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This paper explores the relationship between crisis and experience, concentrating on 'excentric positionality' in relation to the shared world, as presented in the work of Helmuth Plessner. A by-product of the 1920s Weimar Germany, Plessner's philosophical anthropology, it is argued, presents us with a forgotten blueprint for transitive and compositional approaches to the social world. Instead of the familiar 'crisis of experience' used to diagnose 'what has gone wrong', it allows us to re-learn how to work with 'the experience of crisis' itself. The latter holds the key to a different type of approach based on 'xeno-communication'. This type of communication utilizes the productive potential of crisis in its uncertainty and hesitation before a decision, showing a way to extend and enlarge experience itself. Cultivation of these 'excentric' dynamics in turn suggests new 'excentric methodologies' based on a more flexible fit between concepts and the worlds they are meant to describe. 'Excentric methodologies' constitute a type of experience-based, analytical response to the shared world. They work with phenomena across spaces and problems, analytically utilizing their joint emergence from the fundamental imbalance and discontinuity characteristic of the human environment. [ABSTRACT FROM AUTHOR]

This paper is a quantitative analysis of the data collected globally by the World Value Survey. The data is used to study the trajectories of change in individuals' religious beliefs, values, and behaviors in societies. Utilizing random forest, we aim to identify the key factors of religiosity and classify respondents of the survey as religious and non-religious using country-level data. We use resampling techniques to balance the data and improve imbalanced learning performance metrics. The results of the variable importance analysis suggest that Age and Income are the most important variables in the majority of countries. The results are discussed with fundamental sociological theories regarding religion and human behavior. This study is an application of machine learning in identifying the underlying patterns in the data of 30 countries participating in the World Value Survey. The results from variable importance analysis and classification of imbalanced data provide valuable insights beneficial to theoreticians and researchers of social sciences. [ABSTRACT FROM AUTHOR]

The user interaction in online social networks can not only reveal the social relationships among users in e-commerce systems, but also imply the social preferences of a target user for recommendation services. However, the current research has rarely explored the impact of social interaction on recommendation performance, especially now that recommender systems face increasing challenges and suffer from poor efficiency due to social data overload. Therefore, applied research on user interaction has become increasingly necessary in the field of social recommendation. In this paper, we develop a novel social recommendation method based on the user interaction in complex social networks, called the SRUI model, to present a basis for improving the efficiency of the recommender systems. Specifically, a weighted social interaction network is first mapped to represent the interactions among social users according to the gathered information about historical user behavior. Thereafter, the complete path set is mined by the complete path mining (CPM) algorithm to find social similar neighbors with tastes similar to those of the target user. Finally, the social similar tendencies of the users on the complete paths are obtained to predict the final ratings of items through the SRUI model. A series of experimental results based on two real public datasets show that our approach performs better than other state-of-the-art methods in terms of recommendation performance. [ABSTRACT FROM AUTHOR]

This paper explores extreme response style to the Life Impact Burn Recovery Evaluation (LIBRE) Profile, a measure of social participation in burn survivors. We fit a Multidimensional Generalized Partial Credit Model (MGPCM) with a positive extreme response style (PERS) factor and compared this model with the original MGPCM, estimated the impact that PERS has on scores, and examined the personal characteristics that may result in an individual more likely to respond in a fashion that would inflate their true low scores. The average impact of the PERS, based upon the root mean squared bias, ranged from 0.27 to 0.50 of a standard deviation of the scale. Individuals who were older, had participated in a burn survivor support group, and had selected to self-administer the measure were less likely to have a high PERS bias that masks low scores. Future work can consider PERS when measuring the psychosocial impacts of burn injuries and other health conditions. [ABSTRACT FROM AUTHOR]

This paper studies whether intentionality is more prevalent than fairness in social preferences. We do this by introducing a new three-player game in which the choices of neutral third-party arbiters are isolated from any monetary or strategic concerns. This allows us to study the normative preferences of subjects, and to compare the relative weight they give to intentions and inequality. The results show that arbiters are mainly concerned with inequality, while other’s (selfish) intentions seem to play a minor role in their preferences. This result is robust to a series of experimental designs, suggesting that the role of intentions in social preferences might be smaller than implied by the previous literature. [ABSTRACT FROM AUTHOR]

This paper is an attempt to broaden economic discourse by importing insights into human behavior not just from psychology, but also from sociology and anthropology. Whereas in standard economics the concept of the decision-maker is the rational actor , and in early work in behavioral economics it is the quasi-rational actor influenced by the context of the moment of decision, in some recent work in behavioral economics, the decision-maker could be called the enculturated actor . This actor's preferences, perception, and cognition are subject to two deep social influences: (a) the social contexts to which he has become exposed and, especially, accustomed; and (b) the cultural mental models —including categories, identities, narratives, and worldviews—that he uses to process information. The paper traces how these factors shape behavior through the endogenous determination of preferences and the lenses through which individuals see the world—their perception and interpretation of situations. The paper offers a tentative taxonomy of the social determinants of behavior and describes the results of controlled and natural experiments that only a broader view of these determinants can plausibly explain. The perspective suggests more realistic models of human behavior for explaining outcomes and designing policies. [ABSTRACT FROM AUTHOR]

This paper describes a novel method for building affectively intelligent human-interactive agents. The method is based on a key sociological insight that has been developed and extensively verified over the last twenty years, but has yet to make an impact in artificial intelligence. The insight is that resource bounded humans will, by default, act to maintain affective consistency. Humans have culturally shared fundamental affective sentiments about identities, behaviours, and objects, and they act so that the transient affective sentiments created during interactions confirm the fundamental sentiments. Humans seek and create situations that confirm or are consistent with, and avoid and suppress situations that disconfirm or are inconsistent with, their culturally shared affective sentiments. This “ affect control principle ” has been shown to be a powerful predictor of human behaviour. In this paper, we present a probabilistic and decision-theoretic generalisation of this principle, and we demonstrate how it can be leveraged to build affectively intelligent artificial agents. The new model, called BayesAct , can maintain multiple hypotheses about sentiments simultaneously as a probability distribution, and can make use of an explicit utility function to make value-directed action choices. This allows the model to generate affectively intelligent interactions with people by learning about their identity, predicting their behaviours using the affect control principle, and taking actions that are simultaneously goal-directed and affect-sensitive. We demonstrate this generalisation with a set of simulations. We then show how our model can be used as an emotional “plug-in” for artificially intelligent systems that interact with humans in two different settings: an exam practice assistant (tutor) and an assistive device for persons with a cognitive disability. [ABSTRACT FROM AUTHOR]

This paper explores the concept of understanding (Verstehen) in the work of Max Weber. Parts of Weber's writings discussing this issue are here selected and reproduced. It is shown that in his use, the concept of understanding is part of a complex approach that tries to complement the specificities of humanistic disciplines with their exigence of a rigorous scientific approach. Weber conceives understanding as explanatory understanding, i.e the causal interpretation of a third person's action. The model he uses is that of the legal imputation of concrete effects to concrete causes (i.e. motives). According to Weber, understanding is always an hypothetical interpretation that must be confirmed by objective verification. Weber also makes a distinction between direct observational understanding [aktuelles Verstehen] and explanatory understanding [erklarendes Verstehen]. The former directly grasps the subjective meaning of a given act as such, while the latter is understanding of motivations, which consists in placing the act in an intelligible context of meaning. Both can be rational or affectively determined. Moreover, Weber distinguishes between subjective (on the level of meaning) and causal adequacy of the explanation of an action, and both must be present. [ABSTRACT FROM AUTHOR]

Psychological and other persuasive mechanisms across diverse contexts are well researched, with many studies of the effectiveness of specific persuasive techniques on distinct types of human behaviour. In the present paper, our specific interest lies in the development of a generalized modular psychometric tool to measure individuals’ susceptibility to persuasion. The scale is constructed using items from previously developed and validated particulate scales established in the domains of social psychology and behavioural economics. In the first study we establish the Susceptibility to Persuasion–II (StP-II) scale, containing 54 items, 10 subscales and further 6 sub-sub scales. In Study 2 we establish the scale’s construct validity and reconfirm its reliability. We present a valid and reliable modular psychometric tool that measures general susceptibility to persuasive techniques. Since its inception, we have successfully implemented the StP-II scale to measure susceptibility to persuasion of IT security officers, the role of psychology of persuasion in cybercrime victims and general persuadability levels of Facebook users; these manuscripts are in preparation. We argue that the StP-II scale shows promise in measuring individual differences in susceptibility to persuasion, and is applicable across diverse contexts such as Internet security and cybercrime. [ABSTRACT FROM AUTHOR]

Situated between the Altai Mountains and the Chinese Loess Plateau, the current territory of Mongolia played a pivotal role in Pleistocene human population dynamics in Northeast Asia with archaeological evidence suggesting the existence of cultural links with southern Siberia beginning in the Late Pleistocene. Here, we present preliminary results from the newly discovered site of Kharganyn Gol 5 in northern Mongolia. The results obtained from the Kharganyn Gol 5 site allow new reconstructions of chrono-cultural sequences and human behavior in eastern Central Asia. The site has yielded evidence of human occupation corresponding to several phases of the regional Upper Paleolithic. In addition, we present the first evidence of human occupation of the region prior to Greenland Interstadial 12 (GI12; 40,000–43,000 BP) and discuss the implications of such data. The Kharganyn Gol River basin contains sedimentary rock formations including numerous raw material outcrops, containing various types of chert. Prehistoric people used all these chert varieties for tool production, but the modes of raw material exploitation changed through time. This paper reports the presence, unique in Central and North Asia, of a non-utilitarian object made of muscovite mica in an Initial Upper Paleolithic assemblage in Archaeological Horizon 5 of the Kharganyn Gol 5 site. [ABSTRACT FROM AUTHOR]

Recent decades have witnessed online social media being a big-data window for testifying conventional social theories quantitatively and exploring much detailed human behavioral patterns. In this paper, by tracing the emoticon use in Weibo, a group of hidden “ambivalent users” are disclosed for frequently posting ambivalent tweets containing both positive and negative emotions. Further investigation reveals that this ambivalent expression could be a novel indicator of many unusual social behaviors. For instance, ambivalent users with the female as the majority like to make a sound in midnights and at weekends. They mention their close friends frequently in ambivalent tweets, which attract more replies and serve as a more private communication way. Ambivalent users also respond differently to public affairs from others and demonstrate more interests in entertainment and sports events. Moreover, the sentiment shift in ambivalent tweets is more evident than usual and exhibits a clear “negative to positive” pattern. The above observations, though being promiscuous seemingly, actually point to the self-regulation of negative mood in Weibo, which could find its basis from the traditional emotion management theories in sociology but makes an important extension to the online environment in this study. Finally, as an interesting corollary, ambivalent users are found connected with compulsive buyers and turn out to be perfect targets for online marketing. [ABSTRACT FROM AUTHOR]

Online consumer behavior in general and online customer engagement with brands in particular, has become a major focus of research activity fuelled by the exponential increase of interactive functions of the internet and social media platforms and applications. Current research in this area is mostly hypothesis-driven and much debate about the concept of Customer Engagement and its related constructs remains existent in the literature. In this paper, we aim to propose a novel methodology for reverse engineering a consumer behavior model for online customer engagement, based on a computational and data-driven perspective. This methodology could be generalized and prove useful for future research in the fields of consumer behaviors using questionnaire data or studies investigating other types of human behaviors. The method we propose contains five main stages; symbolic regression analysis, graph building, community detection, evaluation of results and finally, investigation of directed cycles and common feedback loops. The ‘communities’ of questionnaire items that emerge from our community detection method form possible ‘functional constructs’ inferred from data rather than assumed from literature and theory. Our results show consistent partitioning of questionnaire items into such ‘functional constructs’ suggesting the method proposed here could be adopted as a new data-driven way of human behavior modeling. [ABSTRACT FROM AUTHOR]