not to make small offers faster than responders learn not to reject them. We experimentally test whether relative speed of learning has the predicted effect, by manipulating the amount of experience accumulated by proposers and responders. The experiment allows the predicted learning by responders to be observed, for the first time.

This paper investigates whether and to what extent group identity plays a role in peer effects on risk behaviour. We run a laboratory experiment in which different levels of group identity are induced through different matching protocols (random or based on individual painting preferences) and the possibility to interact with group members via an online chat in a group task. Risk behaviour is measured by using the Bomb Risk Elicitation Task and peer influence is introduced by giving subjects feedback regarding group members’ previous decisions. We find that subjects are affected by their peers when taking decisions and that group identity influences the magnitude of peer effects: painting preferences matching significantly reduces the heterogeneity in risk behaviour compared with random matching. On the other hand, introducing a group task has no significant effect on behaviour, possibly because interaction does not always contribute to enhancing group identity. Finally, relative riskiness within the group matters and individuals whose peers are riskier than they are take on average riskier decisions, even when controlling for regression to the mean.

We investigate in a laboratory experiment whether procedural fairness concerns affect how well individuals are able to solve a coordination problem in a two-player Volunteer’s Dilemma. Subjects receive external action recommendations, either to volunteer or to abstain from it, in order to facilitate coordination and improve efficiency. We manipulate the fairness of the recommendation procedure by varying the probabilities of receiving the disadvantageous recommendation to volunteer between players. We find evidence that while recommendations improve overall efficiency regardless of their implications for expected payoffs, there are behavioural asymmetries depending on the recommendation: advantageous recommendations are followed less frequently than disadvantageous ones and beliefs about others’ actions are more pessimistic in the treatment with recommendations inducing unequal expected payoffs.

This study investigates the mechanisms driving the effectiveness of free-form communication in promoting cooperation within a sequential social dilemma game. We hypothesize that the self-constructing nature of free-form communication enhances the sincerity of messages and increases the disutility of dishonoring promises. Our experimental results demonstrate that free-form messages outperform both restricted promises and treatments where subjects select and use previously constructed free-form messages. Interestingly, we find that selected free-form messages and restricted promises achieve similar levels of cooperation. We observe that free-form messages with higher sincerity increase the likelihood of high-price and high-quality choices, thereby promoting cooperation. These messages frequently include promises and honesty, while threats do not promote cooperation. Our findings emphasize the crucial role of the self-constructed nature of free-form messages in promoting cooperation, exceeding the impact of message content compared to restricted communication protocols.

This paper is the first to use the WeChat platform, one of the largest social networks, to conduct an online experiment of artificial investment games. We investigate how people’s forecasts about the financial market and investment decisions are shaped by whether they can observe others’ forecasts and whether they engage in public or private investment decisions. We find that with forecast sharing, subjects’ forecasts converge but in different directions across groups; consequently, forecast sharing does not lead to better forecasts nor more individually rational investment decisions. Whether or not subjects engage in public investment decisions does not significantly affect forecasts or investment.

We run a laboratory experiment to test the concept of coarse correlated equilibrium (Moulin and Vial in Int J Game Theory 7:201–221, 1978), with a two-person game with unique pure Nash equilibrium which is also the solution of iterative elimination of strictly dominated strategies. The subjects are asked to commit to a device that randomly picks one of three symmetric outcomes (including the Nash point) with higher ex-ante expected payoff than the Nash equilibrium payoff. We find that the subjects do not accept this lottery (which is a coarse correlated equilibrium); instead, they choose to play the game and coordinate on the Nash equilibrium. However, given an individual choice between a lottery with equal probabilities of the same outcomes and the sure payoff as in the Nash point, the lottery is chosen by the subjects. This result is robust against a few variations. We explain our result as selecting risk-dominance over payoff dominance in equilibrium.

The Individual Evolutionary Learning (IEL) model explains human subjects’ behavior in a wide range of repeated games which have unique Nash equilibria. Using a variation of ‘better response’ strategies, IEL agents quickly learn to play Nash equilibrium strategies and their dynamic behavior is like that of humans subjects. In this paper we study whether IEL can also explain behavior in games with gains from coordination. We focus on the simplest such game: the 2 person repeated Battle of Sexes game. In laboratory experiments, two patterns of behavior often emerge: players either converge rapidly to one of the stage game Nash equilibria and stay there or learn to coordinate their actions and alternate between the two Nash equilibria every other round. We show that IEL explains this behavior if the human subjects are truly in the dark and do not know or believe they know their opponent’s payoffs. To explain the behavior when agents are not in the dark, we need to modify the basic IEL model and allow some agents to begin with a good idea about how to play. We show that if the proportion of inspired agents with good ideas is chosen judiciously, the behavior of IEL agents looks remarkably similar to that of human subjects in laboratory experiments.

We investigate how the selection process of a leader affects team performance with respect to social learning. We use a laboratory experiment in which an incentivized guessing task is repeated in a star network with the leader at the center. Leader selection is either based on competence, on self-confidence, or made at random. In our setting, teams with random leaders do not underperform. They even outperform teams with leaders selected on self-confidence. Hence, self-confidence can be a dangerous proxy for competence of a leader. We show that it is the declaration of the selection procedure which makes non-random leaders overly influential. To investigate the opinion dynamics, we set up a horse race between several rational and naïve models of social learning. The prevalent conservatism in updating, together with the strong influence of the team leader, imply an information loss since the other team members’ knowledge is not sufficiently integrated.

The COVID-19 pandemic presents a remarkable opportunity to put to work all of the research that has been undertaken in past decades on the elicitation and structural estimation of subjective belief distributions as well as preferences over atemporal risk, patience, and intertemporal risk. As contributors to elements of that research in laboratories and the field, we drew together those methods and applied them to an online, incentivized experiment in the United States. We have two major findings. First, the atemporal risk premium during the COVID-19 pandemic appeared to change significantly compared to before the pandemic, consistent with theoretical results of the effect of increased background risk on foreground risk attitudes. Second, subjective beliefs about the cumulative level of deaths evolved dramatically over the period between May and November 2020, a volatile one in terms of the background evolution of the pandemic.

The present study explores the value of machine learning techniques in the classification of communication content in experiments. Previously human-coded datasets are used to both train and test algorithm-generated models that relate word counts to categories. For various games, the computer models of the classification are able to match out-of-sample the human classification to a considerable extent. The analysis raises hope that the substantial effort going into such studies can be reduced by using computer algorithms for classification. This would enable a quick and replicable analysis of large-scale datasets at reasonable costs and widen the applicability of such approaches. The paper gives an easily accessible technical introduction into the computational method.

In many search environments, searchers are learning about the distribution of offers in the market. I conduct an experiment exploring a broad class of search problems with learning about the distribution of payoffs. My results support the prediction that learning results in declining reservation values, providing evidence that learning may be an explanation for recall. Theory predicts a “one step” reservation value strategy, but many subjects instead choose to set a high reservation value in order to learn about the distribution before adjusting based on their observations. Under-searching in search experiments may stem from a reinforcement heuristic and lack of negative feedback after using sub-optimal strategies.

We investigate how people make choices when they are unsure about the value of the options they face and have to decide whether to choose now or wait and acquire more information first. In an experiment, we find that participants deviate from optimal information acquisition in a systematic manner. They acquire too much information (when they should only collect little) or not enough (when they should collect a lot). We show that this pattern can be explained as naturally emerging from Fechner cognitive errors. Over time participants tend to learn to approximate the optimal strategy when information is relatively costly.

Bayesian updating remains the benchmark for dynamic modeling under uncertainty within economics. Recent theory and evidence suggest individuals may process information asymmetrically when it relates to personal characteristics or future life outcomes, with good news receiving more weight than bad news. I examine information processing across a broad set of contexts: (1) ego relevant, (2) financially relevant, and (3) non value relevant. In the first two cases, information about outcomes is valenced, containing either good or bad news. In the third case, information is value neutral. In contrast to a number of previous studies I do not find differences in belief updating across valenced and value neutral settings. Updating across all contexts is asymmetric and conservative: the former is influenced by sequences of signals received, a new variation of confirmation bias, while the latter is driven by non-updates. Despite this, posteriors are well approximated by those calculated using Bayes’ rule. Most importantly these patterns are present across all contexts, cautioning against the interpretation of asymmetric updating or other deviations from Bayes’ rule as being motivated by psychological biases.

We report an experiment on a decision task by Samuelson and Bazerman (1985). Subjects submit a bid for an item with an unknown value. A winner's curse phenomenon arises when subjects bid too high and make losses. Learning direction theory can account for this. However, other influences on behaviour can also be identified. We introduce impulse balance theory to make quantitative predictions on the basis of learning direction theory. We also look at monotonic ladder processes. It is shown that for this kind of Markov chains the impulse balance point is connected to the mode of the stationary distribution.

Economic models typically allow for “free disposal” or “reversibility” of information, which implies non-negative value. Building on previous research on the “curse of knowledge” we explore situations where this might not be so. In three experiments, we document situations in which participants place positive value on information in attempting to predict the performance of uninformed others, even when acquiring that information diminishes their earnings. In the first experiment, a majority of participants choose to hire informed—rather than uninformed—agents, leading to lower earnings. In the second experiment, a significant number of participants pay for information—the solution to a puzzle—that hurts their ability to predict how many others will solve the puzzle. In the third experiment, we find that the effect is reduced with experience and feedback on the actual performance to be predicted. We discuss implications of our results for the role of information and informed decision making in economic situations.

This study considers a model of road congestion with average cost pricing. Subjects must choose between two routes—Road and Metro. The travel cost on the road is increasing in the number of commuters who choose this route, while the travel cost on the metro is decreasing in the number of its users. We examine how changes to the road capacity, the number of commuters, and the metro pricing scheme influence the commuters’ route-choice behavior. According to the Downs-Thomson paradox, improved road capacity increases travel times along both routes because it attracts more users to the road and away from the metro, thereby worsening both services. A change in route design generates two Nash equilibria; and the resulting coordination problem is amplified even further when the number of commuters is large. We find that, similar to other binary choice experiments with congestion effects, aggregate traffic flows are close to the equilibrium levels, but systematic individual differences persist over time.