Although the majority of people value the idea of helping others, they often take no particular action. In two field studies we investigated the impact of differently justified requests for spontaneous charity donations and for antisocial behavior like stealing. In the experiments, unwatched stands with cookies and money jars were placed on a crowded city square with one of three different notes: (1) detailed prosocial justification, (2) general justification or (3) no justification. After testing almost 500 participants, we show that mere general arguments can both increase prosocial behavior and decrease antisocial behavior. Additionally, detailed prosocial justification augments generosity, causing people voluntarily to pay more than required. We conclude that prosocial (compliance with request) and antisocial (stealing) behavior is guided by automatic processes that track that there is any reason for the request, while generosity is guided by reflective assessment of the justification of the request.
We calculate the dynamics of tax evasion within a multi-agent econophysics model which is adopted from the theory of magnetism and previously has been shown to capture the main characteristics from agent-based based models which build on the standard Allingham and Sandmo approach. In particular, we implement a feedback of public goods provision on the decision-making of selfish agents which aim to pursue their self interest. Our results imply that such a feedback enhances the moral attitude of selfish agents thus reducing the percentage of tax evasion. Two parameters govern the behavior of selfish agents, (i) the rate of adaption to changes in public goods provision and (ii) the threshold of perception of public goods provision. Furtheron we analyze the tax evasion dynamics for different agent compositions and under the feedback of public goods provision. We conclude that policymakers may enhance tax compliance behavior via the threshold of perception by means of targeted public relations.
The paper presents a prototype of a rehabilitation robot for lower extremities. It is created on the basis of cylindrical kinematic model, equipped with two rigid arms, special handles and fixtures. It has five active degrees of freedom and is designed to repeat the trajectories generated by physiotherapist during the learning phase. Presented prototype of rehabilitation robot has the ability to replay different types of trained exercises such as: hip and knee flexion/extension, leg abduction/adduction. The protection system (including overload detection) implemented in the robot ensures safe working with patients.
Creep compliance of the hot-mix asphalt (HMA) is a primary input of the current pavement thermal cracking prediction model used in the US. This paper discusses a process of training an Artificial Neural Network (ANN) to correlate the creep compliance values obtained from the Indirect Tension (IDT) with similar values obtained on small HMA beams from the Bending Beam Rheometer (BBR). In addition, ANNs are also trained to predict HMA creep compliance from the creep compliance of asphalt binder and vice versa using the BBR setup. All trained ANNs exhibited a very high correlation of 97 to 99 percent between predicted and measured values. The binder creep compliance functions built on the ANN-predicted discrete values also exhibited a good correlation when compared with the laboratory experiments. However, the simulation of trained ANNs on the independent dataset produced a significant deviation from the measured values which was most likely caused by the differences in material composition, such as aggregate type and gradation, presence of recycled additives, and binder type.