Many important decisions, for example, applying to college, require an individual to simultaneously submit several applications. These decisions are unique as each application is risky because acceptance is uncertain while also being rival as one can only attend a single college. In an influential theoretical analysis of these problems, Chade and Smith (2006) establish the No Safety Schools Theorem which suggests larger portfolios are riskier than single choices. We offer experimental evidence, using several experiments, that this theorem is routinely violated. In fact, the majority of our subjects violate this theorem. However, performance improves with practice, advice, and feedback.

I study the optimal design of monetary incentives in experiments where incentives are a treatment variable. I propose a novel framework called the Budget Minimization problem in which a researcher chooses the level of incentives that allows her to detect a predicted treatment effect while minimizing her expected budget. The Budget Minimization problem builds upon the power analysis and structural modeling. It extends the standard optimal design approach by explicitly incorporating the budget as a part of the objective function. I prove theoretically that the problem has an interior solution under fairly mild conditions. To showcase the practical applications of the Budget Minimization problem, I provide examples of its implementation in several well-known experiments. I also offer a practical guide to assist researchers in utilizing the proposed framework. The Budget Minimization problem contributes to the experimental economists’ toolkit for an optimal design, however, it also challenges some conventional design recommendations.

We design an experiment to study the implications of introducing position uncertainty in a social dilemma where eight players decide to contribute to a public good sequentially. Contributions are significantly higher when players make sequential decisions to contribute or not, are uncertain about their position in the sequence, and observe a sample of their predecessors’ choices compared to the simultaneous-move game. Yet, contribution rates remain invariant to the number of agents sampled. Consequently, contributions don’t unravel even with position certainty, and there is no incremental benefit of introducing position uncertainty, contrary to the theoretical prediction. Furthermore, controlling for the sum of contributions observed, individuals contribute less the later in the sequence they are.

Legitimizing property rights over the resources that participants use in dictator and ultimatum games has been shown to significantly alter behavior. However, a similar impact has not been observed in public good experiments. We employ an interior public good design with thirty periods of peer punishment, which allows groups to choose between plausible contribution norms without conflicting with efficiency. Across our Unearned and Earned treatments, endowments are randomly allocated or earned through a real effort task. In Unearned, both High and Low types adhere to a norm of contributing an equal proportion of one’s endowment. In contrast, in Earned, only Low types adhere to the proportional contribution norm, while High types contribute less than an equal proportion. Notably, deviations from the proportional contribution by High types are punished significantly less in Earned, suggesting a greater tolerance to such deviations when property rights are earned.

Although compulsory insurance mitigates the negative externalities caused by uninsured individuals, it raises the issue of insurance crowding out prevention. However, at the theoretical level, compulsory insurance and self-insurance (preventive investments dedicated to loss reduction) are know to be substitutes for risk averters but complements for risk lovers. This paper aims to empirically test these opposite predictions through a laboratory experiment using a model-based design. Our experimental results confirm the theoretical predictions: compulsory insurance and self-insurance are complements for risk lovers and substitutes for risk averters. This study strongly supports public policies advocating mandatory insurance implementation as they enhance risk lovers’ self-insurance investments. Therefore, a risk management scheme combining voluntary top-up and compulsory partial insurance guarantees an optimal risk allocation for risk-averters and increases the investments in self-insurance for risk-lovers.

Do large language models (LLMs) – such as ChatGPT-3.5 Turbo, ChatGPT-4.0, and Gemini 1.0 Pro, and DeepSeek-R1 – simulate human behavior in the context of the Prisoner’s Dilemma (PD) game with varying stake sizes? Through a replication of Yamagishi et al. (2016) ‘Study 2,’ we investigate this question, examining LLM responses to different payoff stakes and the influence of stake order on cooperation rates. We find that LLMs do not mirror the inverse relationship between stake size and cooperation found in the study. Rather, some models (DeepSeek-R1 and ChatGPT-4.0) almost wholly defect, while others (ChatGPT-3.5 Turbo and Gemini 1.0 Pro) mirror human behavior only under very specific circumstances. LLMs demonstrate sensitivity to framing and order effects, implying the need for cautious application of LLMs in behavioral research.

We design an experiment to study the effect of asymmetry in the context of group lending with joint liability. The performance of group loan contracts crucially hinges on borrowers engaging in peer monitoring and the common practice is to offer participants of a group loan symmetric contract terms. Our experiment shows that asymmetric contracts, in which monitoring is a dominant strategy for one borrower, increase the monitoring rate, and thus the repayment rate, without leaving borrowers substantially worse off. In addition, asymmetric contracting also raises expected profits of the lending institution. Overall, our experiment reveals that asymmetric group loan contracts are worth considering as part of a policy to maintain both financial stability and higher lender profits.

There is existing evidence that many individuals have preferences regarding selection of numbers in lottery games. Lottery data indicate that the percentage of players who choose their numbers, instead of having numbers randomly assigned, varies widely by lottery game. Differences in number selection mechanisms between games and an expected return maximization motive only present for parimutuel games are both reasons that can explain the variation. Differences in the payoff distributions between lottery games could also be contributing to the observed variation, a novel proposition. An experiment is designed to control for differences in number selection mechanisms and remove the expected return maximization motive, to test for the presence of distribution-dependent number preferences. Results indicate that 40% to 50% of subjects may display such preferences. It is therefore possible that distribution-dependent number preferences contribute to the empirical variation in number selection percentages in lottery games.