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In the current paper, we discuss the need for regulation at EU level of Connected and Automated Driving solutions (henceforth CAD) based on multiple considerations, namely (i) the need for uniformity of criteria across European Member States, and (ii) the impact that regulation—or the absence of it—has on the proliferation of specific technological solutions. The analysis is grounded on legal and economic considerations of possible interactions between vehicles with different levels of automation, and shows how the existing framework delays innovation. A Risk-Management Approach, identifying one sole responsible party ex ante (one-stop-shop), liable under all circumstances—pursuant to a strict, if not absolute liability rule—is to be preferred. We analyse the solution adopted by some Member States in light of those considerations and conclude that none truly corresponds to a RMA approach, and differences will also cause market fragmentation. We conclude that because legal rules determine what kind of technological application is favoured over others—and thence they are not technology-neutral—uniformity across MSs is of essential relevance, and discuss possible policy approaches to be adopted at European level.

In this paper,we present an original study on the use of social media data to analyze the structure of the global health networks (GHNs) relative to health organizations targeted to malaria, tuberculosis (TBC) and pneumonia as well as twitter popularity, evaluating the performance of their strategies in response to the arising health threats. We use a machine learning ensemble classifier and social network analysis to discover the Twitter users that represent organizations or groups active for each disease. We have found evidence that the GHN of TBC is the more mature, active and global. Meanwhile, the networks of malaria and pneumonia are found to be less connected and lacking global coverage. Our analysis validates the use of social media to analyze GHNs and to propose these networks as an important organizational tool in mobilizing the community versus global sustainable development goals.

We present a new network model accounting for multidimensional assortativity. Each node is characterized by a number of features and the probability of a link between two nodes depends on common features. We do not fix a priori the total number of possible features. The bipartite network of the nodes and the features evolves according to a stochastic dynamics that depends on three parameters that respectively regulate the preferential attachment in the transmission of the features to the nodes, the number of new features per node, and the power-law behavior of the total number of observed features. Our model also takes into account a mechanism of triadic closure. We provide theoretical results and statistical estimators for the parameters of the model. We validate our approach by means of simulations and an empirical analysis of a network of scientific collaborations.

The exact shape of the distribution of city size is subject to considerable scholarly debate, as competing theoretical models yield different implications. The alternative distributions being tested are typically the Pareto and the log-normal, whose finite sample upper tail behavior is very difficult to tell apart. Using data at different levels of aggregation (census blocks and cities) we show that the tail behavior of the distribution changes upon aggregation, and the final result depends crucially on the shape of the distribution of the number of elementary units associated with each aggregate element.

In the current COVID-19 pandemic, we witness sharp fluctuations of biopharmaceutical equity prices as a response to innovation related news. On 23.04.2020, the stock price of Biotech firm Gilead Sciences fell by as much as 9% after rumors suggested that remdesivir, its experimental COVID-19 treatment, showed no benefit. One week later, official clinical trial results suggesting the opposite have led to a rebounce of 11% in equity value. Jumps in equity prices of this amplitude are frequent in the biopharmaceutical industry, yet we lack knowledge about the underlying forces that drive them. In this paper we investigate the impact of product innovation on firm equity value by linking venture specific characteristics to stock market reactions using a large data set of the biopharmaceutical industry. We find that market reactions increase in the portfolio importance of the product under development, measured by product specific net present value data, and diminish in its success probability, estimated by a combination of supervised learning methods. Our results aid corporations and investors to evaluate the financial consequences of innovation related news.

In this work we introduce and analyze a new and comprehensive multilayer dataset covering a wide spectrum of international relationships between coutries. We select two cross sections of the dataset corresponding to years 2003 and 2010 with 19 layers and 112 nodes to study the structure and evolution of the network. Country centrality is measured by the multiplex PageRank (MultiRank) and the multiplex hub and authority scores (MultiHub and MultiAuth). We find that the MultiHub measure has the highest correlation to GDP per capita, with respect to the other multilayer measures and to their single layer analogues. Finally we analyze the differences in the ranking between GDP per capita and the multilayer centrality measures to evaluate them as measures of development.

We analyze peer influence of production and consumption of projects in the Scratch community, an online platform developed by MIT Media Lab, where users collectively learn to program by creating and sharing projects. Scratchers can follow others’ activities on the platform; in the followers network, we investigate if Scratchers’ production popularity (determined by others) and consumption preference (self determined) are influenced by whom they follow on the platform (peers). Several mechanisms established in the literature like homophily, selection, peer influence, own behavioural tendency, reciprocated ties, and particular contexts can lead to observations of behavioural clustering in a social network like Scratch, and therefore isolating peer influence from other mechanisms is a challenging task. In this study, we measure peer influence in the Scratch community after accounting for such alternative confounding mechanisms. There are two key steps we follow to estimate peer influence of a behaviour. First, at a given time, we create experimental and control groups such that the peers’ behaviour under investigation can be justified as a random assignment. To do so we exactly match Scratchers’ personal and network attributes in both groups such that Scratchers in the experimental group have peers with higher degree of the behaviour under study compared to the control group, and all other attributes of Scratchers are balanced across both groups. Second, conditional on all activities up to this time (as captured by the attributes), we measure peer influence as the difference in Scratchers’ personal behavioural changes in subsequent periods across the two groups.

International trade has been increasingly organized in the form of global value chains (GVCs) where different stages of production are located in different countries. This recent phenomenon has substantial consequences for both trade policy design at the national or regional level and business decision making at the firm level. In this paper, we provide a new method for comparing GVCs across countries and over time. First, we use the World Input-Output Database (WIOD) to construct both the upstream and downstream global value networks, where the nodes are individual sectors in different countries and the links are the value-added contribution relationships. Second, we introduce a network-based measure of node similarity to compare the GVCs between any pair of countries for each sector and each year available in the WIOD. Our network-based similarity is a better measure for node comparison than the existing ones because it takes into account all the direct and indirect relationships between country-sector pairs, is applicable to both directed and weighted networks with self-loops, and takes into account externally defined node attributes. As a result, our measure of similarity reveals the most intensive interactions among the GVCs across countries and over time. From 1995 to 2011, the average similarity between sectors and countries have clear increasing trends, which are temporarily interrupted by the recent economic crisis. This measure of the similarity of GVCs provides quantitative answers to important questions about dependency, sustainability, risk, and competition in the global production system., Comment: 30 pages, 14 figures

This paper investigates the role of real options reasoning in R&D project portfolio management and investment decisions of pharmaceutical firms. We analyse a unique dataset that integrates information on initiation and termination of clinical trials at the level of specific medical indications. Consistent with existing literature, we find a positive relationship between market size and firm entry in clinical trials. We also show that the option value of R&D investments, as proxied by the scope of R&D projects, affects the selection of target markets. Moreover, high-risk research areas attract more entry, in line with the predictions of real options theory. However, we also find that more flexibility in project duration and delayed project discontinuation attract higher rates of entry. Departures from pure real options reasoning are motivated by the presence of incremental learning in pharmaceutical R&D.

When studying patent data as a way to understand innovation and technological change, the conventional indicators might fall short, and categorizing technologies based on the existing classification systems used by patent authorities could cause inaccuracy and misclassification, as shown in literature. Gao et al. (International Workshop on Complex Networks and their Applications, 2017) have established a method to analyze patent classes of similar technologies as network communities. In this paper, we adopt the stabilized Louvain method for network community detection to improve consistency and stability. Incorporating the overlapping community mapping algorithm, we also develop a new method to identify the central nodes based on the temporal evolution of the network structure and track the changes of communities over time. A case study of Germany’s patent data is used to demonstrate and verify the application of the method and the results. Compared to the non-network metrics and conventional network measures, we offer a heuristic approach with a dynamic view and more stable results. Electronic supplementary material The online version of this article (10.1007/s41109-018-0090-3) contains supplementary material, which is available to authorized users.

Classifying patents by the technology areas they pertain is important to enable information search and facilitate policy analysis and socio-economic studies. Based on the OECD Triadic Patent Family database, this study constructs a cohort network based on the grouping of IPC subclasses in the same patent families, and a citation network based on citations between subclasses of patent families citing each other. This paper presents a systematic analysis approach which obtains naturally formed network clusters identified using a Lumped Markov Chain method, extracts community keys traceable over time, and investigates two important community characteristics: consistency and changing trends. The results are verified against several other methods, including a recent research measuring patent text similarity. The proposed method contributes to the literature a network-based approach to study the endogenous community properties of an exogenously devised classification system. The application of this method may improve accuracy and efficiency of the IPC search platform and help detect the emergence of new technologies. © Springer International Publishing AG 2018.

Modern production is increasingly fragmented across countries.To disentangle theworld production system at sector level, we use the World Input-Output Database to construct the World Input-Output Network (WION) where the nodes are the individual sectors in different countries and the edges are the transactions between them. In order to explore the features and dynamics of the WION, in this article we detect the communities in the WION and evaluate their significance using a random walk Markov chain approach. Our results contribute to the recent stream of literature analysing the role of global value chains in economic integration across countries, by showing global value chains as endogenously emerging communities in the world production system, and discussing how different perspectives produce different results in terms of the pattern of integration. © The authors 2017. Published by Oxford University Press. All rights reserved.

When resources are shared between interacting networks, the importance of each node depends strongly on how collaborative or competitive each sub-network is. In this paper, we develop a new method of measuring centrality in the complex network of patent citations that can take political borders into account, where the national benefit of domestic citations relative to foreign citations can be controlled by a free parameter. We find that while some patent classes are of high importance both in the global and the domestic economy, there often exist patent classes in individual countries that are more central nationally than in global economy. We characterize the most important classes globally and domestically for six different nations, and describe their robustness to various perturbations to the model and to noise.

Network theory proved recently to be useful in the quantification of many properties of financial systems. The analysis of the structure of investment portfolios is a major application since their eventual correlation and overlap impact the actual risk by individual investors. We investigate the bipartite network of US mutual fund portfolios and their assets. We follow its evolution during the Global Financial Crisis and study the diversification, as understood in modern portfolio theory, and the similarity of the investments of different funds. We show that, on average, portfolios have become more diversified and less similar during the crisis. However, we also find that large overlap is far more likely than expected from benchmark models of random allocation of investments. This indicates the existence of strong correlations between fund investment strategies. We exploit a deliberately simplified model of shock propagation to identify a systemic risk component stemming from the similarity of portfolios. The network is still partially vulnerable after the crisis because of this effect, despite the increase in the diversification of multi asset portfolios. Diversification and similarity should be taken into account jointly to properly assess systemic risk.

We question the claim that the largest US cities are Pareto distributed. We show that results of multiple tests on real data are similar to those obtained when the true distribution is lognormal, and largely depend on sample sizes.

Experience and socialization are key factors in customer commitment and defection decisions. To study the effect of experience and social relationships on customer retention, we analyze a reality-mined co-presence network of health club members over a period of 4 years. Since central customers in the network have more social ties they will lose if they defect, we use centrality as a proxy for customer relationship switching costs. We find that long-standing customers do have a lower chance of renewing their contracts. However, in line with theoretical predictions (Burnham et al., Journal of the Academy of Marketing Science 31(2):109–126, 2003), the consumer’s centrality in the network (reflecting a social cost of defection) reduces customer churn rate. This study’s results indicate that the inclusion of social effects increases the predictive power of the customer churn model (Nitzan and Libai, Journal of Marketing 75(6):24–38, 2011), thus contributing to our understanding of the role social networks play in customer decisions.

In light of growing water scarcity, virtual water, or the water embedded in key water-intensive commodities, has been an active area of debate among practitioners and academics alike. As of yet, however, there is no consensus on whether water scarcity affects conflict behavior and we still lack empirical research intending to account for the role of virtual water in affecting the odds of militarized disputes between states. Using quantitative methods and data on virtual water trade, we find that bilateral and multilateral trade openness reduce the probability of war between any given pair of countries, which is consistent with the strategic role of this important commodity and the opportunity cost associated with the loss of trade gains. We also find that the substantive effect of virtual water trade is comparable to that of oil and gas, the archetypal natural resources, in determining interstate conflicts’ probability.\ud

Patent data represent a significant source of information on innovation and the evolution of technology through networks of citations, co-invention and co-assignment of new patents. A major obstacle to extracting useful information from this data is the problem of name disambiguation: linking alternate spellings of individuals or institutions to a single identifier to uniquely determine the parties involved in the creation of a technology. In this paper, we describe a new algorithm that uses high-resolution geolocation to disambiguate both inventor and assignees on more than 3.6 million patents found in the European Patent Office (EPO), under the Patent Cooperation treaty (PCT), and in the US Patent and Trademark Office (USPTO). We show that our algorithm has both high precision and recall in comparison to a manual disambiguation of EPO assignee names in Boston and Paris, and show it performs well for a benchmark of USPTO inventor names that can be linked to a high-resolution address (but poorly for inventors that never provided a high quality address). The most significant benefit of this work is the high quality assignee disambiguation with worldwide coverage coupled with an inventor disambiguation that is competitive with other state of the art approaches. To our knowledge this is the broadest and most accurate simultaneous disambiguation and cross-linking of the inventor and assignee names for a significant fraction of patents in these three major patent collections., Comment: 25 pages, 3 figures

Complex economic systems can often be described by a network, with nodes representing economic entities and edges their interdependencies, while network centrality is often a good indicator of importance. Recent publications have implemented a nonlinear iterative Fitness-Complexity (FC) algorithm to measure centrality in a bipartite trade network, which aims to represent the ‘Fitness’ of national economies as well as the ‘Complexity’ of the products being traded. In this paper, we discuss this methodological approach and conclude that further work is needed to identify stable and reliable measures of fitness and complexity. We provide theoretical and numerical evidence for the intrinsic instability in the nonlinear definition of the FC algorithm. We perform an in-depth evaluation of the algorithm’s rankings in two real world networks at the country level: the global trade network, and the patent network in different technological domains. In both networks, we find evidence of the instabilities predicted theoretically, and show that ‘complex’ products or patents tend often to be those that countries rarely produce, rather than those that are intrinsically more difficult to produce. ispartof: Scientific Reports vol:7 issue:1 ispartof: location:England status: published

Considerable efforts have been made in recent years to produce detailed topologies of the Internet. Although Internet topology data have been brought to the attention of a wide and somewhat diverse audience of scholars, so far they have been overlooked by economists. In this paper, we suggest that such data could be effectively treated as a proxy to characterize the size of the "digital economy" at country level and outsourcing: thus, we analyse the topological structure of the network of trade in digital services (trade in bits) and compare it with that of the more traditional flow of manufactured goods across countries. To perform meaningful comparisons across networks with different characteristics, we define a stochastic benchmark for the number of connections among each country-pair, based on hypergeometric distribution. Original data are thus filtered by means of different thresholds, so that we only focus on the strongest links, i.e., statistically significant links. We find that trade in bits displays a sparser and less hierarchical network structure, which is more similar to trade in high-skill manufactured goods than total trade. Lastly, distance plays a more prominent role in shaping the network of international trade in physical goods than trade in digital services., Comment: 25 pages, 6 figures

Innovation is a trial and error process in which both successes and failures contribute to knowledge creation and destruction. In this paper we test theoretical predictions about the role of failures in new product development on private and public knowledge and interfirm knowledge transfer. We analyse the outcomes of world-wide R&D projects in the pharmaceutical industry, and proxy knowledge flows with forward citations received by patents associated with each project. We find that patents covering successfully completed projects (i.e., leading to drug launch on the market) receive more citations than those associated to failed (terminated) projects, which in turn are cited more often than patents lacking clinical or preclinical information. Failures by specialized firms are cited more frequently than the ones of generalist companies. We therefore offer evidence of the value of failures as research inputs in (pharmaceutical) innovation.

Technological and market transitions are difficult to manage, and collaborations can be viewed as either resources or constraints in dynamic settings. In the biopharmaceutical industry, a paradigmatic shift in the relevant knowledge bases occurred in the mid-1990s, inducing a structural change in the network of R&D collaborations. Search and relational strategies oriented toward exploration versus exploitation have prevailed in different phases of the network evolution. Therefore, biotechnology firms have experienced overwhelming difficulties in reorienting their learning strategies throughout paradigmatic shifts and ambidextrous organizations have been able to attain superior performances in terms of stability and centralization in the R&D network.

The size distribution of business firms is explained using number and size of firms' constituent components. It is a lognormal distribution multiplied by a stretching factor which can lead to a Pareto upper tail. This result is confirmed empirically.

In this work we are interested in identifying clusters of "positional equivalent" actors, i.e. actors who play a similar role in a system. In particular, we analyze weighted bipartite networks that describes the relationships between actors on one side and features or traits on the other, together with the intensity level to which actors show their features. The main contribution of our work is twofold. First, we develop a methodological approach that takes into account the underlying multivariate dependence among groups of actors. The idea is that positions in a network could be defined on the basis of the similar intensity levels that the actors exhibit in expressing some features, instead of just considering relationships that actors hold with each others. Second, we propose a new clustering procedure that exploits the potentiality of copula functions, a mathematical instrument for the modelization of the stochastic dependence structure. Our clustering algorithm can be applied both to binary and real-valued matrices. We validate it with simulations and applications to real-world data.

In this paper, we build a cumulative innovation model to understand the role of both success and failure in the learning dynamics that characterize pharmaceutical R&D. We test the prediction of our model by means of a unique dataset that combines patent information with R&D projects, thus distinguishing patents related to successfully marketed products from those covering candidate drugs that failed in clinical trials. Results confirm model predictions showing that patents associated with successfully completed projects receive more citations than those associated with failed projects. However, we also show that failed projects can be in turn cited more often than patents lacking clinical or preclinical information. We further explore the ‘black box’ of innovation, providing evidence that both successes and failures contribute to R&D investment decisions and knowledge dynamics in science-driven sectors.

We introduce a model of proportional growth to explain the distribution $P(g)$ of business firm growth rates. The model predicts that $P(g)$ is Laplace in the central part and depicts an asymptotic power-law behavior in the tails with an exponent $\zeta=3$. Because of data limitations, previous studies in this field have been focusing exclusively on the Laplace shape of the body of the distribution. We test the model at different levels of aggregation in the economy, from products, to firms, to countries, and we find that the its predictions are in good agreement with empirical evidence on both growth distributions and size-variance relationships., Comment: 8 pages, 4 figures

We refer to the framework developed by Ijiri and Simon (1977) and to the notion of independent submarkets (Sutton 1998) to provide a simple candidate explanation for the shape of the firm growth distribution based on a model of proportional growth at the level of both the introduction of new products by firms and their size dynamics. We exploit the features of a unique longitudinal data set which covers the entire distribution of products and firms in the worldwide pharmaceutical industry to test the model at different levels of aggregation as well as at different time lags. Econometric investigations show that the model's predictions are in good agreement with empirical evidence. (JEL: L11, L65)

This article addresses the validity of assumptions about the importance of co-locality for innovation, by analyzing whether or not co-location matters for formal knowledge collaboration in the Swedish biotechnology–pharmaceutical sector, or biotech–pharma sector. The population of Swedish biotech–pharma firms has been defined, based on the three criteria of geographical location, their engagement in active knowledge development, and their specialized knowledge/product focus. The firms’ patterns of regional, national and international collaboration with other firms and with universities is analyzed, as well as the differing collaborative patterns of small versus large firm. In addressing the theoretical questions about the relative importance of co-location for innovation, the article also provides an empirical overview of the Swedish biotech–pharma sector, especially trends over time. This paper thus contributes to the literature by expanding our empirical knowledge about one European biotech–pharma sectoral system, e.g. Sweden, as well as addressing the theoretical question about the relative importance of co-location for formal knowledge collaboration.

The fragmentation of production across countries has become an important feature of the globalization in recent decades and is often conceptualized by the term, global value chains (GVCs). When empirically investigating the GVCs, previous studies are mainly interested in knowing how global the GVCs are rather than how the GVCs look like. From a complex networks perspective, we use the World Input-Output Database (WIOD) to study the global production system. We find that the industry-level GVCs are indeed not chain-like but are better characterized by the tree topology. Hence, we compute the global value trees (GVTs) for all the industries available in the WIOD. Moreover, we compute an industry importance measure based on the GVTs and compare it with other network centrality measures. Finally, we discuss some future applications of the GVTs., Comment: 26 pages, 6 figures. arXiv admin note: text overlap with arXiv:1407.0225

In this paper we examine the relationship between organizational status and nonconformity in complex institutional fields characterized by the enduring tension between divergent institutional logics. First, we hypothesize the existence of an inverted U-shaped (IUS) relationship between status and nonconformity. Second, we argue that legitimacy and celebrity enhancing signals, that complement the external perception of a firm’s market identity, moderate the relationship between status and nonconformity, by springing up or inhibiting opportunities for firms to protect vs. raise their status position through high status patterns of affiliations. Specifically, we argue that while celebrity enhancing efforts reinforce middle status nonconformist behaviors by emphasizing the exceptional and positive valence of their nonconformist undertakings, legitimacy enhancing signals constraint middle status deviant behaviors and reverse the IUS curve. Unveiling the taken-for-granted traits of their identities hampers middle status ambition to raise the social hierarchy through nonconformity but fosters low and high status freedom of deviance through a reinforced security in their social position. We found strong support for our arguments in statistical analysis of 42 Italian opera houses repertoires from 2004 to 2011. We synthesize our findings by offering an integrated framework on how the interplay between status, legitimacy and celebrity enhancing signals affects organizations’ ability to depart from established institutional frameworks under conditions of institutional complexity.

The recent financial crisis highlighted the relevant role of the systemic effects of banks’ defaults on the stability of the whole financial system. In this work we draw an organic picture of the current regulations, moving from the definitions of systemic risk to the issues concerning data availability. We show how a more detailed flow of data on traded deals might shed light on some systemic risk features taken into account only partially in the past. In particular, we analyse how the new regulatory framework allows regulators to describe OTC derivatives markets according to more detailed partitions, thus depicting a more realistic picture of the system. Finally, we suggest to study sub-markets illiquidity conditions to consider possible spill over effects which might lead to a worsening for the entire system

We draw on diverse data sets to compare the institutional organization of upstream life science research across the United States and Europe. Understanding cross-national differences in the organization of innovative labor in the life sciences requires attention to the structure and evolution of biomedical networks involving public research organizations (universities, government laboratories, nonprofit research institutes, and research hospitals), science-based biotechnology firms, and multinational pharmaceutical corporations. We use network visualization methods and correspondence analyses to demonstrate that innovative research in biomedicine has its origins in regional clusters in the United States and in European nations. But the scientific and organizational composition of these regions varies in consequential ways. In the United States, public research organizations and small firms conduct R&D across multiple therapeutic areas and stages of the development process. Ties within and across these regions link small firms and diverse public institutions, contributing to the development of a robust national network. In contrast, the European story is one of regional specialization with a less diverse group of public research organizations working in a smaller number of therapeutic areas. European institutes develop local connections to small firms working on similar scientific problems, while cross-national linkages of European regional clusters typically involve large pharmaceutical corporations. We show that the roles of large and small firms differ in the United States and Europe, arguing that the greater heterogeneity of the U.S. system is based on much closer integration of basic science and clinical development.

This paper shows how specific technological and relational regimes have shaped the growth of the network of R&D collaborative agreements in pharmaceuticals in the 1990s. Our analysis reveals the existence of a complex set of regimes of firm growth within the network, providing additional evidence supporting prediction that both growth and innovative activities of large and small firms respond, even within a given industry, to considerably different technological and economic factors. Moreover, the paper shows, in the context of a specific industry and by means of a series of preliminary and explorative empirical analyses, that information on the topological properties of a given industrial settings and on roles/positions of organizations within it can be used to disentangle some fundamental generative processes underlying observed processes of growth. This result contributes to the "old" stochastic approach to firm growth, in the direction of building parsimonious and, at the same time, more realistic, representations of processes of industrial growth.

Reputation is an important social construct in science, which enables informed quality assessments of both publications and careers of scientists in the absence of complete systemic information. However, the relation between reputation and career growth of an individual remains poorly understood, despite recent proliferation of quantitative research evaluation methods. Here we develop an original framework for measuring how a publication's citation rate $\Delta c$ depends on the reputation of its central author $i$, in addition to its net citation count $c$. To estimate the strength of the reputation effect, we perform a longitudinal analysis on the careers of 450 highly-cited scientists, using the total citations $C_{i}$ of each scientist as his/her reputation measure. We find a citation crossover $c_{\times}$ which distinguishes the strength of the reputation effect. For publications with $c < c_{\times}$, the author's reputation is found to dominate the annual citation rate. Hence, a new publication may gain a significant early advantage corresponding to roughly a 66% increase in the citation rate for each tenfold increase in $C_{i}$. However, the reputation effect becomes negligible for highly cited publications meaning that for $c\geq c_{\times}$ the citation rate measures scientific impact more transparently. In addition we have developed a stochastic reputation model, which is found to reproduce numerous statistical observations for real careers, thus providing insight into the microscopic mechanisms underlying cumulative advantage in science., Comment: Final published version of the main manuscript including additional analysis: 9 pages, 4 figures, 1 table, and full reference list, including those in the Supplementary Information. For the SI Appendix, see http://physics.bu.edu/~amp17/webpage_files/MyPapers/Reputation_SI.pdf

After the systemic effects of bank defaults during the recent financial crisis, and despite a huge amount of literature over the last years to detect systemic risk, no standard methodologies have been set up until now. We aim to build a concise but comprehensive picture of the state of the art, illustrating the open issues, and outlining pathways for future research. In particular, we propose the analysis of some examples of local systems that attract the attention of the financial sector. This work is directed to both academic researchers and practitioners.

In this paper we develop a model for the evolution of multiple networks which is able to replicate the concentrated and sparse nature of world trade data. Our model is an extension of the preferential attachment growth model to the case of multiple networks. Countries trade a variety of goods of different complexity. Every country progressively evolves from trading less sophisticated to high-tech goods. The probabilities of capturing more trade opportunities at a given level of complexity and of starting to trade more complex goods are both proportional to the number of existing trade links. We provide a set of theoretical predictions and simulative results. A calibration exercise shows that our model replicates the same concentration level of world trade as well as the sparsity pattern of the trade matrix. We also discuss a set of numerical solutions to deal with large multiple networks. ispartof: Physical Review E vol:90 issue:2 ispartof: location:United States status: published

The consideration of both the direct and the indirect effects of global production and trade is the first step in order to assess the sustainability of resource exploitation, in particular water usage. This paper applies the Global Multi-Regional Input-Output model to quantify the interdependencies of different sectors and to determine the overall water consumption of each country. This procedure allows the measurement of Virtual Water Trade, that is the volume of water embedded in traded goods. This paper introduces further extensions based on network analysis to overcome the limitations of I-O models. To the best of our knowledge, this is the first attempt to build a bridge between two different, but related, methodologies. Firstly, we assess the evolution of the structure of international trade in Virtual Water (VW). Secondly, we present the results from the Structural Decomposition Analysis. Finally, we introduce other measures from Network Theory, in order to integrate the previous results. Community Detection assessment reveals the emergence of regional VW systems composed by a limited set of countries. Thus our study confirms the need of elaborating and implementing transboundary policies for water management, especially in the European Union.

This paper studies the relationship between migration and trade, with the aim of measuring both direct and indirect network effects. We analyze trade of diferentiated and homogeneous goods using an econometric approach inspired by spatial econometrics, proposing a new way to define country neighbors based on the most intense links in the migration network. We find that migration significantly affects trade across categories both in direct and in indirect way. The indirect impact highlights a stronger competitive effect of third country migrants for homogeneous goods. We also confirm that the effect of migration channels is higher on differentiated goods.

In this paper, we investigate how underlying relevant technological conditions induce distinguishable patterns of change in industry structure and evolution. A mapping is detected between the specific nature of problem decompositions and research techniques at the micro level of knowledge bases, and patterns of structural evolution at the macro level of the industry network. The graph-theoretic techniques we introduce map major technological discontinuities on changes observed at the level of dominant organization forms. They might have applications in other domains, whenever the identification of structural breaks and homological relationships between technological and industrial spaces are important issues.

Theory of complex networks proved successful in the description of a variety of static networks ranging from biology to computer and social sciences and to economics and finance. Here we use network models to describe the evolution of a particular economic system, namely the International Trade Network (ITN). Previous studies often assume that globalization and regionalization in international trade are contradictory to each other. We re-examine the relationship between globalization and regionalization by viewing the international trade system as an interdependent complex network. We use the modularity optimization method to detect communities and community cores in the ITN during the years 1995-2011. We find rich dynamics over time both inter- and intra-communities. Most importantly, we have a multilevel description of the evolution where the global dynamics (i.e., communities disappear or reemerge) tend to be correlated with the regional dynamics (i.e., community core changes between community members). In particular, the Asia-Oceania community disappeared and reemerged over time along with a switch in leadership from Japan to China. Moreover, simulation results show that the global dynamics can be generated by a preferential attachment mechanism both inter- and intra-communities., 8 pages, 4 figures

In the field of risk management, scholars began to bring together the quantitative methodologies with the banking management issues about 30 years ago, with a special focus on market, credit and operational risks. After the systemic eff ects of banks defaults during the recent fi nancial crisis, and despite a huge amount of literature in the last years concerning the systemic risk, no standard methodologies have been set up to now. Even the new Basel 3 regulation has adopted a heuristic indicator-based approach, quite far from an e ffective quantitative tool. In this paper, we refer to the di fferent pieces of the puzzle: defi nition of systemic risk, a set of coherent and useful measures, the computability of these measures, the data set structure. In this challenging fi eld, we aim to build a comprehensive picture of the state of the art, to illustrate the open issues, and to outline some paths for a more successful future research. This work appropriately integrates other useful surveys and it is directed to both academic researchers and practitioners.

The paper moves a step forward in the direction of establishing a connection between the structure and evolution of knowledge bases and the structure and evolution of organizational forms in innovative activities in a science-intensive industry. The paper has an explicit focus on the dynamics of the network of collaborative agreements in R&D in the pharma/biotech industry after the “molecular biology revolution”. Using a comprehensive dataset, built by the authors integrating several sources in the industry, the dynamics of the network over time is extensively analyzed. With regards to network structure, it is found that, while the size of the network increases over time due to net flows of entry, its topological properties remain relatively unchanged. The evolution of the network has occurred without relevant deformations in the core-periphery profile. With regards to age-dependent propensity to collaborate, the paper finds that the extent of inter-generational collaboration is much more significant than intra-generational collaboration. In addition, the propensity of firms of a given generation to enter into collaboration with firms of a different generation increases with the distance between the two, while the total number of intra-generational collaborations decreases over time and, moreover, tends to decrease for most recent generations. In the paper a unitary and coherent explanation of the evidence is developed, coming to reveal the existence of a striking isomorphism between structural properties of the dynamics of knowledge and of the evolution of network structure.

Recent studies on the geography of knowledge networks have documented a negative impact of physical distance and institutional borders upon research and development (R&D) collaborations. Though it is widely recognized that geographic constraints hamper the diffusion of knowledge, less attention has been devoted to the temporal evolution of these constraints. In this study we use data on patents filed with the European Patent Office (EPO) for 50 countries to analyze the impact of physical distance and country borders on inter-regional links in four different networks over the period 1988-2009: (1) co-inventorship, (2) patent citations, (3) inventor mobility and (4) the location of R&D laboratories. We find the constraint imposed by country borders and distance decreased until mid-1990s then started to grow, particularly for distance. The intensity of European cross-country inventor collaborations increased at a higher pace than their non-European counterparts until 2004, with no significant relative progress afterwards. Moreover, when analyzing networks of geographical mobility, multinational R&D activities and patent citations we do not depict any substantial progress in European research integration aside from the influence of common global trends.

An explosion of knowledge and a growing array of tools and technologies have transformed modern drug R&D, while its cost has risen by a sizable amount. At the same time, the unchecked increase in health care and prescription drug spending has spawned cost containment policies that are restricting the demand for drugs in all major markets. This Perspective explores the interplay between technological advances and regulatory policies and their likely impact on the dynamics of the pharmaceutical industry. Advances in the life sciences have profoundly transformed the drug research and development (R&D) process. That transformation has come at a price, boosting the cost of developing a new molecular entity (NME) to $802 million by 2000. More expensive R&D, combined with an aging population and better diagnostic techniques, has swelled drug spending in the United States, which reached $141 billion in 2001. These increases have in turn induced a spate of cost containment measures that are affecting demand for pharmaceuticals in all major markets. This Perspective considers the impact of the interplay between technological advances and health care policy on the future dynamics of the pharmaceutical industry.

The Statistical Physics of Complex Networks has recently provided new theoretical tools for policy makers. Here we extend the notion of network controllability to detect the financial institutions, i.e. the drivers, that are most crucial to the functioning of an interbank market. The system we investigate is a paradigmatic case study for complex networks since it undergoes dramatic structural changes over time and links among nodes can be observed at several time scales. We find a scale-free decay of the fraction of drivers with increasing time resolution, implying that policies have to be adjusted to the time scales in order to be effective. Moreover, drivers are often not the most highly connected “hub” institutions, nor the largest lenders, contrary to the results of other studies. Our findings contribute quantitative indicators which can support regulators in developing more effective supervision and intervention policies., Scientific Reports, 3, ISSN:2045-2322

This paper develops a simple network model to describe the dynamic of the intensive and extensive margin of international trade flows. The result is achieved by means of the combination of two mechanisms of proportional growth: the first (discrete) determines the formation of trade links, the second (continuous) governs trade intensity. We show that our setup is able to simultaneously match a large number of empirical regularities, such as the fraction of zero trade flows across pairs of countries or the high concentration of trade with respect to both products and destinations. Our findings suggest that stylized facts are strongly interconnected across different levels of aggregation of trade data , so that a unifying explanation is called for. By incorporating stochastic elements into standard trade models we can improve their ability to explain relevant facts about world trade.