"The Psychology of Risk: A Brief Primer"

Risk is commonly defined in negative terms-the probability of suffering a loss or factors and actions involving uncertain dangers or hazards. On the other hand, the term risk as used in the social sciences relies on simply the degree of uncertainty: It merely addresses how much variance exists among the possible outcomes associated with a particular choice or action. A counterintuitive example is the classifying of an investment that is certain to lose $5 as less risky than one that has an equal chance of yielding a gain of$10. Andreassen states that uncertainty and value are treated as separate entities because expanding the notion of risk to include gains as well as losses adds considerable conceptual power. Economic theories based on perfect rationality are undoubtedly powerful. Andreassen states that id one wanted to predict human behavior in the simplest manner, one would certainly begin by assuming that people are motivated by self-interest, and that they can be extremely calculating when valuable opportunities arise, learning quickly from the success of others. Research on the psychology of risk does not begin by assuming that all human behavior is irrational, random, or thoughtless. Rather, this research has centered on how people may be biased by myriad social influences, the perceived choices available, or the cognitive rules of thumb used to simplify difficult economic and social decisions.

Reducing the Top Quark Mass Uncertainty with Jet Grooming

The measurement of the top quark mass has large systematic uncertainties coming from the Monte Carlo simulations that are used to match theory and experiment. We explore how much that uncertainty can be reduced by using jet grooming procedures. We estimate the inherent ambiguity in what is meant by Monte Carlo mass to be around 530 MeV without any corrections. This uncertainty can be reduced by 60% to 200 MeV by calibrating to the W mass and a further 33% to 140 MeV by applying soft-drop jet grooming (or by 20% more to 170 MeV with trimming). At e+e- colliders, the associated uncertainty is around 110 MeV, reducing to 50 MeV after calibrating to the W mass. By analyzing the tuning parameters, we conclude that the importance of jet grooming after calibrating to the W mass is to reduce sensitivity to the underlying event.Comment: 21 pages, 7 figure

Study addiction - a new area of psychological study: conceptualization, assessment, and preliminary empirical findings

Aims: Recent research has suggested that for some individuals, educational studying may become compulsive and excessive and lead to ‘study addiction’. The present study conceptualized and assessed study addiction within the framework of workaholism, defining it as compulsive over-involvement in studying that interferes with functioning in other domains and that is detrimental for individuals and/or their environment. Methods: The Bergen Study Addiction Scale (BStAS) was tested - reflecting seven core addiction symptoms (salience, mood modification, tolerance, withdrawal, conflict, relapse, and problems) - related to studying. The scale was administered via a cross-sectional survey distributed to Norwegian (n = 218) and Polish (n = 993) students with additional questions concerning demographic variables, study-related variables, health, and personality. Results: A one-factor solution had acceptable fit with the data in both samples and the scale demonstrated good reliability. Scores on BStAS converged with scores on learning engagement. Study addiction (BStAS) was significantly related to specific aspects of studying (longer learning time, lower academic performance), personality traits (higher neuroticism and conscientiousness, lower extroversion), and negative health-related factors (impaired general health, decreased quality of life and sleep quality, higher perceived stress). Conclusions: It is concluded that BStAS has good psychometric properties, making it a promising tool in the assessment of study addiction. Study addiction is related in predictable ways to personality and health variables, as predicted from contemporary workaholism theory and research

JUNIPR: a Framework for Unsupervised Machine Learning in Particle Physics

In applications of machine learning to particle physics, a persistent challenge is how to go beyond discrimination to learn about the underlying physics. To this end, a powerful tool would be a framework for unsupervised learning, where the machine learns the intricate high-dimensional contours of the data upon which it is trained, without reference to pre-established labels. In order to approach such a complex task, an unsupervised network must be structured intelligently, based on a qualitative understanding of the data. In this paper, we scaffold the neural network's architecture around a leading-order model of the physics underlying the data. In addition to making unsupervised learning tractable, this design actually alleviates existing tensions between performance and interpretability. We call the framework JUNIPR: "Jets from UNsupervised Interpretable PRobabilistic models". In this approach, the set of particle momenta composing a jet are clustered into a binary tree that the neural network examines sequentially. Training is unsupervised and unrestricted: the network could decide that the data bears little correspondence to the chosen tree structure. However, when there is a correspondence, the network's output along the tree has a direct physical interpretation. JUNIPR models can perform discrimination tasks, through the statistically optimal likelihood-ratio test, and they permit visualizations of discrimination power at each branching in a jet's tree. Additionally, JUNIPR models provide a probability distribution from which events can be drawn, providing a data-driven Monte Carlo generator. As a third application, JUNIPR models can reweight events from one (e.g. simulated) data set to agree with distributions from another (e.g. experimental) data set.Comment: 37 pages, 24 figure

Enhancing Automated Test Selection in Probabilistic Networks

In diagnostic decision-support systems, test selection amounts to selecting, in a sequential manner, a test that is expected to yield the largest decrease in the uncertainty about a patient’s diagnosis. For capturing this uncertainty, often an information measure is used. In this paper, we study the Shannon entropy, the Gini index, and the misclassification error for this purpose. We argue that the Gini index can be regarded as an approximation of the Shannon entropy and that the misclassification error can be looked upon as an approximation of the Gini index. We further argue that the differences between the first derivatives of the three functions can explain different test sequences in practice. Experimental results from using the measures with a real-life probabilistic network in oncology support our observations