Network Analysis Based on Important Node Selection and Community Detection

The stability and robustness of a complex network can be significantly improved by determining important nodes and by analyzing their tendency to group into clusters. Several centrality measures for evaluating the importance of a node in a complex network exist in the literature, each one focusing on a different perspective. Community detection algorithms can be used to determine clusters of nodes based on the network structure. This paper shows by empirical means that node importance can be evaluated by a dual perspective—by combining the traditional centrality measures regarding the whole network as one unit, and by analyzing the node clusters yielded by community detection. Not only do these approaches offer overlapping results but also complementary information regarding the top important nodes. To confirm this mechanism, we performed experiments for synthetic and real-world networks and the results indicate the interesting relation between important nodes on community and network level.

Digital Modelling of Underground Volumes, Including the Visualization of Confidence Levels for the Positioning of Subsurface Objects

This paper addresses the issue of offering a consistent 3D visual rendering of subsurface objects when databases face non-completion. Digital modelling of subsurface objects, like utility lines, underground buildings or tree roots, is a difficult task. Data available are incomplete and not precise. The in situ acquisition of existing objects to increase data quality is complex and, therefore, costly. In this paper, a methodology to obtain missing spatial and geometrical data through field or empirical means is proposed. In addition, confidence levels are assigned to existing and derived spatial and geometrical attributes. They are consolidated on a class level and visualized through a bounding shape, called secondary object.

Outcome Effects, Moral Luck and the Hindsight Bias

In a series of ten preregistered experiments (N=2043), we investigate the effect of outcome valence on judgments of probability, negligence, and culpability – a phenomenon sometimes labelled moral (and legal) luck. We found that harmful outcomes, when contrasted with neutral outcomes, lead to increased perceived probability of harm ex post, and consequently to increased attribution of negligence and culpability. Rather than simply postulating a hindsight bias (as is common), we employ a variety of empirical means to demonstrate that the outcome-driven asymmetry across perceived probabilities constitutes a systematic cognitive distortion. We then explore three distinct strategies to alleviate the hindsight bias and its downstream effects on mens rea and culpability ascriptions. Not all are successful, but at least some prove promising. They should, we argue, be taken into consideration in criminal jurisprudence, where distortions due to the hindsight bias are likely considerable and deeply disconcerting.

The Problem of Coordination and the Pursuit of Structural Constraints in Psychology

Paul Meehl’s famous critique detailed many of the problematic practices and conceptual confusions that stand in the way of meaningful theoretical progress in psychological science. By integrating many of Meehl’s points, we argue that one of the reasons for the slow progress in psychology is the failure to acknowledge the problem of coordination. This problem arises whenever we attempt to measure quantities that are not directly observable but can be inferred from observable variables. The solution to this problem is far from trivial, as demonstrated by a historical analysis of thermometry. The key challenge is the specification of a functional relationship between theoretical concepts and observations. As we demonstrate, empirical means alone cannot determine this relationship. In the case of psychology, the problem of coordination has dramatic implications in the sense that it severely constrains our ability to make meaningful theoretical claims. We discuss several examples and outline some of the solutions that are currently available.

Introduction

In order to help to explain cognition, cognitive structures are assumed to be present in the mind/brain. While the empirical investigation of such structures is the task of cognitive psychology, the other cognitive science disciplines like linguistics, philosophy and artificial intelligence have an important role in suggesting hypotheses. Researchers in these disciplines increasingly test such hypotheses by empirical means themselves. In philosophy, the traditional way of referring to such structures is via concepts, i.e. those mental entities by which we conceive reality and with the help of which we reason and plan. Linguists traditionally refer to the cognitive structures as meanings—at least those linguists with a mentalistic concept of meaning do who do not think of meaning as extra-mental entities.

The Problem of Coordination and the Pursuit of Structural Constraints in Psychology

Paul Meehl’s famous critique laid out in detail many of the pathological practices and conceptual confusions that stand in the way of meaningful theoretical progress inpsychological science. Integrating some of Meehl’s points, we argue that one of the reasons for the slow progress in psychology is the failure to acknowledge the problem of coordination. This problem arises whenever we attempt to measure quantities that are not directly observable, but can be inferred from observable variables. The solution to this problem is far from trivial, as demonstrated by a historical analysis of thermometry. Also, it is not a problem that can be solved by empirical means. At its center is the need for a clear understanding of the functional relations between theoretical concepts and observations. In the case of psychology, the problem of coordination has dramatic implications in the sense that it severely limits our ability to make meaningful theoretical claims. We discuss several examples and lay out some of the solutions that are currently available.

Eliciting Understanding of Faculty Advising Role Using Caritas Processes

This study aimed to elicit faculty understanding of the advising role with nursing students in a baccalaureate program. A developmental advising centered pre/post survey were used with a caring workshop to present tools to faculty for use during advising encounters. Content and thematic analysis was used to analyze the data. Three themes emerged: Empirical means are useful for determining student motivation; providing a supportive role was central to caring; and self-reflection helps students see the reality of situations. Developmental advising, in conjunction with a caring science philosophy, has the potential to provide a formal approach to advising for nursing faculty.

Empirical Means on Pseudo-Orthogonal Groups

The present article studies the problem of computing empirical means on pseudo-orthogonal groups. To design numerical algorithms to compute empirical means, the pseudo-orthogonal group is endowed with a pseudo-Riemannian metric that affords the computation of the exponential map in closed forms. The distance between two pseudo-orthogonal matrices, which is an essential ingredient, is computed by both the Frobenius norm and the geodesic distance. The empirical-mean computation problem is solved via a pseudo-Riemannian-gradient-stepping algorithm. Several numerical tests are conducted to illustrate the numerical behavior of the devised algorithm.