Experimental Economics

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.

A correction to this paper has been published: https://doi.org/10.1007/s10683-021-09712-z

In this paper we provide new evidence showing that fair behavior is intuitive to most people. We find a strong association between a short response time and fair behavior in the dictator game. This association is robust to controls that take account of the fact that response time might be affected by the decision-maker’s cognitive ability and swiftness. The experiment was conducted with a large and heterogeneous sample recruited from the general population in Denmark. We find a striking similarity in the association between response time and fair behavior across groups in the society, which suggests that the predisposition to act fairly is a general human trait.

We conduct a laboratory experiment to explore whether the protection of intellectual property (IP) incentivizes people to create non-rivalrous knowledge goods, foregoing the production of other rivalrous goods. In the contrasting treatment with no IP protection, participants are free to resell and remake non-rivalrous knowledge goods originally created by others. We find that creators reap substantial profits when IP is protected and that rampant pirating is common when there is no IP protection, but IP protection in and of itself is neither necessary nor sufficient for generating wealth from the discovery of knowledge goods. Rather, individual entrepreneurship is the key.

This paper reports on the use of carrot (positive) and stick (negative) incentives as methods of increasing effort among members of work teams. We study teams of four members in a laboratory environment in which giving effort towards the team goal is simulated by eliciting voluntary contributions towards the provision of a public good. We test the efficiency-improving properties of four distinct environments: monetary prizes given to high contributors versus monetary fines assessed to low contributors, where high/low contributor is defined first in terms of absolute contributions and then in terms of contributions relative to abilities—which we call handicapping. Our results show that both carrot and stick can increase efficiency (i.e., contributions) levels by 10–28%. We find that handicapped incentives promise the highest efficiency levels, and when handicapping is not used penalties may be more effective than prizes. The implications for work teams and suggestions for practical implementation are discussed.

Experimental game theory studies the behavior of agents who face a stream of one-shot games as a form of learning. Most literature focuses on a single recurring identical game. This paper embeds single-game learning in a broader perspective, where learning can take place across similar games. We posit that agents categorize games into a few classes and tend to play the same action within a class. The agent’s categories are generated by combining game features (payoffs) and individual motives. An individual categorization is experience-based, and may change over time. We demonstrate our approach by testing a robust (parameter-free) model over a large body of independent experimental evidence over $$2 \times 2$$ symmetric games. The model provides a very good fit across games, performing remarkably better than standard learning models.

Buying lottery tickets is not a rational investment from a financial point of view. Yet, the majority of people participate at least once a year in a lottery. We conducted a field experiment to increase understanding of lottery participation. Using representative data for the Netherlands, we find that lottery participation increased the happiness of participants before the draw. Winning a small prize had no effect on happiness. Our results indicate that people may not only care about the outcomes of the lottery, but also enjoy the game. Accordingly, we conclude that lottery participation has a utility value in itself and part of the utility of a lottery ticket is consumed before the draw.