Central European University - Budapest Campus

Qualitative Comparative Analysis (QCA) and other set-theoretic methods distinguish themselves from other approaches to the study of social phenomena by using sets and the search for set relations. In virtually all social science fields, statements about social phenomena can be framed in terms of set relations, and using set-theoretic methods to investigate these statements is therefore highly valuable. This book guides readers through the basic principles of set theory and then on to the applied practices of QCA. It provides a thorough understanding of basic and advanced issues in set-theoretic methods together with tricks of the trade, software handling and exercises. Most arguments are introduced using examples from existing research. The use of QCA is increasing rapidly and the application of set-theory is both fruitful and still widely misunderstood in current empirical comparative social research. This book provides the comprehensive guide to these methods for researchers across the social sciences.

Reasoning is generally seen as a means to improve knowledge and make better decisions. However, much evidence shows that reasoning often leads to epistemic distortions and poor decisions. This suggests that the function of reasoning should be rethought. Our hypothesis is that the function of reasoning is argumentative. It is to devise and evaluate arguments intended to persuade. Reasoning so conceived is adaptive given the exceptional dependence of humans on communication and their vulnerability to misinformation. A wide range of evidence in the psychology of reasoning and decision making can be reinterpreted and better explained in the light of this hypothesis. Poor performance in standard reasoning tasks is explained by the lack of argumentative context. When the same problems are placed in a proper argumentative setting, people turn out to be skilled arguers. Skilled arguers, however, are not after the truth but after arguments supporting their views. This explains the notorious confirmation bias. This bias is apparent not only when people are actually arguing, but also when they are reasoning proactively from the perspective of having to defend their opinions. Reasoning so motivated can distort evaluations and attitudes and allow erroneous beliefs to persist. Proactively used reasoning also favors decisions that are easy to justify but not necessarily better. In all these instances traditionally described as failures or flaws, reasoning does exactly what can be expected of an argumentative device: Look for arguments that support a given conclusion, and, ceteris paribus, favor conclusions for which arguments can be found.

The complexity of many biological, social and technological systems stems from the richness of the interactions among their units. Over the past decades, a variety of complex systems has been successfully described as networks whose interacting pairs of nodes are connected by links. Yet, from human communications to chemical reactions and ecological systems, interactions can often occur in groups of three or more nodes and cannot be described simply in terms of dyads. Until recently little attention has been devoted to the higher-order architecture of real complex systems. However, a mounting body of evidence is showing that taking the higher-order structure of these systems into account can enhance our modeling capacities and help us understand and predict their dynamical behavior. Here we present a complete overview of the emerging field of networks beyond pairwise interactions. We discuss how to represent higher-order interactions and introduce the different frameworks used to describe higher-order systems, highlighting the links between the existing concepts and representations. We review the measures designed to characterize the structure of these systems and the models proposed to generate synthetic structures, such as random and growing bipartite graphs, hypergraphs and simplicial complexes. We introduce the rapidly growing research on higher-order dynamical systems and dynamical topology, discussing the relations between higher-order interactions and collective behavior. We focus in particular on new emergent phenomena characterizing dynamical processes, such as diffusion, synchronization, spreading, social dynamics and games, when extended beyond pairwise interactions. We conclude with a summary of empirical applications, and an outlook on current modeling and conceptual frontiers.

Identifying fundamental drivers of science and developing predictive models to capture its evolution are instrumental for the design of policies that can improve the scientific enterprise-for example, through enhanced career paths for scientists, better performance evaluation for organizations hosting research, discovery of novel effective funding vehicles, and even identification of promising regions along the scientific frontier. The science of science uses large-scale data on the production of science to search for universal and domain-specific patterns. Here, we review recent developments in this transdisciplinary field.

Aus dem Dt. übers. - "Qualitative Comparative Analysis (QCA) and other set-theoretic methods distinguish themselves from other approaches to the study of social phenomena by using sets and the search for set relations. In virtually all social science fields, statements about social phenomena can be framed in terms of set relations, and using set-theoretic methods to investigate these statements is therefore highly valuable. This book guides readers through the basic principles of set theory and then on to the applied practices of QCA. It provides a thorough understanding of basic and advanced issues in set-theoretic methods together with tricks of the trade, software handling and exercises. Most arguments are introduced using examples from existing research. The use of QCA is increasing rapidly and the application of set-theory is both fruitful and still widely misunderstood in current empirical comparative social research. This book provides an invaluable guide to these methods for researchers across the social sciences"

Existing technologies, institutions, and behavioral norms together act to constrain the rate and magnitude of carbon emissions reductions in the coming decades. The inertia of carbon emissions due to such mutually reinforcing physical, economic, and social constraints is referred to as carbon lock-in. Carbon lock-in is a special case of path dependency, which is common in the evolution of complex systems. However, carbon lock-in is particularly prone to entrenchment given the large capital costs, long infrastructure lifetimes, and interrelationships between the socioeconomic and technical systems involved. Further, the urgency of efforts to avoid dangerous climate change exacerbates the liability of even small lock-in risks. Although carbon lock-in has been recognized for years, efforts to characterize the types and causes of carbon lock-in, or to quantitatively assess and evaluate its policy implications, have been limited and scattered across a number of different disciplines. This systematic review of the literature synthesizes what is known about the types and causes of carbon lock-in, including the scale, magnitude, and longevity of the effects, and policy implications. We identify three main types of carbon lock-in and describe how they coevolve: (a) infrastructural and technological, (b) institutional, and (c) behavioral. Although each type of lock-in has its own set of processes, all three are tightly intertwined and contribute to the inertia of carbon emissions. We outline the conditions, opportunities, and strategies for fostering transitions toward less-carbon-intensive emissions trajectories. We conclude by proposing a carbon lock-in research agenda that can help bridge the gaps between science, knowledge, and policy-making.

The complexity of many biological, social and technological systems stems from the richness of the interactions among their units. Over the past decades, a variety of complex systems has been successfully described as networks whose interacting pairs of nodes are connected by links. Yet, from human communications to chemical reactions and ecological systems, interactions can often occur in groups of three or more nodes and cannot be described simply in terms of dyads. Until recently little attention has been devoted to the higher-order architecture of real complex systems. However, a mounting body of evidence is showing that taking the higher-order structure of these systems into account can enhance our modeling capacities and help us understand and predict their dynamical behavior. Here we present a complete overview of the emerging field of networks beyond pairwise interactions. We discuss how to represent higher-order interactions and introduce the different frameworks used to describe higher-order systems, highlighting the links between the existing concepts and representations. We review the measures designed to characterize the structure of these systems and the models proposed to generate synthetic structures, such as random and growing bipartite graphs, hypergraphs and simplicial complexes. We introduce the rapidly growing research on higher-order dynamical systems and dynamical topology, discussing the relations between higher-order interactions and collective behavior. We focus in particular on new emergent phenomena characterizing dynamical processes, such as diffusion, synchronization, spreading, social dynamics and games, when extended beyond pairwise interactions. We conclude with a summary of empirical applications, and an outlook on current modeling and conceptual frontiers.

Despite extensive evidence showing that exposure to specific chemicals can lead to disease, current research approaches and regulatory policies fail to address the chemical complexity of our world. To safeguard current and future generations from the increasing number of chemicals polluting our environment, a systematic and agnostic approach is needed. The "exposome" concept strives to capture the diversity and range of exposures to synthetic chemicals, dietary constituents, psychosocial stressors, and physical factors, as well as their corresponding biological responses. Technological advances such as high-resolution mass spectrometry and network science have allowed us to take the first steps toward a comprehensive assessment of the exposome. Given the increased recognition of the dominant role that nongenetic factors play in disease, an effort to characterize the exposome at a scale comparable to that of the human genome is warranted.

Post-Communist Party Systems examines democratic party competition in four post-communist polities in the mid-1990s: Bulgaria, the Czech Republic, Hungary, and Poland. Legacies of pre-communist rule turn out to play as much a role in accounting for differences as the institutional differences incorporated in the new democratic rules of the game. The book demonstrates various developments within the four countries with regard to different voter appeal of parties, patterns of voter representation, and dispositions to join other parties in legislative or executive alliances. The authors also present interesting avenues of comparison for broader sets of countries.

There is extensive, yet fragmented, evidence of gender differences in academia suggesting that women are underrepresented in most scientific disciplines and publish fewer articles throughout a career, and their work acquires fewer citations. Here, we offer a comprehensive picture of longitudinal gender differences in performance through a bibliometric analysis of academic publishing careers by reconstructing the complete publication history of over 1.5 million gender-identified authors whose publishing career ended between 1955 and 2010, covering 83 countries and 13 disciplines. We find that, paradoxically, the increase of participation of women in science over the past 60 years was accompanied by an increase of gender differences in both productivity and impact. Most surprisingly, though, we uncover two gender invariants, finding that men and women publish at a comparable annual rate and have equivalent career-wise impact for the same size body of work. Finally, we demonstrate that differences in publishing career lengths and dropout rates explain a large portion of the reported career-wise differences in productivity and impact, although productivity differences still remain. This comprehensive picture of gender inequality in academia can help rephrase the conversation around the sustainability of women's careers in academia, with important consequences for institutions and policy makers.

This paper focuses on the role of international actors in policy/ knowledge transfer processes to suggest a dynamic for the transnationalization of policy results. The paper seeks to redress the tendency towards methodological nationalism in much of the early policy transfer literature by bringing to the fore the role of international organizations and non-state actors in transnational transfer networks. Secondly, attention is drawn to ‘soft’ forms of transfer – such as the spread of norms – as a necessary complement to the hard transfer of policy tools, structures and practices and in which non-state actors play a more prominent role. Thirdly, transnational networks are identified as an important vehicle for the spread of policy and practice not only cross-nationally but in emergent venues of global governance.

The lack of predictability of citation-based measures frequently used to gauge impact, from impact factors to short-term citations, raises a fundamental question: Is there long-term predictability in citation patterns? Here, we derive a mechanistic model for the citation dynamics of individual papers, allowing us to collapse the citation histories of papers from different journals and disciplines into a single curve, indicating that all papers tend to follow the same universal temporal pattern. The observed patterns not only help us uncover basic mechanisms that govern scientific impact but also offer reliable measures of influence that may have potential policy implications.

Adopted by the United Nations General Assembly, December 16, 1966. Preamble The States Parties to the present Covenant, Considering that, in accordance with the principles proclaimed in the Charter of the United Nations, recognition of the inherent dignity and of the equal and inalienable rights of all members of the human family is the foundation of freedom, justice and peace in the world, Recognizing that these rights derive from the inherent dignity of the human person, Recognizing that, in accordance with the Universal Declaration of Human Rights, the ideal of free human beings enjoying civil and political freedom and freedom from fear and want can only be achieved if conditions are created whereby everyone may enjoy his civil and political rights, as well as his economic, social and cultural rights, Considering the obligation of States under the Charter of the United Nations to promote universal respect for, and observance of, human rights and freedoms, Realizing that the individual, having duties to other individuals and to the community to which he belongs, is under a responsibility to strive for the promotion and observance of the rights recognized in the present Covenant, Agree upon the following articles: PART I Article 1 All peoples have the right of self-determination. By virtue of that right they freely determine their political status and freely pursue their economic, social and cultural development. All peoples may, for their own ends, freely dispose of their natural wealth and resources without prejudice to any obligations arising out of international economic co-operation, based upon the principle of mutual benefit, and international law. In no case may a people be deprived of its own means of subsistence. […]

In order to avoid both theoretically eclectic and redundant approaches to constructivism, this article proposes one possible and coherent reconstruction of constructivism understood as a reflexive meta-theory. This reconstruction starts by taking seriously the double sociological and interpretivist turn of the social sciences. Based on `double hermeneutics', constructivism is perhaps best understood by distinguishing its position on the level of observation, the level of action proper, and the relationship between these two levels. On the basis of this distinction, the article argues that constructivism is epistemologically about the social construction of knowledge and ontologically about the construction of social reality. It furthermore asks us to combine a social theory of knowledge with an intersubjective, not an individualist, theory of action. Finally, the analysis of power is central to understanding the reflexive link between the two levels of observation and action. The argument is embedded in a contextualization where constructivism is seen as inspired by `reflexive modernity', as well as more directly by the end of the Cold War.

When making decisions about energy, consumers and policymakers typically overlook moral issues, which can have profound societal consequences. This Perspective explores how ideas from justice and ethics can provide a framework to reconsider energy problems and better inform decision-making processes.

What makes humans moral beings? This question can be understood either as a proximate “how” question or as an ultimate “why” question. The “how” question is about the mental and social mechanisms that produce moral judgments and interactions, and has been investigated by psychologists and social scientists. The “why” question is about the fitness consequences that explain why humans have morality, and has been discussed by evolutionary biologists in the context of the evolution of cooperation. Our goal here is to contribute to a fruitful articulation of such proximate and ultimate explanations of human morality. We develop an approach to morality as an adaptation to an environment in which individuals were in competition to be chosen and recruited in mutually advantageous cooperative interactions. In this environment, the best strategy is to treat others with impartiality and to share the costs and benefits of cooperation equally. Those who offer less than others will be left out of cooperation; conversely, those who offer more will be exploited by their partners. In line with this mutualistic approach, the study of a range of economic games involving property rights, collective actions, mutual help and punishment shows that participants’ distributions aim at sharing the costs and benefits of interactions in an impartial way. In particular, the distribution of resources is influenced by effort and talent, and the perception of each participant’s rights on the resources to be distributed.

Despite the frequent use of numerous quantitative indicators to gauge the professional impact of a scientist, little is known about how scientific impact emerges and evolves in time. Here, we quantify the changes in impact and productivity throughout a career in science, finding that impact, as measured by influential publications, is distributed randomly within a scientist's sequence of publications. This random-impact rule allows us to formulate a stochastic model that uncouples the effects of productivity, individual ability, and luck and unveils the existence of universal patterns governing the emergence of scientific success. The model assigns a unique individual parameter Q to each scientist, which is stable during a career, and it accurately predicts the evolution of a scientist's impact, from the h-index to cumulative citations, and independent recognitions, such as prizes.

Here we identify hundreds of new drug-disease associations for over 900 FDA-approved drugs by quantifying the network proximity of disease genes and drug targets in the human (protein-protein) interactome. We select four network-predicted associations to test their causal relationship using large healthcare databases with over 220 million patients and state-of-the-art pharmacoepidemiologic analyses. Using propensity score matching, two of four network-based predictions are validated in patient-level data: carbamazepine is associated with an increased risk of coronary artery disease (CAD) [hazard ratio (HR) 1.56, 95% confidence interval (CI) 1.12-2.18], and hydroxychloroquine is associated with a decreased risk of CAD (HR 0.76, 95% CI 0.59-0.97). In vitro experiments show that hydroxychloroquine attenuates pro-inflammatory cytokine-mediated activation in human aortic endothelial cells, supporting mechanistically its potential beneficial effect in CAD. In summary, we demonstrate that a unique integration of protein-protein interaction network proximity and large-scale patient-level longitudinal data complemented by mechanistic in vitro studies can facilitate drug repurposing.

Migration studies have focused attention on ethnic institutions in global and gateway cities. This ethnic lens distorts migration scholarship, reinforces methodological nationalism, and disregards the role of city scale in shaping migrant pathways of settlement and transnational connection. The scale of cities reflects their positioning within neoliberal processes of local, national, regional, and global rescaling. To encourage further explorations of nonethnic pathways that may be salient in small-scale cities, we examine born-again Christianity as a means of migrant incorporation locally and transnationally in two small-scale cities, one in the United States and the other in Germany.

The COVID-19 pandemic has highlighted the need to quickly and reliably prioritize clinically approved compounds for their potential effectiveness for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections. Here, we deployed algorithms relying on artificial intelligence, network diffusion, and network proximity, tasking each of them to rank 6,340 drugs for their expected efficacy against SARS-CoV-2. To test the predictions, we used as ground truth 918 drugs experimentally screened in VeroE6 cells, as well as the list of drugs in clinical trials that capture the medical community’s assessment of drugs with potential COVID-19 efficacy. We find that no single predictive algorithm offers consistently reliable outcomes across all datasets and metrics. This outcome prompted us to develop a multimodal technology that fuses the predictions of all algorithms, finding that a consensus among the different predictive methods consistently exceeds the performance of the best individual pipelines. We screened in human cells the top-ranked drugs, obtaining a 62% success rate, in contrast to the 0.8% hit rate of nonguided screenings. Of the six drugs that reduced viral infection, four could be directly repurposed to treat COVID-19, proposing novel treatments for COVID-19. We also found that 76 of the 77 drugs that successfully reduced viral infection do not bind the proteins targeted by SARS-CoV-2, indicating that these network drugs rely on network-based mechanisms that cannot be identified using docking-based strategies. These advances offer a methodological pathway to identify repurposable drugs for future pathogens and neglected diseases underserved by the costs and extended timeline of de novo drug development.