For the trust game, recent models of belief-dependent motivations make opposite predictions regarding the correlation between back transfers and second-order beliefs of the trustor: while reciprocity models predict a negative correlation, guilt-aversion models predict a positive one. This paper tests the hypothesis that the inconclusive results in the previous studies investigating the reaction of trustees to their beliefs are due to the fact that reciprocity and guilt aversion are behaviorally relevant for different subgroups and that their impact cancels out in the aggregate. We find little evidence in support of this hypothesis and conclude that type heterogeneity is unlikely to explain previous results.

The replication crisis across several disciplines raises challenges for behavioural sciences in general. In this report, we review the lessons for experimental economists of these developments. We present the new research methods and practices which are being proposed to improve the replicability of scientific studies. We discuss how these methods and practices can have a positive impact in experimental economics and the extent to which they should be encouraged.

Super-resolution of turbulence is a term used to describe the prediction of high-resolution snapshots of a flow from coarse-grained observations. This is typically accomplished with a deep neural network and training usually requires a dataset of high-resolution images. An approach is presented here in which robust super-resolution can be performed without access to high-resolution reference data, as might be expected in an experiment. The training procedure is similar to data assimilation, wherein the model learns to predict an initial condition that leads to accurate coarse-grained predictions at later times, while only being shown coarse-grained observations. Implementation of the approach requires the use of a fully differentiable flow solver in the training loop to allow for time-marching of predictions. A range of models are trained on data generated from forced, two-dimensional turbulence. The networks have reconstruction errors which are similar to those obtained with ‘standard’ super-resolution approaches using high-resolution data. Furthermore, the methods are comparable to the performance of standard data assimilation for state estimation on individual trajectories, outperforming these variational approaches at initial time and remaining robust when unrolled in time where performance of the standard data-assimilation algorithm improves.

We aim to estimate school value-added dynamically in time. Our principal motivation for doing so is to establish school effectiveness persistence while taking into account the temporal dependence that typically exists in school performance from one year to the next. We propose two methods of incorporating temporal dependence in value-added models. In the first we model the random school effects that are commonly present in value-added models with an auto-regressive process. In the second approach, we incorporate dependence in value-added estimators by modeling the performance of one cohort based on the previous cohort’s performance. An identification analysis allows us to make explicit the meaning of the corresponding value-added indicators: based on these meanings, we show that each model is useful for monitoring specific aspects of school persistence. Furthermore, we carefully detail how value-added can be estimated over time. We show through simulations that ignoring temporal dependence when it exists results in diminished efficiency in value-added estimation while incorporating it results in improved estimation (even when temporal dependence is weak). Finally, we illustrate the methodology by considering two cohorts from Chile’s national standardized test in mathematics.