Nicole Sintov

Mood-related drinking motives mediate the familial association between major depression and alcohol dependence.

BACKGROUND Major depression and alcohol dependence co-occur within individuals and families to a higher than expected degree. This study investigated whether mood-related drinking motives mediate the association between major depression and alcohol dependence, and what the genetic and environmental bases are for this relationship. METHODS The sample included 5,181 individuals from the Virginia Adult Twin Study of Psychiatric and Substance Use Disorders, aged 30 and older. Participants completed a clinical interview which assessed lifetime major depression, alcohol dependence, and mood-related drinking motives. RESULTS Mood-related drinking motives significantly explained the depression-alcohol dependence relationship at both the phenotypic and familial levels. Results from twin analyses indicated that for both males and females, the familial factors underlying mood-related drinking motives accounted for virtually all of the familial variance that overlaps between depression and alcohol dependence. CONCLUSIONS The results are consistent with an indirect role for mood-related drinking motives in the etiology of depression and alcohol dependence, and suggest that mood-related drinking motives may be a useful index of vulnerability for these conditions.

Unlocking the potential of smart grid technologies with behavioral science

Smart grid systems aim to provide a more stable and adaptable electricity infrastructure, and to maximize energy efficiency. Grid-linked technologies vary widely in form and function, but generally share common potentials: to reduce energy consumption via efficiency and/or curtailment, to shift use to off-peak times of day, and to enable distributed storage and generation options. Although end users are central players in these systems, they are sometimes not central considerations in technology or program design, and in some cases, their motivations for participating in such systems are not fully appreciated. Behavioral science can be instrumental in engaging end-users and maximizing the impact of smart grid technologies. In this paper, we present emerging technologies made possible by a smart grid infrastructure, and for each we highlight ways in which behavioral science can be applied to enhance their impact on energy savings.

Utility customer segmentation based on smart meter data: Empirical study

We develop statistical techniques for analyzing the energy information in the 15-min and daily household electricity consumption data. The results provide a good understanding of how usage is affected by environmental, structural and customer features. The analytics yield productive results for a small region, and perform well in other areas and in different seasons. These are versatile tools for identifying specific lifestyle and defining customer segments that can yield measurable results and high returns for energy programs. The data analytics also explore the changes in statistical properties of individual versus aggregated data when customers are clustered.

Empirically Defined Subtypes of Alcohol Dependence in an Irish Family Sample

Alcohol dependence (AD) is clinically and etiologically heterogeneous. The goal of this study was to explore AD subtypes among a sample of 1221 participants in the Irish Affected Sib Pair Study of Alcohol Dependence, all of whom met DSM-IV criteria for AD. Variables used to identify the subtypes included major depressive disorder, antisocial personality disorder, illicit drug dependence (cannabis, sedatives, stimulants, cocaine, opioids, and hallucinogens), nicotine dependence, the personality traits of neuroticism and novelty seeking, and early alcohol use. Using latent class analysis, a 3-class solution was identified as the most parsimonious description of the data. Individuals in a Mild class were least likely to have comorbid psychopathology, whereas a severe class had highest probabilities of all comorbid psychopathology. The third class was characterized by high probabilities of major depression and higher neuroticism scores, but lower likelihood of other comorbid disorders than seen in the severe class. Overall, sibling pair resemblance for class was stronger within than between classes, and was greatest for siblings within the severe class, suggesting a stronger familial etiology for this class. These findings are consistent with the affective regulation and behavioral disinhibition subtypes of alcoholism, and are in line with prior work suggesting familial influences on subtype etiology.

What goes on behind closed doors? How college dormitory residents change to save energy during a competition-based energy reduction intervention

Purpose The purpose of this paper was to evaluate the impact of a competition-based intervention combining high-resolution electricity feedback, incentives, information and prompts on college dormitory residents’ energy consumption and participation in demand response events. The authors also investigated changes in individual-level pro-environmental behaviors and examined psychosocial correlates of behavior change. Design/methodology/approach Residents of 39 suites in a freshman residence hall competed against one another to reduce energy consumption and win prizes as part of a three-week competition. Feedback was provided in near real-time at the suite-level via an interactive touch-screen kiosk. Participants also completed baseline and follow-up surveys. Findings Electricity use among all suites was approximately 6.4 per cent lower during the competition period compared to baseline, a significant reduction. Additionally, participants reported engaging in various pro-environmental behaviors significantly more frequently during the competition relative to baseline. Changes in pro-environmental behavior were associated with changes in level of group identification and perceived social norms. Practical implications In three weeks, dormitory residents saved 3,158 kWh of electricity compared to baseline – the equivalent of more than 3,470 pounds of carbon dioxide emissions. The findings provide evidence that real-time feedback, combined with incentives, information and prompts, can motivate on-campus residents to reduce energy consumption. Originality/value The authors contribute to a limited body of evidence supporting the effectiveness of dorm energy competitions in motivating college students to save energy. In addition, the authors identified individual-level behavioral and psychosocial changes made during such an intervention. University residential life planners may also use the results of this research to inform student programming.

Divide to Defend: Collusive Security Games

Research on security games has focused on settings where the defender must protect against either a single adversary or multiple, independent adversaries. However, there are a variety of real-world security domains where adversaries may benefit from colluding in their actions against the defender, e.g., wildlife poaching, urban crime and drug trafficking. Given such adversary collusion may be more detrimental for the defender, she has an incentive to break up collusion by playing off the self-interest of individual adversaries. As we show in this paper, breaking up such collusion is difficult given bounded rationality of human adversaries; we therefore investigate algorithms for the defender assuming both rational and boundedly rational adversaries. The contributions of this paper include i collusive security games COSGs, a model for security games involving potential collusion among adversaries, ii SPECTRE-R, an algorithm to solve COSGs and break collusion assuming rational adversaries, iii observations and analyses of adversary behavior and the underlying factors including bounded rationality, imbalanced- resource-allocation effect, coverage perception, and individualism/collectivism attitudes within COSGs with data from 700 human subjects, iv a learned human behavioral model that incorporates these factors to predict when collusion will occur, v SPECTRE-BR, an enhanced algorithm which optimizes against the learned behavior model to provide demonstrably better performing defender strategies against human subjects compared to SPECTRE-R.

Beware the Soothsayer: From Attack Prediction Accuracy to Predictive Reliability in Security Games

Interdicting the flow of illegal goods (such as drugs and ivory) is a major security concern for many countries. The massive scale of these networks, however, forces defenders to make judicious use of their limited resources. While existing solutions model this problem as a Network Security Game (NSG), they do not consider humans’ bounded rationality. Previous human behavior modeling works in Security Games, however, make use of large training datasets that are unrealistic in real-world situations; the ability to effectively test many models is constrained by the time-consuming and complex nature of field deployments. In addition, there is an implicit assumption in these works that a model’s prediction accuracy strongly correlates with the performance of its corresponding defender strategy (referred to as predictive reliability). If the assumption of predictive reliability does not hold, then this could lead to substantial losses for the defender. In the following paper, we (1) first demonstrate that predictive reliability is indeed strong for previous Stackelberg Security Game experiments. We also run our own set of human subject experiments in such a way that models are restricted to learning on dataset sizes representative of real-world constraints. In the analysis on that data, we demonstrate that (2) predictive reliability is extremely weak for NSGs. Following that discovery, however, we identify (3) key factors that influence predictive reliability results: the training set’s exposed attack surface and graph structure.

Cognitive Accessibility as a New Factor in Proenvironmental Spillover: Results From a Field Study of Household Food Waste Management:

An emerging body of literature has contributed to understanding behavioral spillover; however, a limited range of behaviors and psychological pathways have been studied. The current study investigates whether starting to compost, a relatively difficult behavior receiving limited attention in the spillover literature, results in spillover to household waste prevention behaviors, including food, energy, and water waste prevention. It also tests cognitive accessibility as a new mediator in the spillover process, and advances an integrative process model to address methodological inconsistencies in the spillover literature. Data are from a 2015 longitudinal field experiment to increase composting. Participants (N = 284) were residents of Costa Mesa, California, who received a structural intervention (i.e., curbside organic waste bins) and procedural information about composting. Positive spillover was observed. Additionally, cognitive accessibility partially mediated the relationship between composting and energy and water waste–prevention behaviors. Future research should adopt a consistent definition of spillover and explore additional pathways.

Personalized Energy Reduction Cyber-physical System PERCS: A Gamified End-User Platform for Energy Efficiency and Demand Response

The mission of the Personalized Energy Reduction Cyber-physical System PERCS is to create new possibilities for improving building operating efficiency, enhancing grid reliability, avoiding costly power interruptions, and mitigating greenhouse gas emissions. PERCS proposes to achieve these outcomes by engaging building occupants as partners in a user-centered smart service platform. Using a non-intrusive load monitoring approach, PERCS uses a single sensing point in each home to capture smart electric meter data in real time. The household energy signal is disaggregated into individual load signatures of common appliances e.g., air conditioners, yielding near real-time appliance-level energy information. Users interact
with PERCS via a mobile phone platform that provides household- and appliance-level energy feedback, tailored recommendations, and a competitive game tied to energy use and behavioral changes. PERCS challenges traditional energy management approaches by directly engaging occupant as key elements in a technological system.

Predicting information security policy compliance intentions and behavior for six employee-based risks

ABSTRACT Employees’ non-compliance with organizational information security policies poses a significant threat to organizations. Enhancing our understanding of compliance behavior is crucial for improving security. Although research has identified numerous psychological factors that affect intentions to comply with security policies, how such intentions map onto actual compliance behavior is not well understood. Building on a well-supported model of security policy compliance intentions, we evaluate compliance with each of six types of information security policies using decision vignettes, and compare parameters across models. The study contributes to information security compliance research by examining each risk separately and exploring heterogeneity across risk types.