Phase Transitions of Civil Unrest across Countries and Time

Phase transitions, characterized by abrupt shifts between macroscopic patterns of organization, are ubiquitous in complex systems. Despite considerable research in the physical and natural sciences, the empirical study of this phenomenon in societal systems is relatively underdeveloped. The goal of this study is to explore whether the dynamics of collective civil unrest can be plausibly characterized as a sequence of recurrent phase shifts, with each phase having measurable and identifiable latent characteristics. Building on previous efforts to characterize civil unrest as a self-organized critical system, we introduce a macro-level statistical model of civil unrest and evaluate its plausibility using a comprehensive dataset of civil unrest events in 170 countries from 1946 to 2017. Our findings demonstrate that the macro-level phase model effectively captures the characteristics of civil unrest data from diverse countries globally and that universal mechanisms may underlie certain aspects of the dynamics of civil unrest. We also introduce a scale to quantify a country's long-term unrest per unit of time and show that civil unrest events tend to cluster geographically, with the magnitude of civil unrest concentrated in specific regions. Our approach has the potential to identify and measure phase transitions in various collective human phenomena beyond civil unrest, contributing to a better understanding of complex social systems.Comment: Main paper (57 pages); Supporting Information (144 pages) will be available upon request. To appear in npj Complexit

Patterns in Payout Policy and Payout Channel Choice of UK Firms in the 1990s

The paper examines the payout policy of UK firms listed on the London Stock Exchange during the 1990s.We complement the existing payout literature studies by analyzing jointly the trends in dividends and share repurchases.Unlike in the US, we find that, in the UK, firms do not demonstrate a decreasing propensity to distribute funds to shareholders.The role of share repurchases is increasing, but dividends still constitute a vast proportion of the total payout.Firms repurchasing shares usually pay dividends as well.We also document that there is a strong relationship between the presence of blockholders and the choice of the payout channel: firms with concentrated ownership tend to opt for dividends rather than share repurchases, irrespectively of the identity of the controlling shareholder.We argue that the differential taxation of dividends and capital gains as well as the insider trading regulation affect the relative attractiveness of dividends and share repurchases to large shareholders.Payout policy;dividends;share repurchases;taxes;power indices;Banzhaf index;ownership structure;corporate governance

What Lies Beneath: How Paranoid Cognition Explains the Relations Between Transgender Employees\u27 Perceptions of Discrimination at Work and their Job Attitudes and Wellbeing

With the recent public gender transitions of celebrities like Caitlin Jenner, greater visibility of transgender characters on television (e.g., Transparent), and controversial laws enacted in some U.S. states and cities banning transgender employees from accessing bathrooms that align with their gender identities, issues of gender expression have been thrust into the national spotlight. In order to promote greater awareness and acceptance of transgender people, greater knowledge of their life experiences is needed. Adding to a small, but growing, body of research on the work experiences of transgender individuals, the goal of the present study is to examine the cognitive processes that shape these individuals\u27 experiences in the workplace. Drawing on existing theory and research on paranoia, we examine the role of paranoid cognition, defined by hypervigilance, rumination, and sinister attributional tendencies, in explaining the relations between transgender employees\u27 perceptions of workplace discrimination and their job attitudes and psychological wellbeing. Our findings suggest that perceptions of transgender discrimination in the workplace are positively related to paranoid cognition at work; paranoid cognition is positively related to transgender employees\u27 turnover intentions and emotional exhaustion and negatively related to their job satisfaction; and paranoid cognition at work mediates the relations between perceptions of discrimination and each of these outcomes. We conclude by discussing the implications of our results, as well as avenues for future research on the work experiences of transgender employees

Credibility analysis of textual claims with explainable evidence

Despite being a vast resource of valuable information, the Web has been polluted by the spread of false claims. Increasing hoaxes, fake news, and misleading information on the Web have given rise to many fact-checking websites that manually assess these doubtful claims. However, the rapid speed and large scale of misinformation spread have become the bottleneck for manual verification. This calls for credibility assessment tools that can automate this verification process. Prior works in this domain make strong assumptions about the structure of the claims and the communities where they are made. Most importantly, black-box techniques proposed in prior works lack the ability to explain why a certain statement is deemed credible or not. To address these limitations, this dissertation proposes a general framework for automated credibility assessment that does not make any assumption about the structure or origin of the claims. Specifically, we propose a feature-based model, which automatically retrieves relevant articles about the given claim and assesses its credibility by capturing the mutual interaction between the language style of the relevant articles, their stance towards the claim, and the trustworthiness of the underlying web sources. We further enhance our credibility assessment approach and propose a neural-network-based model. Unlike the feature-based model, this model does not rely on feature engineering and external lexicons. Both our models make their assessments interpretable by extracting explainable evidence from judiciously selected web sources. We utilize our models and develop a Web interface, CredEye, which enables users to automatically assess the credibility of a textual claim and dissect into the assessment by browsing through judiciously and automatically selected evidence snippets. In addition, we study the problem of stance classification and propose a neural-network-based model for predicting the stance of diverse user perspectives regarding the controversial claims. Given a controversial claim and a user comment, our stance classification model predicts whether the user comment is supporting or opposing the claim.Das Web ist eine riesige Quelle wertvoller Informationen, allerdings wurde es durch die Verbreitung von Falschmeldungen verschmutzt. Eine zunehmende Anzahl an Hoaxes, Falschmeldungen und irreführenden Informationen im Internet haben viele Websites hervorgebracht, auf denen die Fakten überprüft und zweifelhafte Behauptungen manuell bewertet werden. Die rasante Verbreitung großer Mengen von Fehlinformationen sind jedoch zum Engpass für die manuelle Überprüfung geworden. Dies erfordert Tools zur Bewertung der Glaubwürdigkeit, mit denen dieser Überprüfungsprozess automatisiert werden kann. In früheren Arbeiten in diesem Bereich werden starke Annahmen gemacht über die Struktur der Behauptungen und die Portale, in denen sie gepostet werden. Vor allem aber können die Black-Box-Techniken, die in früheren Arbeiten vorgeschlagen wurden, nicht erklären, warum eine bestimmte Aussage als glaubwürdig erachtet wird oder nicht. Um diesen Einschränkungen zu begegnen, wird in dieser Dissertation ein allgemeines Framework für die automatisierte Bewertung der Glaubwürdigkeit vorgeschlagen, bei dem keine Annahmen über die Struktur oder den Ursprung der Behauptungen gemacht werden. Insbesondere schlagen wir ein featurebasiertes Modell vor, das automatisch relevante Artikel zu einer bestimmten Behauptung abruft und deren Glaubwürdigkeit bewertet, indem die gegenseitige Interaktion zwischen dem Sprachstil der relevanten Artikel, ihre Haltung zur Behauptung und der Vertrauenswürdigkeit der zugrunde liegenden Quellen erfasst wird. Wir verbessern unseren Ansatz zur Bewertung der Glaubwürdigkeit weiter und schlagen ein auf neuronalen Netzen basierendes Modell vor. Im Gegensatz zum featurebasierten Modell ist dieses Modell nicht auf Feature-Engineering und externe Lexika angewiesen. Unsere beiden Modelle machen ihre Einschätzungen interpretierbar, indem sie erklärbare Beweise aus sorgfältig ausgewählten Webquellen extrahieren. Wir verwenden unsere Modelle zur Entwicklung eines Webinterfaces, CredEye, mit dem Benutzer die Glaubwürdigkeit einer Behauptung in Textform automatisch bewerten und verstehen können, indem sie automatisch ausgewählte Beweisstücke einsehen. Darüber hinaus untersuchen wir das Problem der Positionsklassifizierung und schlagen ein auf neuronalen Netzen basierendes Modell vor, um die Position verschiedener Benutzerperspektiven in Bezug auf die umstrittenen Behauptungen vorherzusagen. Bei einer kontroversen Behauptung und einem Benutzerkommentar sagt unser Einstufungsmodell voraus, ob der Benutzerkommentar die Behauptung unterstützt oder ablehnt

Determinants and consequences of budget reallocations

We investigate the determinants and consequences of budget reallocations, i.e., corrective actions to the budget made during the year. Using proprietary data of a large consumer goods manufacturer, we analyze the extent to which allocation decisions regarding the initial budget drive subsequent reallocations. Whenever scarce resources need to be allocated among a number of individuals, power struggles and politicking behavior are likely to arise, which potentially affects the outcome of the allocation process. We hypothesize and find that one important driver of reallocation decisions is the firm's aim to correct for systematic deviations from the optimal initial budget allocation that are driven by successful lobbying activities during the initial budgeting process. In a more exploratory analysis, we show that such reallocations do not have the desired effects on market-place performance. In particular, budget cuts are negatively associated with a product's change in market share. More surprisingly, while budget boosts do help product lines internally to achieve their sales targets in the last quarter, they do not have a (positive) effect on the change in market share. Most importantly, our results demonstrate that efficient investment planning ex ante is essential to achieve an improvement in market-place performance, highlighting the value of budgeting.Series: Department of Strategy and Innovation Working Paper Serie