Bayesian optimal experiments that maximize the information gained from collected data are critical to efficiently identify behavioral models. We extend a seminal method for designing Bayesian optimal experiments by introducing two computational improvements that make the procedure tractable: (1) a search algorithm from artificial intelligence that efficiently explores the space of possible design parameters, and (2) a sampling procedure which evaluates each design parameter combination more efficiently. We apply our procedure to a game of imperfect information to evaluate and quantify the computational improvements. We then collect data across five different experimental designs to compare the ability of the optimal experimental design to discriminate among competing behavioral models against the experimental designs chosen by a “wisdom of experts” prediction experiment. We find that data from the experiment suggested by the optimal design approach requires significantly less data to distinguish behavioral models (i.e., test hypotheses) than data from the experiment suggested by experts. Substantively, we find that reinforcement learning best explains human decision-making in the imperfect information game and that behavior is not adequately described by the Bayesian Nash equilibrium. Our procedure is general and computationally efficient and can be applied to dynamically optimize online experiments.

In experimental economics there is a norm against using deception. But precisely what constitutes deception is unclear. While there is a consensus view that providing false information is not permitted, there are also “gray areas” with respect to practices that omit information or are misleading without an explicit lie being told. In this paper, we report the results of a large survey among experimental economists and students concerning various specific gray areas. We find that there is substantial heterogeneity across respondent choices. The data indicate a perception that costs and benefits matter, so that such practices might in fact be appropriate when the topic is important and there is no other way to gather data. Compared to researchers, students have different attitudes about some of the methods in the specific scenarios that we ask about. Few students express awareness of the no-deception policy at their schools. We also briefly discuss some potential alternatives to “gray-area” deception, primarily based on suggestions offered by respondents.

Online experiments allow researchers to collect datasets at times not typical of laboratory studies. We recruit 2336 participants from Amazon Mechanical Turk to examine if participant characteristics and behaviors differ depending on whether the experiment is conducted during the day versus night, and on weekdays versus weekends. Participants make incentivized decisions involving prosociality, punishment, and discounting, and complete a demographic and personality survey. We find no time or day differences in behavior, but do find that participants at nights and on weekends are less experienced with online studies; on weekends are less reflective; and at night are less conscientious and more neurotic. These results are largely robust to finer-grained measures of time and day. We also find that those who participated earlier in the course of the study are more experienced, reflective, and agreeable, but less charitable than later participants.

MTVE is an open-source software tool (citeware) that can be applied in laboratory and online experiments to implement video communication. The tool enables researchers to gather video data from these experiments in a way that these videos can be later used for automatic analysis through machine learning techniques. The browser-based tool comes with an easy user interface and can be easily integrated into z-Tree, oTree (and other experimental or survey tools). It provides the experimenters control over several communication parameters (e.g., number of participants, resolution), produces high-quality video data, and circumvents the Cocktail Party Problem (i.e., the problem of separating speakers solely based on audio input) by producing separate files. Using some of the recommended Voice-to-Text AI, the experimenters can transcribe individual files. MTVE can merge these individual transcriptions into one conversation.

The aim of this paper is to investigate the role that the marital status of children has in shaping the living arrangements of their widowed mothers and themselves and to explain the increase in the proportion of elderly widows living alone, which grew by 23.2% in the USA between 1970 and 1990. We propose a model where living arrangements are determined as the outcome of a game between the mother and her child, and where the fundamentals of the model depend on children's marital status. We estimate the model using 1970 data. We calculate the accuracy of the estimation and we obtain an excellent fit. Using the same measure of accuracy, the estimated model predicts that changes in the incomes of both the widow and her offspring and changes in the children's marital status generate more than the 83% of the increase in the number of widows living alone.