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Abstract

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.
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Abstract

There were two aims of the research. One was to enable more or less automatic confirmation of the known associations – either quantitative or qualitative – between technological data and selected properties of concrete materials. Even more important is the second aim – demonstration of expected possibility of automatic identification of new such relationships, not yet recognized by civil engineers. The relationships are to be obtained by methods of Artificial Intelligence, (AI), and are to be based on actual results from experiments on concrete materials. The reason of applying the AI tools is that in Civil Engineering the real data are typically non perfect, complex, fuzzy, often with missing details, which means that their analysis in a traditional way, by building empirical models, is hardly possible or at least can not be done quickly. The main idea of the proposed approach was to combine application of different AI methods in a one system, aimed at estimation, prediction, design and/or optimization of composite materials. The paradigm of the approach is that the unknown rules concerning the properties of concrete are hidden in experimental results and can be obtained from the analysis of examples. Different AI techniques like artificial neural networks, machine learning and certain techniques related to statistics were applied. The data for the analysis originated from direct observations and from reports and publications on concrete technology. Among others it has been demonstrated that by combining different AI methods it is possible to improve the quality of the data, (e.g. when encountering outliers and missing values or in clustering problems), so that the whole data processing system will be giving better prediction, (when applying ANNs), or the newly discovered rules will be more effective, (e.g. with descriptions more complete and – at the same time – possibly more consistent, in case of ML algorithms).
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