Question: The negative aspects of personality traits in the workplace.

This paper explores the controversial claim that the Dark Triad of personality traits—Machiavellianism, subclinical narcissism, and subclinical psychopathy—are essentially the same in normal populations. Drawing from a sample of 245 students, we employed standardized measures to assess these three traits and investigated a range of laboratory and self-report indicators to test this assertion. While the traits showed moderate inter-correlations, they were far from being identical. The only shared Big Five personality trait among them was disagreeableness. Subclinical psychopathy was characterized by low neuroticism, while Machiavellians and psychopaths were marked by a lack of conscientiousness. Narcissism, on the other hand, had slight positive links with cognitive abilities. Both narcissists and, to a lesser degree, psychopaths demonstrated self-enhancement on two objectively scored measures. Our findings suggest that, as currently measured, the Dark Triad of personalities are overlapping yet distinct constructs, challenging the notion of their equivalence in non-pathological samples.

Affective influences abound in groups. In this article we propose an organizing model for understanding these affective influences and their effects on group life. We begin with individuallevel affective characteristics that members bring to their groups: moods, emotions, sentiments, and emotional intelligence. These affective characteristics then combine to form a group’s affective composition. We discuss explicit and implicit processes through which this affective combination occurs by examining the research on emotional contagion, entrainment, modeling, and the manipulation of affect. We also explore how elements of the affective context, such as organizationwide emotion norms and the group’s particular emotional history, may serve to constrain or amplify group members’ emotions. The outcome, group emotion, results from the combination of the group’s affective composition and the affective context in which the group is behaving. Last, we focus on the important interaction between nonaffective factors and affective factors in group life and suggest a possible agenda for future research. q 2001 Academic Press

he aim of the paper is fourfold: (a) show why humor scholars should study irony, (b) explore the need for considering interindividual differences in healthy adults’ irony performance, (c) stress the necessity for developing tools assessing habitual differences in irony performance, and (d) indicate future directions for joint irony and humor research and outline possible applications. Verbal irony is often employed with a benevolent humorous intent by speakers, but can also serve as a means of disparagement humor. In both cases, encoding and decoding activities entailing irony need to be considered in the context of the psychology of humor. We argue that verbal irony performance can be considered a phenomenon native to the realm of humor and individual differences. We point out that research has widely neglected the meaningfulness of variance in irony performance within experimental groups when looking at determinants of irony detection and production. Based on theoretical considerations and previous empirical findings we show that this variance can be easily related to individual-differences variables such as the sense of humor, dispositions toward laughter and ridicule (e.g., gelotophobia), and general mental ability. Furthermore, we hypothesize that there is an enduring trait determining irony performance we will label the sense of irony. The sense of irony possibly goes along with inclinations toward specific affective and cognitive processing patterns when dealing with verbal irony. As an application, novel irony performance tests can help to study psychological and neurophysiological correlates of irony performance more feasibly, that is, in nonclinical groups. DOI: https://doi.org/10.1037/tps0000054 Posted at the Zurich Open Repository and Archive, University of Zurich ZORA URL: https://doi.org/10.5167/uzh-124094 Accepted Version Originally published at: Bruntsch, Richard; Hofmann, Jennifer; Ruch, Willibald (2016). Virgin soil in irony research: Personality, humor, and the “sense of irony”. Translational Issues in Psychological Science, 2(1):25-34. DOI: https://doi.org/10.1037/tps0000054 VIRGIN SOIL IN IRONY RESEARCH 1 Running head: VIRGIN SOIL IN IRONY RESEARCH Virgin soil in irony research: Personality, humor, and the “sense of irony”

Endowing a dialogue system with particular personality traits is essential to deliver more human-like conversations. However, due to the challenge of embodying personality via language expression and the lack of large-scale persona-labeled dialogue data, this research problem is still far from well-studied. In this paper, we investigate the problem of incorporating explicit personality traits in dialogue generation to deliver personalized dialogues. To this end, firstly, we construct PersonalDialog, a large-scale multi-turn dialogue dataset containing various traits from a large number of speakers. The dataset consists of 20.83M sessions and 56.25M utterances from 8.47M speakers. Each utterance is associated with a speaker who is marked with traits like Age, Gender, Location, Interest Tags, etc. Several anonymization schemes are designed to protect the privacy of each speaker. This large-scale dataset will facilitate not only the study of personalized dialogue generation, but also other researches on sociolinguistics or social science. Secondly, to study how personality traits can be captured and addressed in dialogue generation, we propose persona-aware dialogue generation models within the sequence to sequence learning framework. Explicit personality traits (structured by key-value pairs) are embedded using a trait fusion module. During the decoding process, two techniques, namely persona-aware attention and persona-aware bias, are devised to capture and address trait-related information. Experiments demonstrate that our model is able to address proper traits in different contexts. Case studies also show interesting results for this challenging research problem.

The present study employed multivariate genetic item-level analyses to examine the ontology and the genetic and environmental etiology of the Big Five personality dimensions, as measured by the NEO Five Factor Inventory (NEO-FFI) [Costa and McCrae, Revised NEO personality inventory (NEO PI-R) and NEO five-factor inventory (NEO-FFI) professional manual, 1992; Hoekstra et al., NEO personality questionnaires NEO-PI-R, NEO-FFI: manual, 1996]. Common and independent pathway model comparison was used to test whether the five personality dimensions fully mediate the genetic and environmental effects on the items, as would be expected under the realist interpretation of the Big Five. In addition, the dimensionalities of the latent genetic and environmental structures were examined. Item scores of a population-based sample of 7,900 adult twins (including 2,805 complete twin pairs; 1,528 MZ and 1,277 DZ) on the Dutch version of the NEO-FFI were analyzed. Although both the genetic and the environmental covariance components display a 5-factor structure, applications of common and independent pathway modeling showed that they do not comply with the collinearity constraints entailed in the common pathway model. Implications for the substantive interpretation of the Big Five are discussed.

It has long been recognized that personality test scores are influenced by non-test-relevant response determinants. Wiggins and Rumrill (1959) distinguish three approaches to this problem. Briefly, interest in the problem of response distortion has been concerned with attempts at statistical correction for "faking good" or "faking bad" (Meehl & Hathaway, 1946), the analysis of response sets (Cronbach, 1946,1950), and ratings of the social desirability of personality test items (Edwards, 19 5 7). A further distinction can be made, however, which results in a somewhat different division of approaches to the question of response distortion. Common to both the Meehl and Hathaway corrections for faking good and faking bad and Cronbach's notion of response sets is an interest in the test behavior of the subject(S). By social desirability, on the other hand, Edwards primarily means the "scale value for any personality statement such that the scale value indicates the position of the statement on the social desirability continuum . . ." (1957, p. 3). Social desirability, thus, has been used to refer to a characteristic of test items, i.e., their scale position on a social desirability scale. Whether the test behavior of 5s or the social desirability properties of items are the focus of interest, however, it now seems clear that underlying both these approaches is the concept of statistical deviance. In the construction of the MMPI K scale, for example, items were selected which differentiated between clinically normal persons producing abnormal te¥Tpfpfiles~snd^cTinically abnormal individuals with abnormal test profiles, and between clinically abnormal persons with normal test profiles and abnormal 5s whose test records were abnormal. Keyed responses to the K scale items tend to be statistically deviant in the parent populations. Similarly, the development of the Edwards Social Desirability Scale (SDS) illustrates this procedure. Items were drawn from various MMPI scales (F, L, K, and the Manifest Anxiety Scale [Taylor, 1953]) and submitted to judges who categorized them as either socially desirable or socially undesirable. Only items on which there was unanimous agreement among the 10 judges were included in the SDS. It seems clear that the items in Edwards SDS would, of necessity, have extreme social desirability scale positions or, in other words, be statistically deviant. Some unfortunate consequences follow from the strict use of the statistical deviance model in the development of-sOcialTtesirSbTBty scales. With items drawn from the MMPI, it is apparent that in addition to their scalability for social desirability the items may also be characterized by their content which,^n a general sense, has pathological implications. When a social desrrabtltty^scale constructed according to this procedure is then applied to a college student population, the meaning of high social desirability scores is not at all clear. When 5s given the Edwards SDS deny, for example, that their sleep is fitful and disturbed (Item 6) or that they worry quite a bit over possible misfortunes (Item 35), it cannot be determined whether these responses are attributable to social desirability or to a genuine absence of such symptoms. The probability of occurrence of the symptoms represented in MMPI items (and incorportated in the SDS)


Question: The negative aspects of personality traits in the workplace.

This study sought to achieve two objectives: (a) to assess the alternate form reliability of the Narcissistic Personality Inventory (NPI), and (b) to further substantiate its construct validity by examining correlations with the four scales of the Eysenck Personality Questionnaire (EPQ). The alternate form reliability was found to be robust, with a correlation coefficient of .72. The NPI's construct validity was supported by significant positive correlations with the EPQ's Extraversion and Psychoticism scales, while the Lie scale exhibited a significant negative correlation. Interestingly, the Neuroticism scale did not show a significant relationship with narcissism. Moreover, the combined scores of Extraversion and Psychoticism scales were found to have a multiple correlation with the narcissism measure, explaining a significantly greater portion of variance in narcissism than either scale individually. This finding underscores the multidimensional nature of narcissistic traits and their complex interplay with other personality dimensions.

Using social media during work hours for non-work-related reasons is becoming commonplace. Organizations are therefore challenged with identifying and overcoming the consequences of such use. Social media-induced technostress has been identified as an important unintended consequence of using social media at work, as it could negatively impact job performance. This study draws on Person-Environment Fit to investigate the relationship between social media-induced technostress and job performance in IT professionals, and the moderating effect of job characteristics on this relationship. The results indicate that social media-induced technostress is negatively related to job performance and the negative impact of social media-induced technostress is intensified when the job characteristics are low. This work extends the literature on job-stress, social media, technostress, and job characteristics.

What makes people feel happy, engaged and challenged at work? We conducted an in situ study of Facebook and face-to-face interactions examining how they influence people's mood in the workplace. Thirty-two participants in an organization were each observed for five days in their natural work environment using automated data capture and experience sampling. Our results show that online and offline social interactions are associated with different moods, suggesting that they serve different purposes at work. Face-to-face interactions are associated with a positive mood throughout the day whereas Facebook use and engagement in work contribute to a positive feeling at the end of the day. Email use is associated with negative affect and along with multitasking, is associated with a feeling of engagement and challenge throughout the day. Our findings provide initial evidence of how online and offline interactions affect workplace mood, and could inform practices to improve employee morale.

The goal of this study was to develop and test a sequential mediational model explaining the negative relationship of passive leadership to employee well-being. Based on role stress theory, we posit that passive leadership will predict higher levels of role ambiguity, role conflict and role overload. Invoking Conservation of Resources theory, we further hypothesize that these role stressors will indirectly and negatively influence two aspects of employee well-being, namely overall mental health and overall work attitude, through psychological work fatigue. Using a probability sample of 2467 US workers, structural equation modelling supported the model by showing that role stressors and psychological work fatigue partially mediated the negative relationship between passive leadership and both aspects of employee well-being. The hypothesized, sequential indirect relationships explained 47.9% of the overall relationship between passive leadership and mental health and 26.6% of the overall relationship between passive leadership and overall work attitude. Copyright © 2016 John Wiley & Sons, Ltd.


Question: The negative impacts of personality traits in the workplace.

This study delves into the complex interplay between narcissistic personality traits and workplace dynamics, specifically exploring their impact on perceptions of leadership, workplace deviance, and task and contextual performance. Across two studies, the researchers examined how narcissism influences self-perceived and other-rated assessments of these critical workplace factors. In the first study, findings indicated that individuals with narcissistic tendencies tended to view themselves as more effective leaders, even after accounting for the influence of the Big Five personality traits. The second study further corroborated this self-aggrandizing view of leadership among narcissists, revealing a stark contrast between self-ratings and those provided by others. Interestingly, while narcissists rated themselves highly on leadership qualities, their colleagues and supervisors tended to rate them lower. Moreover, the study uncovered that narcissism was associated with inflated self-perceptions of workplace deviance and contextual performance, which were not supported by external evaluations. A noteworthy finding was the stronger negative association between narcissism and contextual performance as opposed to task performance, suggesting that narcissistic individuals may struggle more in areas requiring interpersonal skills and cooperation.

**Question**: The role of social support in the health of older adults

**Documents**: Social support and health in later life: a review of concepts, measures, and findings.
This review examines the concept of social support and its impact on the health of older adults. Social support can be defined as the provision of resources by one's social network that is intended to help individuals cope with stress and adversity. The review highlights the various types of social support, including emotional, informational, and instrumental support, and discusses the ways in which these different forms of support can influence health outcomes in later life. The review also discusses the measures used to assess social support and the methodological issues that arise when studying this topic. The findings suggest that social support has a significant impact on the health of older adults, with higher levels of social support being associated with better health outcomes, including reduced mortality, improved physical function, and lower rates of depression.

**Similar Documents**: This comprehensive review explores the critical role of social support in the health and well-being of the elderly population. Social support is conceptualized as the assistance and resources provided by one's social network, designed to aid in coping with life's stresses and challenges. The review identifies and discusses the diverse facets of social support, encompassing emotional, informational, and practical assistance, and how these distinct forms can significantly affect health outcomes in older age. It also scrutinizes the methods used to quantify social support and addresses the complexities and limitations encountered in

In this article we present a review of research on core self-evaluations, a broad personality trait indicated by 4 more narrow traits: self-esteem, generalized self-efficacy, locus of control, and emotional stability. We review evidence suggesting that the 4 core traits are highly related, load on a single unitary factor, and have dubious incremental validity controlling for their common core. We more generally investigate the construct validity of core self-evaluations. We also report on the development and validation of the first direct measure of the concept, the Core Self-Evaluations Scale (CSES). Cross-cultural evidence on the CSES is provided. We conclude by offering an agenda for future research, discussing areas where future core self-evaluations research is most needed.

In this paper, the impact of gender and Big Five personality factors (Openness, Conscientiousness, Extraversion, Agreeableness and Neuroticism) on visual media preferences (for comedy, horror, action, romance and fantasy film genres) was examined. The analyses were carried out using data collected by Stillwell’s (2007) myPersonality application on Facebook; and focused on a sub-sample of British residents aged 16-25 (n=29,197). It was confirmed that females prefer romantic films, whereas males favour action films. As predicted, there was also a marked interaction between gender and personality characteristics: females score higher on Neuroticism and Agreeableness than males. Individuals scoring high on the dimension of Openness were more likely to prefer comedy and fantasy films than romantic films. Conscientious individuals were more inclined to enjoy action and romance genres. Individuals scoring high on Neuroticism tended to prefer romantic films. Finally, significant interactions were noted between gender and Openness with regard to preferences for comedy, action and romance genres. Overall, it is certain that the combination of gender and Big Five personality factors contributes substantially to an understanding of people’s film preferences. GENDER, PERSONALITY AND FILM PREFERENCES 3 Assessing the impact of Gender and Personality on Film Preferences Do comedies have a sexually differentiated public? Are males and females who enjoy horror films similar in terms of personality? Regardless of gender, do personality characteristics influence preference for the fantasy genre? In the present study, we have attempted to provide a meaningful answer to the aforementioned set of questions, based on the analysis of gender and personality interactions with regard to film preferences. In an attempt to complement previous research findings, we focused our analysis on the role of sex and personality in film preferences for British Facebook users aged 16-25. We paid attention to their scores on online Big Five personality questionnaires and their stated gender, using both single and multivariate measures to relate the two variables to film preferences, also listed on their profiles. The following hypotheses were tested: 1. Females and males differ with regard to film preferences. 2. Personality characteristics differ according to gender. 3. Film preferences vary in accordance with personality characteristics. 4. There is a significant interaction between gender and personality with regard to film preferences. the explanatory potential of online social networks Online social networks are mass repositories of valuable social and psychological information for researchers. Facebook, ranked as the most popular social network by worldwide monthly users, hosts for instance more than 400 million user profiles, in which individuals’ number of friends, media content preferences and socio-demographic background– amongst other things are displayed. Their systematic use for data collection purposes is a relatively recent trend in social psychological research; Stillwell’s creation of the myPersonality application (2007) on Facebook is a striking exemplar of the considerable explanatory potential of those online social networks. With myPersonality, users can take an online version of the Big Five personality questionnaire and agree to share their profile information for research purposes. As such, users’ personalities can be matched to any other single or collection of individual information available on Facebook, opening up an array of possible areas of investigation. GENDER, PERSONALITY AND FILM PREFERENCES 4 the predominance of visual media The appeal of visual media is easily demonstrated in terms of the importance it has in the lives of most individuals. Data from the National Survey of Culture, Leisure and Sport (2007) indicates that watching television is the most common leisure activity for over eight out of ten men and women in England, taking precedence over spending time with friends and family, sport, etc. Another intriguing finding relates to TV and movie watching online: the number of Britons visiting such websites reached 21 million in 2007. Although it is clear that many people enjoy visual media offerings, it is much more difficult to pinpoint the specific viewer characteristics that predict genre enjoyment. In other words, who prefers which type of content? visual media content: an explanation of viewer preferences Initially the domain of inquiry of communication theorists, media preferences and their determinants are increasingly studied from the vantage point of a variety of disciplines, amongst which psychology and sociology feature predominantly (Kraaykamp et al, 2005). Their combined explanatory potential finds its articulation in the uses and gratification approach (Blumler & Katz, 1974; Rosengreen & Windhal, 1989), according to which preferences for specific media and/or specific content result from an individual’s psychological and social attributes. gender differences in media content preferences Research on media preferences has paid notable attention to sex differences in responses to different types of film. It has been established that various movie genres elicit differentiated affective responses between the two sexes. Rather interestingly, the widespread gender – stereotyping running through entertainment products seems to correspond to actual viewer preferences. Males indeed prefer action/adventure genres typically associated with the masculine sex, whilst females have a soft spot for romance/drama. Oliver et al. (2000) point to the explanatory potential of differential gender-linked expectations, which can be conceptualized along two dimensions: (1) communality, typically associated with females and (2) agency, often associated with males (Eagly, 1987). Depending on whether they are communal or agency-consistent, the themes preponderant in each genre make up for a sexually differentiated public. A number of studies highlighted the importance of sex in social affiliation with media characters: viewers tend to affiliate more strongly with and GENDER, PERSONALITY AND FILM PREFERENCES 5 experience more intense emotional responses to same-sex characters (e.g. Deutsch, 1975; Hoffner & Cantor, 1991). Another potential explanation is that of sex differences in mean personality levels, which remain fairly constant across the lifespan (Feingold, 1994; McCrae & Costa, 1984). personality differences in media preferences Theorists have also focused considerable attention on the role of personality characteristics in modern mass communication theory (Blumler & Katz, 1974; Wober, 1986). In most personality/media use models, personality is conceptualized as the nexus of attitudes, beliefs and values that guide one’s cognitive and affective interactions with the social environment (Weaver et al, 1993). Personality characteristics are thus believed to influence media preferences, which are essentially evaluative judgements pertaining to the gratifications consumers anticipate from their interaction with the media (Palmgreen, 1984). The Big Five framework of personality is most frequently employed by researchers seeking to demonstrate empirical connections between media gratifications and their psychological roots (e.g. Kraaykamp, 2001; Kraaykamp et al, 2005). It includes the following domains: Openness (traits like originality and open-mindedness), Conscientiousness (traits like hardworking and orderly), Extraversion (traits like energetic and sociable), Agreeableness (traits like affectionate and considerate) and Neuroticism (traits like nervous and tense) (Donellan & Lucas, 2008). With regard to media preferences, individuals scoring high on the dimension of Openness seem to prefer original and serious media content, whilst conscientious individuals like predictable and structured formats (Kraaykamp et al, 2005). The findings for extroverts are less straightforward: they seemingly favour sensory stimulation because of relatively high arousal levels (Costa & McCrae, 1988). Agreeableness appears to be the second most important personality trait, after Openness, in predicting visual preferences. Friendly individuals tend to watch popular content, devoid of upsetting and unconventional images (Kraykaamp, 2001). Finally, emotionally unstable individuals are thought to find easy means of escape from feelings of tension and stress in watching TV (Conway & Rubin, 1991). Most studies on personality effects in media preferences tend to either overlook or control for participants’ gender (e.g. Weaver et al, 1993; Kraaykamp, 2001). As a result, it is difficult to tell in the former case whether the differentiated association between media GENDER, PERSONALITY AND FILM PREFERENCES 6 preferences and personality characteristics is partially caused by the fact that personality traits differ systematically between people of different sexes (Kraaykamp & Van Eijck, 2005). The latter design precludes the possibility of assessing the interaction between gender and personality when it comes to predicting media taste. It seemed to us as though no one has investigated gendered differences in personality in the context of similar genre preferences.

This study aimed to investigate birth order effect on personality and academic performance amongst 120 Malaysians. Besides, it also aimed to examine the relationship between personality and academic achievement. Thirty firstborns, 30 middle children, 30 lastborns, and 30 only children, who shared the mean age of 20.0 years (SD= 1.85), were recruited into this study. Participants’ Sijil Pelajaran Malaysia (SPM) results were recorded and their personality was assessed by Ten Item Personality Inventory (TIPI). Results indicated that participants of different birth positions did not differ significantly in terms of personality and academic performance. However, Pearson’s correlation showed that extraversion correlated positively with academic performance. Keywordsbirth order; personality; academic achievement

The Multidimensional Work Motivation Scale: Validation evidence in seven languages and nine countries Marylène Gagné, Jacques Forest, Maarten Vansteenkiste, Laurence Crevier-Braud, Anja Van den Broeck, Ann Kristin Aspeli, Jenny Bellerose, Charles Benabou, Emanuela Chemolli, Stefan Tomas Güntert, Hallgeir Halvari, Devani Laksmi Indiyastuti, Peter A. Johnson, Marianne Hauan Molstad, Mathias Naudin, Assane Ndao, Anja Hagen Olafsen, Patrice Roussel, Zheni Wang & Cathrine Westbye a School of Psychology, University of Western Australia, Perth, Australia b Department of Organization and Human Resource Management, UQAM School of Management Sciences, Université du Québec à Montréal, Montréal, Canada c Department of Developmental, Personality and Social Psychology, University of Gent, Gent, Belgium d Department of Psychology, Université du Québec à Montréal, Montréal, Canada e Department of Economy and Management, Hogeschool Universiteit Brussel, Brussels, and KU Leuven, Leuven, Belgium f Faculty of Economics and Social Science, Høgskolen i Buskerud og Vestfold, Kongsberg, Norway g Department of Psychology, Université de Montréal, Montréal, Canada h Psychology of Work in Organization and Society Research Group, Swiss Federal Institute of Technology, Zurich, Switzerland i Faculty of Economics and Business, Universitas Gadjah Mada, Yogyakarta, Indonesia j Institute of Work Psychology, University of Sheffield, Sheffield, United Kingdom k International Institute of Commerce and Development, Université Panthéon Assas — Paris II, Paris, France l Faculty of Economics and Management Sciences, Université Cheikh Anta DIOP, Dakkar, Sénégal m Department of Management and Human Resources, Université Toulouse 1, Toulouse, France Published online: 05 Feb 2014.

In software development, people have a fundamental role as the basis for a project’s success. Regarding agile methodologies, this factor is increased by the need of self-organized teams, which is related to its member’s personality and the relationships between them. This paper evaluates how the member’s personality types and social relations influence the outcome of Scrum teams, based on MBTI and sociometry. As a result it was possible to identify how psychological profiles influence the quality, productivity and achievement of goals defined in the Sprint Backlog.

For the natural and social interaction it is necessary to understand human behavior. Personality is one of the fundamental aspects, by which we can understand behavioral dispositions. It is evident that there is a strong correlation between users’ personality and the way they behave on online social network (e.g., Facebook). This paper presents automatic recognition of Big-5 personality traits on social network (Facebook) using users’ status text. For the automatic recognition we studied different classification methods such as SMO (Sequential Minimal Optimization for Support Vector Machine), Bayesian Logistic Regression (BLR) and Multinomial Naïve Bayes (MNB) sparse modeling. Performance of the systems had been measured using macro-averaged precision, recall and F1; weighted average accuracy (WA) and un-weighted average accuracy (UA). Our comparative study shows that MNB performs better than BLR and SMO for personality traits recognition on the social network data.

The construct of narcissism is inconsistently defined across clinical theory, social-personality psychology, and psychiatric diagnosis. Two problems were identified that impede integration of research and clinical findings regarding narcissistic personality pathology: (a) ambiguity regarding the assessment of pathological narcissism vs. normal narcissism and (b) insufficient scope of existing narcissism measures. Four studies are presented documenting the initial derivation and validation of the Pathological Narcissism Inventory (PNI). The PNI is a 52-item self-report measure assessing 7 dimensions of pathological narcissism spanning problems with narcissistic grandiosity (Entitlement Rage, Exploitativeness, Grandiose Fantasy, Self-sacrificing Self-enhancement) and narcissistic vulnerability (Contingent Self-esteem, Hiding the Self, Devaluing). The PNI structure was validated via confirmatory factor analysis. The PNI correlated negatively with self-esteem and empathy, and positively with shame, interpersonal distress, aggression, and borderline personality organization. Grandiose PNI scales were associated with vindictive, domineering, intrusive, and overly-nurturant interpersonal problems, and vulnerable PNI scales were associated with cold, socially avoidant, and exploitable interpersonal problems. In a small clinical sample, PNI scales exhibited significant associations with parasuicidal behavior, suicide attempts, homicidal ideation, and several aspects of psychotherapy utilization.

We propose that guilt proneness is a critical characteristic of leaders and find support for this hypothesis across 3 studies. Participants in the first study rated a set of guilt-prone behaviors as more indicative of leadership potential than a set of less guilt-prone behaviors. In a follow-up study, guilt-prone participants in a leaderless group task engaged in more leadership behaviors than did less guilt-prone participants. In a third, and final, study, we move to the field and analyze 360° feedback from a group of young managers working in a range of industries. The results indicate that highly guilt-prone individuals were rated as more capable leaders than less guilt-prone individuals and that a sense of responsibility for others underlies the positive relationship between guilt proneness and leadership evaluations.


Question: Trends, Tactics, Tariffs: A longitudinal examination of Bitcoin transaction fees.

In this paper, we delve into the vulnerabilities of Bitcoin, the decentralized digital currency based on Proof-of-Work, particularly focusing on its use in rapid transactions. As Bitcoin's popularity grows and its adoption expands to various sectors, including fast-food outlets for instant payments, we scrutinize the security implications of such swift exchanges. Our analysis zeroes in on the risk of double-spending attacks, which can be executed with minimal resources against the current Bitcoin infrastructure. We reveal that the protective measures suggested by Bitcoin developers for fast transactions are insufficient to thwart double-spending. Even if these recommendations are integrated into future Bitcoin versions, the threat of double-spending remains. Drawing on our insights, we propose a lightweight solution to detect double-spending in rapid transactions, enhancing the security of Bitcoin's fast payment feature. This study underscores the need for robust countermeasures to safeguard Bitcoin transactions, especially in scenarios where goods and services are exchanged within seconds.

We introduce a technique for traversal of Merkle trees, and propose an efficient algorithm that generates a sequence of leaves along with their associated authentication paths. For one choice of parameters, and a total of N leaves, our technique requires a worst-case computational effort of 2 log N/loglog N hash function evaluations per output, and a total storage capacity of less than 1.5 log N/loglog N hash values. This is a simultaneous improvement both in space and time complexity over any previously published algorithm.

Bitcoin has recently attracted considerable attention in the fields of economics, cryptography, and computer science due to its inherent nature of combining encryption technology and monetary units. This paper reveals the effect of Bayesian neural networks (BNNs) by analyzing the time series of Bitcoin process. We also select the most relevant features from Blockchain information that is deeply involved in Bitcoin’s supply and demand and use them to train models to improve the predictive performance of the latest Bitcoin pricing process. We conduct the empirical study that compares the Bayesian neural network with other linear and non-linear benchmark models on modeling and predicting the Bitcoin process. Our empirical studies show that BNN performs well in predicting Bitcoin price time series and explaining the high volatility of the recent Bitcoin price.

The blockchain paradigm when coupled with cryptographically-secured transactions has demonstrated its utility through a number of projects, with Bitcoin being one of the most notable ones. Each such project can be seen as a simple application on a decentralised, but singleton, compute resource. We can call this paradigm a transactional singleton machine with shared-state. Ethereum implements this paradigm in a generalised manner. Furthermore it provides a plurality of such resources, each with a distinct state and operating code but able to interact through a message-passing framework with others. We discuss its design, implementation issues, the opportunities it provides and the future hurdles we envisage.

The concept of a blockchain was invented by Satoshi Nakamoto to maintain a distributed ledger for an electronic payment system, Bitcoin. In addition to its security, important performance measures of a blockchain protocol are its transaction throughput, confirmation latency and confirmation reliability. Existing systems operate far away from these physical limits. In this work we introduce Prism , a new proof-of-work blockchain protocol, which can achieve 1) security against up to 50% adversarial hashing power; 2) optimal throughput up to the capacity C of the network; 3) confirmation latency for honest transactions proportional to the propagation delay D, with confirmation error probability exponentially small in the bandwidth-delay product CD; 4) eventual total ordering of all transactions. Our approach to the design of this protocol is based on deconstructing the blockchain into its basic functionalities and systematically scaling up these functionalities to approach their physical limits.


Question: Trends, Tactics, Tariffs: A longitudinal analysis of Bitcoin transaction fees.

This paper delves into an extensive empirical analysis of distributed denial-of-service (DDoS) attacks within the Bitcoin ecosystem, examining their frequency and effects on various Bitcoin service providers. By scrutinizing over 3,000 posts from the prominent Bitcoin forum, bitcointalk.org, spanning from May 2011 to October 2013, we have identified 142 distinct DDoS attacks targeting 40 different Bitcoin services. Our findings reveal that 7% of known Bitcoin operators have fallen victim to such attacks, with currency exchanges, mining pools, gambling operators, eWallets, and financial services being disproportionately targeted. Interestingly, we observe that these frequently targeted services are more inclined to invest in DDoS protection mechanisms, such as CloudFlare, Incapsula, or Amazon Cloud. Our analysis further indicates that operators that have experienced attacks are over three times more likely to procure anti-DDoS services compared to those that have not. Moreover, we note a significant correlation between the size of mining pools (those with historical hashrate shares of at least 5%) and the likelihood of being subjected to DDoS attacks. Taking Mt. Gox as a case study, we highlight a surge in DDoS reports during the period of high trading volume and escalating exchange rates in spring 2013. We conclude by underscoring potential avenues for future research on DDoS attacks in the Bitcoin landscape.

**Question**: The Impact of Social Media on Customer Engagement in the Retail Sector

**Documents**: An Examination of the Relationship between Social Media and Customer Satisfaction in the Retail Industry
This study investigates the role of social media in enhancing customer satisfaction within the retail sector. A survey was conducted among 500 customers who have interacted with retail brands on social media platforms such as Facebook, Twitter, and Instagram. The results indicate a significant positive correlation between social media engagement and customer satisfaction. Customers who engage with brands on social media tend to report higher levels of satisfaction with the brand and are more likely to make repeat purchases. The study also found that social media can serve as an effective tool for customer service, with timely responses to customer inquiries and complaints leading to increased customer loyalty. The findings suggest that retailers should prioritize social media strategies as part of their customer engagement efforts. However, the study also highlights the importance of managing customer expectations and ensuring that social media interactions are positive and personalized. Retailers that fail to meet these expectations risk damaging their brand reputation and losing customer loyalty.

**Similar Documents

5 Nowadays computers are in the middle of most economic transactions. 6 These “computer-mediated transactions” generate huge amounts of 7 data, and new tools can be used to manipulate and analyze this data. 8 This essay offers a brief introduction to some of these tools and meth9 ods. 10 Computers are now involved in many economic transactions and can cap11 ture data associated with these transactions, which can then be manipulated 12 and analyzed. Conventional statistical and econometric techniques such as 13 regression often work well but there are issues unique to big data sets that 14 may require different tools. 15 First, the sheer size of the data involved may require more powerful data 16 manipulation tools. Second, we may have more potential predictors than 17 appropriate for estimation, so we need to do some kind of variable selection. 18 Third, large data sets may allow for more flexible relationships than simple 19 ∗Hal Varian is Chief Economist, Google Inc., Mountain View, California, and Emeritus Professor of Economics, University of California, Berkeley, California. Thanks to Jeffrey Oldham, Tom Zhang, Rob On, Pierre Grinspan, Jerry Friedman, Art Owen, Steve Scott, Bo Cowgill, Brock Noland, Daniel Stonehill, Robert Snedegar, Gary King, the editors of this journal for comments on earlier versions of this paper. 1 linear models. Machine learning techniques such as decision trees, support 20 vector machines, neural nets, deep learning and so on may allow for more 21 effective ways to model complex relationships. 22 In this essay I will describe a few of these tools for manipulating and an23 alyzing big data. I believe that these methods have a lot to offer and should 24 be more widely known and used by economists. In fact, my standard advice 25 to graduate students these days is “go to the computer science department 26 and take a class in machine learning.” There have been very fruitful collabo27 rations between computer scientists and statisticians in the last decade or so, 28 and I expect collaborations between computer scientists and econometricians 29 will also be productive in the future. 30 1 Tools to manipulate big data 31 Economists have historically dealt with data that fits in a spreadsheet, but 32 that is changing as new more detailed data becomes available; see Einav 33 and Levin [2013] for several examples and discussion. If you have more 34 than a million or so rows in a spreadsheet, you probably want to store it in a 35 relational database, such as MySQL. Relational databases offer a flexible way 36 to store, manipulate and retrieve data using a Structured Query Language 37 (SQL) which is easy to learn and very useful for dealing with medium-sized 38 data sets. 39 However, if you have several gigabytes of data or several million observa40 tions, standard relational databases become unwieldy. Databases to manage 41 data of this size are generically known as “NoSQL” databases. The term is 42 used rather loosely, but is sometimes interpreted as meaning “not only SQL.” 43 NoSQL databases are more primitive than SQL databases in terms of data 44 manipulation capabilities but can handle larger amounts of data. 45 Due to the rise of computer mediated transactions, many companies have 46 found it necessary to develop systems to process billions of transactions per 47

The decentralized digital currency Bitcoin presents an anonymous alternative to the centralized banking system and indeed enjoys widespread and increasing adoption. Recent works, however, show how users can be reidentified and their payments linked based on Bitcoin’s most central element, the blockchain, a public ledger of all transactions. Thus, many regard Bitcoin’s central promise of financial privacy as broken. In this paper, we propose CoinParty, an efficient decentralized mixing service that allows users to reestablish their financial privacy in Bitcoin and related cryptocurrencies. CoinParty, through a novel combination of decryption mixnets with threshold signatures, takes a unique place in the design space of mixing services, combining the advantages of previously proposed centralized and decentralized mixing services in one system. Our prototype implementation of CoinParty scales to large numbers of users and achieves anonymity sets by orders of magnitude higher than related work as we quantify by analyzing transactions in the actual Bitcoin blockchain. CoinParty can easily be deployed by any individual group of users, i.e., independent of any third parties, or provided as a commercial or voluntary service, e.g., as a community service by privacy-aware organizations.

http://dx.doi.org/10.1016/j.infsof.2014.09.012 0950-5849/ 2014 Elsevier B.V. All rights reserved. ⇑ Corresponding author at: Center of Informatics, Federal University of Pernambuco (CIn/UFPE), Av. Jornalista Anibal Fernandes, S/N, Cidade Universitária, 50. Recife, PE, Brazil. Tel.: +55 81 8250 0470. E-mail addresses: fss4@cin.ufpe.br (F. Selleri Silva), fsfs@cin.ufpe.br (F.S.F. Soares), alp5@cin.ufpe.br (A.L. Peres), ima2@cin.ufpe.br (I.M.d. Azevedo), aplfv@ci (A.P.L.F. Vasconcelos), fkk@cin.ufpe.br (F.K. Kamei), srlm@cin.ufpe.br (S.R.d.L. Meira). Fernando Selleri Silva a,b,⇑, Felipe Santana Furtado Soares , Angela Lima Peres , Ivanildo Monteiro de Azevedo , Ana Paula L.F. Vasconcelos , Fernando Kenji Kamei , Silvio Romero de Lemos Meira a,c

The concept of peer-to-peer applications is not new, nor is the concept of distributed hash tables. What emerged in 2008 with the publication of the Bitcoin white paper was an incentive structure that unified these two software paradigms with a set of economic stimuli to motivate the creation of a dedicated computing network orders of magnitude more powerful than the world’s fastest supercomputers. The purpose of which is the maintenance of a massive distributed database known as the Bitcoin “block chain”. Apart from the digital currency it enables, block chain technology is a fascinating new computing paradigm with broad implications for the future development of the World Wide Web, and by extension, the further growth of Linked Data and the Semantic Web. This work is divided into two main sections, we first demonstrate how block chain technologies can contribute toward the realization of a more robust Semantic Web, and subsequently we provide a framework wherein the Semantic Web is utilized to ameliorate block chain technology itself.


Question: Trends, Tactics, Tariffs: An in-depth analysis of Bitcoin transaction fees over time.

This paper delves into the perilous landscape of Bitcoin exchanges, exploring the risks that investors encounter when converting between Bitcoins and traditional currencies. Despite Bitcoin's unprecedented popularity among digital currencies, its very success has inadvertently exposed vulnerabilities that fraudsters have exploited, capitalizing on operational weaknesses and the irreversible nature of transactions. Our analysis spans a comprehensive dataset of 40 Bitcoin exchanges that have operated over the last three years, revealing a stark reality: 18 of these exchanges have ceased operations, often leaving customer account balances in shambles. While fraudulent activities are indeed a significant culprit, they do not solely account for the closures. By applying a proportional hazards model, we uncover a telling pattern: the transaction volume of an exchange is a critical indicator of its longevity. Exchanges with lower volumes are disproportionately at risk of shutting down compared to their more popular counterparts. Moreover, our logistic regression analysis highlights another intriguing trend: exchanges that are in high demand are more susceptible to security breaches. These findings underscore the complex interplay between popularity, risk, and security in the volatile world of Bitcoin exchanges.

Recognizing high-level human behavior and decisions from their digital traces are critical issues in pervasive computing systems. In this paper, we develop a novel methodology to reveal cabdrivers’ operation patterns by analyzing their continuous digital traces. For the first time, we systematically study large scale cabdrivers’ behavior in a real and complex city context through their daily digital traces. We identify a set of valuable features, which are simple and effective to classify cabdrivers, delineate cabdrivers’ operation patterns and compare the different cabdrivers’ behavior. The methodology and steps could spatially and temporally quantify, visualize, and examine different cabdrivers’ operation patterns. Drivers were categorized into top drivers and ordinary drivers by their daily income. We use the daily operations of 3000 cabdrivers in over 48 million of trips and 240 million kilometers to uncover: (1) spatial selection behavior, (2) context-aware spatio-temporal operation behavior, (3) route choice behavior, and (4) operation tactics. Though we focused on cabdriver operation patterns analysis from their digital traces, the methodology is a general empirical and analytical methodology for any GPS-like trace analysis. Our work demonstrates the great potential to utilize the massive pervasive data sets to understand human behavior and high-level intelligence. Published by Elsevier Ltd.

This paper takes a critical look at the recent trend of building electronic voting systems on top of block chain technology. Even though being very appealing from the election integrity perspective, block chains have numerous technical, economical and even political drawbacks that need to be taken into account. Selecting a good trade-off between desirable properties and restrictions imposed by different block chain implementations is a highly non-trivial task. This paper aims at bringing some clarity into performing this task. We will mostly be concentrating on public permissionless block chains and their applications as bulletin board implementations as these are the favourite choices in majority of the recent block chain based voting protocol proposals.

Participatory sensing is now becoming more popular and has shown its great potential in various applications. It was originally proposed to recruit ordinary citizens to collect and share massive amounts of sensory data using their portable smart devices. By attracting participants and paying rewards as a return, incentive mechanisms play an important role to guarantee a stable scale of participants and to improve the accuracy/coverage/timeliness of the sensing results. Along this direction, a considerable amount of research activities have been conducted recently, ranging from experimental studies to theoretical solutions and practical applications, aiming at providing more comprehensive incentive procedures and/or protecting benefits of different system stakeholders. To this end, this paper surveys the literature over the period of 2004-2014 from the state of the art of theoretical frameworks, applications and system implementations, and experimental studies of the incentive strategies used in participatory sensing by providing up-to-date research in the literature. We also point out future directions of incentive strategies used in participatory sensing.

Mobile security has become more and more important due to the boom of mobile commerce (m-commerce). However, the development of m-commerce is facing many challenges regarding data security problems. Recently, blockchain has been introduced as an effective security solution deployed successfully in many applications in practice, such as, Bitcoin, cloud computing, and Internet-of-Things. In this paper, we introduce a new m-commerce application using blockchain technology, namely, MobiChain, to secure transactions in the m-commerce. Especially, in the MobiChain application, the mining processes can be executed efficiently on mobile devices using our proposed Android core module. Through real experiments, we evaluate the performance of the proposed model and show that blockchain will be an efficient security solution for future m-commerce.

Uber is a popular ride-sharing application that matches people who need a ride (or riders) with drivers who are willing to provide it using their personal vehicles. Despite its growing popularity, there exist few studies that examine large-scale Uber data, or in general the factors affecting user participation in the sharing economy. We address this gap through a study of the Uber market that analyzes large-scale data covering 59 million rides which spans a period of 7 months. The data were extracted from email receipts sent by Uber collected on Yahoo servers, allowing us to examine the role of demographics (e.g., age and gender) on participation in the ride-sharing economy. In addition, we evaluate the impact of dynamic pricing (i.e., surge pricing) and income on both rider and driver behavior. We find that the surge pricing does not bias Uber use towards higher income riders. Moreover, we show that more homophilous matches (e.g., riders to drivers of a similar age) can result in higher driver ratings. Finally, we focus on factors that affect retention and use information from earlier rides to accurately predict which riders or drivers will become active Uber users.

Using data from 43 users across two platforms, we present a detailed look at smartphone traffic. We find that browsing contributes over half of the traffic, while each of email, media, and maps contribute roughly 10%. We also find that the overhead of lower layer protocols is high because of small transfer sizes. For half of the transfers that use transport-level security, header bytes correspond to 40% of the total. We show that while packet loss is the main factor that limits the throughput of smartphone traffic, larger send buffers at Internet servers can improve the throughput of a quarter of the transfers. Finally, by studying the interaction between smartphone traffic and the radio power management policy, we find that the power consumption of the radio can be reduced by 35% with minimal impact on the performance of packet exchanges.


Question: Trends, Tactics, Tariffs: An In-Depth Analysis of Bitcoin Transaction Fees Over Time

A Strategic Analysis of DDoS Attacks on Bitcoin Mining Pools Using Game Theory

In the digital currency ecosystem of Bitcoin, the introduction of new coins is facilitated through a process known as mining, which requires miners to perform complex proof-of-work computations. To enhance their probability of securing freshly minted bitcoins, miners often aggregate their computational power by joining mining pools. However, the intense rivalry among these pools has given rise to two distinct competitive strategies. On one hand, miners may opt to increase their investment in computing resources to boost their chances of winning the next mining challenge. On the other hand, a more malevolent approach involves launching distributed denial-of-service (DDoS) attacks to undermine the expected success of rival mining pools. This paper delves into the strategic dynamics between these two tactics through a series of game-theoretic models, specifically focusing on the competition between pools of varying sizes. We take into account the differential costs associated with investment and attacks, as well as the uncertainty surrounding the success of a DDoS assault. By identifying the equilibrium states of these games, we uncover several key insights. Notably, our analysis reveals that mining pools are more inclined to target larger pools rather than smaller ones. Moreover, we find that larger pools possess a heightened incentive to initiate attacks compared to their smaller counterparts. These findings offer a nuanced understanding of the strategic landscape within the Bitcoin mining community, highlighting the complex interplay between investment and aggression.

Client puzzles have been proposed as a mechanism for proving legitimate intentions by providing “proofs of work”, which can be applied to discourage malicious usage of resources. A typical problem of puzzle constructions is the difference in expected solving time on different computing platforms. We call puzzles which can be solved independently of client computing resources fair client puzzles. We propose a construction for client puzzles requiring widely distributed computational effort for their solution. These puzzles can be solved using the mining process of Bitcoin, or similar cryptocurrencies. Adapting existing definitions, we show that our puzzle construction satisfies formal requirements of client puzzles under reasonable assumptions. We describe a way of transforming our client puzzles for use in denial of service scenarios and demonstrate a practical construction.

The concept of peer-to-peer applications is not new, nor is the concept of distributed hash tables. What emerged in 2008 with the publication of the Bitcoin white paper was an incentive structure that unified these two software paradigms with a set of economic stimuli to motivate the creation of a dedicated computing network orders of magnitude more powerful than the world’s fastest supercomputers. The purpose of which is the maintenance of a massive distributed database known as the Bitcoin “block chain”. Apart from the digital currency it enables, block chain technology is a fascinating new computing paradigm with broad implications for the future development of the World Wide Web, and by extension, the further growth of Linked Data and the Semantic Web. This work is divided into two main sections, we first demonstrate how block chain technologies can contribute toward the realization of a more robust Semantic Web, and subsequently we provide a framework wherein the Semantic Web is utilized to ameliorate block chain technology itself.

The celebrated Nakamoto consensus protocol [16] ushered in several new consensus applications including cryptocurrencies. A few recent works [7, 17] have analyzed important properties of blockchains, including most significantly, consistency, which is a guarantee that all honest parties output the same sequence of blocks throughout the execution of the protocol. To establish consistency, the prior analysis of Pass, Seeman and Shelat [17] required a careful counting of certain combinatorial events that was difficult to apply to variations of Nakamoto. The work of Garay, Kiayas, and Leonardas [7] provides another method of analyzing the blockchain under the simplifying assumption that the network was synchronous. The contribution of this paper is the development of a simple Markov-chain based method for analyzing consistency properties of blockchain protocols. The method includes a formal way of stating strong concentration bounds as well as easy ways to concretely compute the bounds. We use our new method to answer a number of basic questions about consistency of blockchains: Our new analysis provides a tighter guarantee on the consistency property of Nakamoto's protocol, including for parameter regimes which [17] could not consider; We analyze a family of delaying attacks first presented in [17], and extend them to other protocols; We analyze how long a participant should wait before considering a high-value transaction "confirmed"; We analyze the consistency of CliqueChain, a variation of the Chainweb [14] system; We provide the first rigorous consistency analysis of GHOST [20] and also analyze a folklore "balancing"-attack. In each case, we use our framework to experimentally analyze the consensus bounds for various network delay parameters and adversarial computing percentages. We hope our techniques enable authors of future blockchain proposals to provide a more rigorous analysis of their schemes.

The concept of cryptocurrencies is built from forgotten ideas in research literature.

This paper presents ideas on how to advance the research on high quality information provisioning and information pricing. To this end, the current state of the art in combining data curation and information provisioning as well as in regard to data pricing is reviewed. Based on that, open issues, such as tailoring data to a user's need and determining a market value of data, are identified. As preliminary solutions, it is proposed to investigate the identified problems in an integrated manner.

Rate Control for Communication Networks: Shadow Prices, Proportional Fairness and Stability Author(s): F. P. Kelly, A. K. Maulloo and D. K. H. Tan Source:  The Journal of the Operational Research Society, Vol. 49, No. 3 (Mar., 1998), pp. 237-252 Published by: on behalf of the Palgrave Macmillan Journals Operational Research Society Stable URL: http://www.jstor.org/stable/3010473 Accessed: 07-10-2015 21:10 UTC

Bitcoin has recently attracted considerable attention in the fields of economics, cryptography, and computer science due to its inherent nature of combining encryption technology and monetary units. This paper reveals the effect of Bayesian neural networks (BNNs) by analyzing the time series of Bitcoin process. We also select the most relevant features from Blockchain information that is deeply involved in Bitcoin’s supply and demand and use them to train models to improve the predictive performance of the latest Bitcoin pricing process. We conduct the empirical study that compares the Bayesian neural network with other linear and non-linear benchmark models on modeling and predicting the Bitcoin process. Our empirical studies show that BNN performs well in predicting Bitcoin price time series and explaining the high volatility of the recent Bitcoin price.


Question: Trends, Tactics, Tariffs: A Longitudinal Analysis of Bitcoin Transaction Costs

Exploring the Dynamics of Bitcoin Transaction Fees: A Comprehensive Analysis

As Bitcoin continues to gain prominence as a preferred digital payment system on the internet, it is increasingly attracting the attention of law enforcement agencies and regulatory bodies. These entities are collaborating to develop strategies to combat illicit activities involving Bitcoin transactions. One such measure involves the blacklisting of specific transaction prefixes at points of sale where goods and services are exchanged for Bitcoin. This initiative aims to deter the circulation of blacklisted Bitcoins, thereby reducing their liquidity and value. Consequently, every entity accepting Bitcoin payments must not only monitor incoming transactions for potential blacklisting but also evaluate the risk of future transactions being blacklisted.

In this context, we delve into the evolving landscape of Bitcoin transactions, outlining a risk assessment framework. Our approach leverages publicly available data to predict the likelihood of a transaction being blacklisted. Using historical data from documented thefts, we present initial findings that shed light on the predictive accuracy of our model. Moreover, we scrutinize the implications of this risk assessment on Bitcoin trading markets and question Bitcoin's efficacy as a stable unit of account, considering the potential volatility introduced by blacklisting practices.

Our study underscores the need for a robust risk scoring mechanism for Bitcoin transactions, highlighting the importance of proactive measures in ensuring the integrity and reliability of the Bitcoin ecosystem. As Bitcoin matures, stakeholders must remain vigilant against emerging threats and adopt strategies that mitigate risks associated with blacklisting and other security challenges.

The celebrated Nakamoto consensus protocol [16] ushered in several new consensus applications including cryptocurrencies. A few recent works [7, 17] have analyzed important properties of blockchains, including most significantly, consistency, which is a guarantee that all honest parties output the same sequence of blocks throughout the execution of the protocol. To establish consistency, the prior analysis of Pass, Seeman and Shelat [17] required a careful counting of certain combinatorial events that was difficult to apply to variations of Nakamoto. The work of Garay, Kiayas, and Leonardas [7] provides another method of analyzing the blockchain under the simplifying assumption that the network was synchronous. The contribution of this paper is the development of a simple Markov-chain based method for analyzing consistency properties of blockchain protocols. The method includes a formal way of stating strong concentration bounds as well as easy ways to concretely compute the bounds. We use our new method to answer a number of basic questions about consistency of blockchains: Our new analysis provides a tighter guarantee on the consistency property of Nakamoto's protocol, including for parameter regimes which [17] could not consider; We analyze a family of delaying attacks first presented in [17], and extend them to other protocols; We analyze how long a participant should wait before considering a high-value transaction "confirmed"; We analyze the consistency of CliqueChain, a variation of the Chainweb [14] system; We provide the first rigorous consistency analysis of GHOST [20] and also analyze a folklore "balancing"-attack. In each case, we use our framework to experimentally analyze the consensus bounds for various network delay parameters and adversarial computing percentages. We hope our techniques enable authors of future blockchain proposals to provide a more rigorous analysis of their schemes.

0968-090X/$ see front matter 2011 Published b doi:10.1016/j.trc.2011.12.004 ⇑ Corresponding author. Tel.: +44 20 7679 7214. E-mail address: n.lathia@cs.ucl.ac.uk (N. Lathia). The increasing availability of sensor data in urban areas now offers the opportunity to perform continuous evaluations of transport systems and measure the effects of policy changes, in an empirical, large-scale, and non-invasive way. In this paper, we study one such example: the effect of changing the user-access policy in the London Barclays Cycle Hire scheme. When the scheme was launched in July 2010, users were required to apply for a key to access to the system. By December 2010, this policy was overridden in order to allow for ‘‘casual’’ usage, so that anyone in possession of a debit or credit card could gain access. While the transport authority measured the policy shift’s success by the increased number of trips, we set out to investigate how the change affected the system’s usage throughout the city. We present an extensive analysis of station data collected from the scheme’s web site both preand post-policy change, showing how differences in both global and local behaviour can be measured, and how the policy change correlates with a variety of effects observed around the city. We find that, as expected, quicker access to the system correlates with greater week end usage; it also reinforces the week-day commuting trend. In both the preand post-change periods, the geographic distribution of activity at individual stations forms concentric circles around central London. However, upon policy change, a number of stations undergo a complete usage change, now exhibiting an opposite trend with respect to that which they had prior to the policy change. 2011 Published by Elsevier Ltd.

Credit cards provide benefits to consumers and merchants not provided by other payment instruments as evidenced by their explosive growth in the number and value of transactions over the last 20 years. Recently, credit card networks have come under scrutiny from regulators and antitrust authorities around the world. The costs and benefits of credit cards to network participants are discussed. Focusing on interrelated bilateral transactions, several theoretical models have been constructed to study the implications of several business practices of credit card networks. The results and implications of these economic models along with future research topics are discussed.


Question: A sliding conditional gradient method in convex optimization problems.

This paper introduces a groundbreaking method for tackling a significant subset of convex programming (CP) problems, specifically, stochastic composite optimization (SCO). SCO is characterized by an objective function that is the aggregate of a general nonsmooth and a smooth stochastic component. Given that SCO encompasses non-smooth, smooth, and stochastic CP as its special cases, the foundational complexity theory of convex programming establishes a valid lower bound for the convergence rate of algorithms designed to solve these problems. Until now, however, algorithms capable of attaining this lower bound had not been devised. Herein, we demonstrate that the straightforward mirror-descent stochastic approximation method achieves the best-known convergence rate for SCO. Our primary contribution lies in the introduction of the accelerated stochastic approximation (AC-SA) algorithm, inspired by Nesterov’s optimal method for smooth CP [32,34]. We prove that AC-SA can attain the established lower bound on the convergence rate for SCO, marking it as the first universally optimal algorithm in the literature for addressing non-smooth, smooth, and stochastic CP problems. This is underscored through a detailed comparison with existing methods, particularly in solving a specific yet extensive class of stochastic programming problems, highlighting the substantial superiority of the AC-SA algorithm.

In this paper we propose a new framework for learning from large scale datasets based on iterative learning from small mini-batches. By adding the right amount of noise to a standard stochastic gradient optimization algorithm we show that the iterates will converge to samples from the true posterior distribution as we anneal the stepsize. This seamless transition between optimization and Bayesian posterior sampling provides an inbuilt protection against overfitting. We also propose a practical method for Monte Carlo estimates of posterior statistics which monitors a “sampling threshold” and collects samples after it has been surpassed. We apply the method to three models: a mixture of Gaussians, logistic regression and ICA with natural gradients.

Motivated by a real-world problem, we study a novel budgeted optimization problem where the goal is to optimize an unknown function f(·) given a budget by requesting a sequence of samples from the function. In our setting, however, evaluating the function at precisely specified points is not practically possible due to prohibitive costs. Instead, we can only request constrained experiments. A constrained experiment, denoted by Q, specifies a subset of the input space for the experimenter to sample the function from. The outcome of Q includes a sampled experiment x, and its function output f(x). Importantly, as the constraints of Q become looser, the cost of fulfilling the request decreases, but the uncertainty about the location x increases. Our goal is to manage this trade-off by selecting a set of constrained experiments that best optimize f(·) within the budget. We study this problem in two different settings, the non-sequential (or batch) setting where a set of constrained experiments is selected at once, and the sequential setting where experiments are selected one at a time. We evaluate our proposed methods for both settings using synthetic and real functions. The experimental results demonstrate the efficacy of the proposed methods.

When applying sparse representation techniques to images, the standard approach is to independently compute the representations for a set of overlapping image patches. This method performs very well in a variety of applications, but results in a representation that is multi-valued and not optimized with respect to the entire image. An alternative representation structure is provided by a convolutional sparse representation, in which a sparse representation of an entire image is computed by replacing the linear combination of a set of dictionary vectors by the sum of a set of convolutions with dictionary filters. The resulting representation is both single-valued and jointly optimized over the entire image. While this form of a sparse representation has been applied to a variety of problems in signal and image processing and computer vision, the computational expense of the corresponding optimization problems has restricted application to relatively small signals and images. This paper presents new, efficient algorithms that substantially improve on the performance of other recent methods, contributing to the development of this type of representation as a practical tool for a wider range of problems.

introduction to nonlinear optimization theory algorithms introduction to nonlinear optimization theory algorithms introduction to nonlinear optimization theory algorithms introduction to nonlinear optimization theory algorithms chapter 16: introduction to nonlinear programming nonlinear programming: concepts, algorithms and applications theory, algorithms, and applications with matlab introduction to nonlinear optimization epubsam course: math 657|optimization theory time: spring 2016, 5 convex optimization: modeling and algorithms nonlinear programming 13 mit massachusetts institute an introduction to nonlinear optimization theory ebook nonlinear optimization lecture notes for the course mat a and trends in optimization with e applications solving optimization problems using the matlab siam bestsellers 2000–2015 order author title code 2015 optimization an introduction imperial college london [review published in siam review, vol. 58, issue 1, pp. 164.] nonlinear optimization and applications springer convex analysis and nonlinear optimization theory and examples appm 4720/5720 advanced topics in convex optimization lecture slides on nonlinear programming based on lectures convex optimization — boyd & vandenberghe

We present an iterative linear-quadratic-Gaussian method for locally-optimal feedback control of nonlinear stochastic systems subject to control constraints. Previously, similar methods have been restricted to deterministic unconstrained problems with quadratic costs. The new method constructs an affine feedback control law, obtained by minimizing a novel quadratic approximation to the optimal cost-to-go function. Global convergence is guaranteed through a Levenberg-Marquardt method; convergence in the vicinity of a local minimum is quadratic. Performance is illustrated on a limited-torque inverted pendulum problem, as well as a complex biomechanical control problem involving a stochastic model of the human arm, with 10 state dimensions and 6 muscle actuators. A Matlab implementation of the new algorithm is availabe at www.cogsci.ucsd.edu//spl sim/todorov.

As a testament to their success, the theory of random forests has long been outpaced by their application in practice. In this paper, we take a step towards narrowing this gap by providing a consistency result for online random forests.

In this manuscript we review new ideas and first results on application of the Graphical Models approach, originated from Statistical Physics, Information Theory, Computer Science and Machine Learning, to optimization problems of network flow type with additional constraints related to the physics of the flow. We illustrate the general concepts on a number of enabling examples from power system and natural gas transmission (continental scale) and distribution (district scale) systems. 1.1 Introductory remarks In this chapter we discuss optimization problems which appears naturally in the classical settings describing flows over networks constrained by the physical nature Michael Chertkov Theoretical Division, T-4 & CNLS, Los Alamos National Laboratory Los Alamos, NM 87545, USA and Energy System Center, Skoltech, Moscow, 143026, Russia, e-mail: chertkov@lanl. gov Sidhant Misra Theoretical Division, T-5, Los Alamos National Laboratory Los Alamos, NM 87545, USA, e-mail: sidhant@lanl.gov Marc Vuffray Theoretical Division, T-4, Los Alamos National Laboratory Los Alamos, NM 87545, USA, e-mail: sidhant@lanl.gov Krishnamurthy Dvijotham Pacific Northwest National Laboratory, PO Box 999, Richland, WA 99352, USA e-mail: krishnamurthy.dvijotham@pnnl.gov Pascal Van Hentenryck University of Michigan, Department of Industrial & Operations Engineering Ann Arbor, MI 48109, USA, e-mail: pvanhent@umich.edu 1 ar X iv :1 70 2. 01 89 0v 1 [ cs .S Y ] 7 F eb 2 01 7 2 M. Chertkov, S. Misra, M. Vuffray, D. Krishnamurthy, and P. Van Hentenryck of the flows which appear in the context of electric power systems, see e.g. [27, 44], and natural gas application, see e.g. [13] and references there in. Other examples of physical flows where similar optimization problem arise include pipe-flow systems, such as district heating [75, 1] and water [54], as well as traffic systems [40]. We aim to show that the network flow optimization problem can be stated naturally in terms of the so-called Graphical Models (GM). In general, GMs for optimization and inference are wide spread in statistical disciplines such as Applied Probability, Machine Learning and Artificial Intelligence [53, 29, 16, 12, 32, 50], Information Theory [55] and Statistical Physics [47]. Main benefit of adopting GM methodology to the physics-constrained network flows is in modularity and flexibility of the approach – any new constraints, set of new variables, and any modification of the optimization objective can be incorporated in the GM formulation with an ease. Besides, if all (or at least majority of) constraints and modifications are factorized, i.e. can be stated in terms of a small subset of variables, underlying GM optimization or GM statistical inference problems can be solved exactly or approximately with the help of an emerging set of techniques, algorithms and computational approaches coined collectively Belief Propagation (BP), see e.g. an important original paper [74] and recent reviews [47, 55, 66]. It is also important to emphasize that an additional benefit of the GM formulation is in its principal readiness for generalization. Even though we limit our discussion to application of the GM and BP framework to deterministic optimizations, many probabilistic and/or mixed generalizations (largely not discussed in this paper) fit very naturally in this universal framework as well. We will focus on optimization problems associated with Physics-Constrained Newtork Flow (PCNF) problems. Structure of the networks will obviously be inherited in the GM formulation, however indirectly through graphand variabletransformations and modifications. Specifically, next Section 1.2 is devoted solely to stating a number of exemplary energy system formulations in GM terms. Thus, in Section 1.2.1 and Section 1.2.2 we consider dissipation optimal and respectively general physics-constrained network flow problems. In particular, Section 1.2.2 includes discussion of power flow problems in both power-voltage, Section 1.2.2.1, and current-voltage, Section 1.2.2.2, formats, as well as discussion of the gas flow formulation in Section 1.2.2.3 and general k-component physics-constrained network flow problem in Section 1.2.2.4. Section 1.2.3 describes problems of the next level of complexity – these including optimization over resources. In particular, general optimal physics-controlled network flow problem is discussed in Section 1.2.3.1 and more specific cases of optimal flows, involving optimal power flow (in both power-flow and current-voltage formulations) and gas flows are discussed in Sections 1.2.3.2,1.2.3.3,1.2.2.3, respectively. Section 1.2.4 introduces a number of feasibility problems, all stated as special kinds of optimizations. Here we discuss the so-called instanton, Section 1.2.4.1, containment Section 1.2.4.2, and state estimation, Section 1.2.4.3, formulation. The long introductory section concludes with a discussion in Section 1.2.5 of an exemplary (and even more) complex optimization involving split of resources between participants/aggregators. 1 Graphical Models for Optimal Flows 3 In Section 1.3 we describe how any of the aforementioned PCNF and optimal PCNF problems can be re-stated in the universal Graphical Model format. Then, in Section 1.4, we take advantage of the factorized form of the PCNF GM and illustrate how BP methodology can be used to solve the optimization problems exactly and/or approximately. Specifically, in Section 1.4.1 we restate the optimization (Maximum Likelihood) GM problem as a Linear Programming (LP) in the space of beliefs (proxies for probabilities). The resulting LP is generally difficult as working with all variables in a combination. We take advantage of the GM factorization and introduce in Section 1.4.2 the so-called Linear Programming Belief Propagation (LP-BP) relaxation, providing a provable lower bound for the optimal. Finally, in Section 1.4.3 we construct a tractable relaxation of LP-BP based on an interval partitioning of the underlying space. Section 1.5 discuss hierarchies which allow to generalize, and thus improve LPBP. The so-called LP-BP hierarchies, related to earlier papers on the subject [65, 30, 63] are discussed in Section 1.5.1. Then, relation between the LP-BP hierarchies and classic LP-based Sherali-Adams [59] and Semi-Definite-Programming based Lasserre hierarchies [41, 36, 52, 37] are discussed in Section 1.5.2. Section 1.6 discuss the special case of a GM defined over a tree (graph without loops). In this case LP-BP is exac, equivalent to the so-called Dynamic Programming approach, and as such it provides a distributed alternative to the global optimization through a sequence of graph-element-local optimizations. However, even in the tree case the exact LP-BP and/or DP are not tractable for GM stated in terms of physical variables, such as flows, voltages and/or pressures, drawn from a continuous set. Following, [18] we discuss here how the problem can be resolved with a proper interval-partitioning (discretization). We conclude the manuscript presenting summary and discussing path forward in Section 1.7. 1.2 Problems of Interest: Formulations In this Section we formulate a number of physics-constrained network flow problems which we will then attempt to analyze and solve with the help of Graphical Model (GM)/Belief Propagation (BP) approaches/techniques in the following Sections. 1.2.1 Dissipation-Optimal Network Flow We start introducing/discussing Network Flows constrained by a minimum dissipation principle, i.e. one which can be expressed as an unconstrained optimization/minimization of an energy function (potential). 4 M. Chertkov, S. Misra, M. Vuffray, D. Krishnamurthy, and P. Van Hentenryck Consider a static flow of a commodity over an undirected graph, G = (V ,E ) described through the following network flow equations i ∈ V : qi = ∑ j:(i, j)∈E φi j, (1.1) where qi stand for injection, qi > 0, or consumption, qi < 0, of the flow at the node i and φi j =−φ ji stands for the value of the flow through the directed edge (i, j) – in the direction from i to j 1. We consider a balanced network, ∑i∈V qi = 0. We constraint the flow requiring that the minimum dissipation principle is obeyed min φ ∑ {i, j}∈E Ei j(φi j) ∣∣∣∣ Eq. (1.1) , (1.2) where φ . = (φi j = −φ ji|{i, j} ∈ E ), and Ei j(x) are local (energy) functions of their arguments for all {i, j} ∈ E . The local energy functions Ei j(x) are required to be convex at least on a restricted domain. We call the sum of local energy functions E(φ) = ∑{i, j}∈E Ei j(φi j) the global energy function or simply the energy function. Versions of this problem appear in the context of the feasibility analysis of the dissipative network flows, that is flows whose redistribution over the network is constrained by potentials, e.g. voltages or pressures in the context of resistive electric networks and gas flow networks, respectively [22, 48, 64]. Note, that the formulation (1.2) can also be supplemented by additional flow or potential constraints. Requiring Karush-Kuhn-Tucker (KKT) stationary point conditions on the optimization problem stated in Eq. (1.2) leads to the following set of equations ∀{i, j} ∈ E : E ′ i j(φi j) = λi−λ j, (1.3) where λi is a Lagrangian multiplier corresponding to the i’s equation (1.1). The problem becomes fully defined by the pair of Eqs. (1.1,1.3), which can also be restated solely in terms of the λ -variables i ∈ V : qi = ∑ j:{i, j}∈E ( E ′ i j )−1 (λi−λ j). (1.4) 1.2.2 General Physics-Constrained Network Flows We call “unconstrained” a network flow for which only conservation of flow(s), described by Eq. (1.1), is enforced. Contrariwise we call “Physics-constrained” a 1 In the following we will use notation {i, j} for the undirected graph and (i, j) for the respective directed graph. When the meaning is clear we slightly abuse notations denoting by E both the set of undirected and directed edges. 1 Graphical Models for Optimal Flows 5 network flow t

This paper presents the Averaged CVB (ACVB) inference and offers convergence-guaranteed and practically useful fast Collapsed Variational Bayes (CVB) inferences. CVB inferences yield more precise inferences of Bayesian probabilistic models than Variational Bayes (VB) inferences. However, their convergence aspect is fairly unknown and has not been scrutinized. To make CVB more useful, we study their convergence behaviors in a empirical and practical approach. We develop a convergence-guaranteed algorithm for any CVB-based inference called ACVB, which enables automatic convergence detection and frees non-expert practitioners from the difficult and costly manual monitoring of inference processes. In experiments, ACVB inferences are comparable to or better than those of existing inference methods and deterministic, fast, and provide easier convergence detection. These features are especially convenient for practitioners who want precise Bayesian inference with assured convergence.

An optimal control problem to find the fastest collision-free trajectory of a robot surrounded by obstacles is presented. The collision avoidance is based on linear programming arguments and expressed as state constraints. The optimal control problem is solved with a sequential programming method. In order to decrease the number of unknowns and constraints a backface culling active set strategy is added to the resolution technique.

Kernel method has been developed as one of the standard approaches for nonlinear learning, which however, does not scale to large data set due to its quadratic complexity in the number of samples. A number of kernel approximation methods have thus been proposed in the recent years, among which the random features method gains much popularity due to its simplicity and direct reduction of nonlinear problem to a linear one. Different random feature functions have since been proposed to approximate a variety of kernel functions. Among them the Random Binning (RB) feature, proposed in the first random-feature paper [21], has drawn much less attention than the Random Fourier (RF) feature proposed also in [21]. In this work, we observe that the RB features, with right choice of optimization solver, could be orders-of-magnitude more efficient than other random features and kernel approximation methods under the same requirement of accuracy. We thus propose the first analysis of RB from the perspective of optimization, which by interpreting RB as a Randomized Block Coordinate Descent in the infinite-dimensional space, gives a faster convergence rate compared to that of other random features. In particular, we show that by drawing R random grids with at least κ number of non-empty bins per grid in expectation, RB method achieves a convergence rate of O(1/κ R)), which not only sharpens its O(1/√R) rate from Monte Carlo analysis, but also shows a κ times speedup over other random features under the same analysis framework. In addition, we demonstrate another advantage of RB in the L1-regularized setting, where unlike other random features, a RB-based Coordinate Descent solver can be parallelized with guaranteed speedup proportional to κ. Our extensive experiments demonstrate the superior performance of the RB features over other random features and kernel approximation methods.