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The Similarity of Global Value Chains: a Network-Based Measure
No full textInternational trade has been increasingly organized in the form of global value chains
(GVCs) where different stages of production are located in dfferent countries. This
recent phenomenon has substantial consequences for both trade policy design at the
national or regional level and business decision making at the form 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 dfferent
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 networkbased
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
The interaction ‘Supply’, ‘Demand’, and ‘Technological Capabilities’ in terms of Medical Subject Headings: A triple helix model of medical innovations
No full textWe develop a model of innovation that enables us to trace the interplay among three key dimensions of the innovation process: (i) demand of and (ii) supply for innovation, and (iii) technological capabilities available to generate innovation in the forms of products, processes, and services. Building on Triple Helix research, we use entropy statistics to elaborate an indicator of mutual information among these dimensions that can provide indication of reduction of uncertainty. To do so, we focus on the medical context, where uncertainty poses significant challenges to the governance of innovation. The Medical Subject Headings (MeSH) of MEDLINE/PubMed provide us with publication records classified within the categories “Diseases” (C), “Drugs and Chemicals” (D), “Analytic, Diagnostic, and Therapeutic Techniques and Equipment” (E) as knowledge representations of demand, supply, and technological capabilities, respectively. Three case-studies of medical research areas are used as representative ‘entry perspectives’ of the medical innovation process. These are: (i) Human Papilloma Virus, (ii) RNA interference, and (iii) Magnetic Resonance Imaging. We find statistically significant periods of synergy among demand, supply, and technological capabilities (C-D-E) that points to three-dimensional interactions as a fundamental perspective for the understanding and governance of the uncertainty associated with medical innovation. Among the pairwise configurations in these contexts, the demand-technological capabilities (C-E) provided the strongest link, followed by the supply-demand (D-C) and the supply-technological capabilities (D-E) channels
An anisotropic large displacement cohesive zone model for fibrillar and crazing interfaces
No full textA new cohesive zone model to describe fracture of interfaces with a microstructurs made of fibrils with statistically distributed in-plane and out-of-plane orientations is proposed. The elementary force–displacement relation of each fibril is considered to obey the peeling theory of a tape, although other refined constitutive relations could be invoked for the adhesive constitutive response without any lack of generality. The proposed consistent 2D and 3D interface finite element formulations for large displacements account for both the mechanical and the geometrical tangent stiffness matrices, required for implicit solution schemes. After a preliminary discussion on model parameters identification, it is shown that by tailoring the spatial density of fibrils at different orientations can be a way to realize innovative interfaces enhancing adhesion or decohesion, depending on the need. For instance, it can be possible to realize microstructured adhesives to facilitate debonding of the glass cover in photovoltaic modules to simplify recycling purposes. Moreover, the use of probability distribution functions describing the density of fibrils at different orientations is a very effective approach for modeling the anisotropy in the mechanical bonding between paper tissues and for simulating the complex process of crazing in amorphous polymers
Evolution of the free volume between rough surfaces in contact
No full textThe free volume comprised between rough surfaces in contact governs the fluid/gas transport properties across networks of cracks and the leakage/percolation phenomena in seals. In this study, a fundamental insight into the evolution of the free volume depending on the mean plane separation, on the real contact area and on the applied pressure is gained in reference to fractal surfaces whose contact response is solved using the boundary element method. Particular attention is paid to the effect of the surface fractal dimension and of the surface resolution on the predicted results. The free volume domains corresponding to different threshold levels are found to display fractal spatial distributions whose bounds to their fractal dimensions are theoretically derived. A synthetic formula based on the probability distribution function of the free volumes is proposed to synthetically interpret the numerically observed trends
Assessing financial distress dependencies in OTC markets: a new approach by Trade Repositories data
No full textAfter the recent financial crisis, it is undoubtedly recognized the importance of assessing
not only the risk of distress for a single \financial entity", but also the distress dependencies
between the different \entities", where by \entities" we mean in a broad sense any relevant
cluster of products, risk factors, counterparties. In this paper, we focus on the Interest Rate
Swap (IRS) segment as a significant fraction of the OTC market. We define a distress indicator
by combining some distress drivers, such as averaged volumes, liquidity, volatility and
bid-ask proxies. Hence, we analyse the distress dependencies among sub-markets identified
by the segmentation of the IRS market according to contractual and financial features. We
try to combine in an innovative way some new ingredients, namely the more granular data
on OTC derivatives available from the trade repositories along with the classical JPoD approach
introduced in the recent years by the IMF for studying the distress interdependence
structure among financial institutions. The proposed technique seems to be quite promising.
Indeed, the results are quite close to the practical intuition. At the best of our knowledge,
this work is the first empirical study based on trade repositories' data for assessing systemic
risk
Sparse optimization for automated energy end use disaggregation
No full textRetrieving the household electricity consumption at individual appliance level is an essential requirement to assess the contribution of different end uses to the total household consumption, and thus to design energy saving policies and user-tailored feedback for reducing household electricity usage. This has led to the development of nonintrusive appliance load monitoring (NIALM), or energy disaggregation, algorithms, which aim to decompose the aggregate energy consumption data collected from a single measurement point into device-level consumption estimations. Existing NIALM algorithms are able to provide accurate estimate of the fraction of energy consumed by each appliance. Yet, in the authors' experience, they provide poor performance in reconstructing the power consumption trajectories overtime. In this brief, a new NIALM algorithm is presented, which, besides providing very accurate estimates of the aggregated consumption by appliance, also accurately characterizes the appliance power consumption profiles overtime. The proposed algorithm is based on the assumption that the unknown appliance power consumption profiles are piecewise constant overtime (as it is typical for power use patterns of household appliances) and it exploits the information on the time-of-day probability in which a specific appliance might be used. The disaggregation problem is formulated as a least-square error minimization problem, with an additional (convex) penalty term aiming at enforcing the disaggregate signals to be piecewise constant overtime. Testing on household electricity data available in the literature is reported
A GDP-driven model for the binary and weighted structure of the International Trade Network
No full textRecent events such as the global financial crisis have renewed the interest in the topic of economic networks. One of the main channels of shock propagation among countries is the International Trade Network (ITN). Two important models for the ITN structure, the classical gravity model of trade (more popular among economists) and the fitness model (more popular among networks scientists), are both limited to the characterization of only one representation of the ITN. The gravity model satisfactorily predicts the volume of trade between connected countries, but cannot reproduce the missing links (i.e. the topology). On the other hand, the fitness model can successfully replicate the topology of the ITN, but cannot predict the volumes. This paper tries to make an important step forward in the unification of those two frameworks, by proposing a new gross domestic product (GDP) driven model which can simultaneously reproduce the binary and the weighted properties of the ITN. Specifically, we adopt a maximum-entropy approach where both the degree and the strength of each node are preserved. We then identify strong nonlinear relationships between the GDP and the parameters of the model. This ultimately results in a weighted generalization of the fitness model of trade, where the GDP plays the role of a ‘macroeconomic fitness’ shaping the binary and the weighted structure of the ITN simultaneously. Our model mathematically explains an important asymmetry in the role of binary and weighted network properties, namely the fact that binary properties can be inferred without the knowledge of weighted ones, while the opposite is not true
Randomizing bipartite networks: the case of the World Trade Web
No full textWithin the last fifteen years, network theory has been successfully applied both to natural sciences and to socioeconomic disciplines. In particular, bipartite networks have been recognized to provide a particularly insightful representation of many systems, ranging from mutualistic networks in ecology to trade networks in economy, whence the need of a pattern detection-oriented analysis in order to identify statistically-significant structural properties. Such an analysis rests upon the definition of suitable null models, i.e. upon the choice of the portion of network structure to be preserved while randomizing everything else. However, quite surprisingly, little work has been done so far to define null models for real bipartite networks. The aim of the present work is to fill this gap, extending a recently-proposed method to randomize monopartite networks to bipartite networks. While the proposed formalism is perfectly general, we apply our method to the binary, undirected, bipartite representation of the World Trade Web, comparing the observed values of a number of structural quantities of interest with the expected ones, calculated via our randomization procedure. Interestingly, the behavior of the World Trade Web in this new representation is strongly different from the monopartite analogue, showing highly non-trivial patterns of self-organization
How Evolutionary Dynamics Affects Network Reciprocity in Prisoner's Dilemma
No full textCooperation lies at the foundations of human societies, yet why people cooperate remains a conundrum. The issue, known as network reciprocity, of whether population structure can foster cooperative behavior in social dilemmas has been addressed by many, but theoretical studies have yielded contradictory results so far-as the problem is very sensitive to how players adapt their strategy. However, recent experiments with the prisoner's dilemma game played on different networks and in a specific range of payoffs suggest that humans, at least for those experimental setups, do not consider neighbors' payoffs when making their decisions, and that the network structure does not influence the final outcome. In this work we carry out an extensive analysis of different evolutionary dynamics, taking into account most of the alternatives that have been proposed so far to implement players' strategy updating process. In this manner we show that the absence of network reciprocity is a general feature of the dynamics (among those we consider) that do not take neighbors' payoffs into account. Our results, together with experimental evidence, hint at how to properly model real people's behavior
Evolutionary network games: equilibria from imitation and best-response dynamics
No full textWe consider games of strategic substitutes and strategic complements on networks. We introduce two different evolutionary dynamics in order to refine their multiplicity of equilibria, and we analyse the system through a mean field approach. We find that for the best-shot game, taken as a model for substitutes, a replicator-like dynamics does not lead to Nash equilibria, whereas it leads to unique equilibria (full cooperation or full defection, depending on the initial condition and the game parameter) for complements, represented by a coordination game. On the other hand, when the dynamics becomes more cognitively demanding in the form of a best response evolution, predictions are always Nash equilibria (at least when individuals are fully rational): For the best-shot game we find equilibria with a definite value of the fraction of contributors, whereas for the coordination game symmetric equilibria arise only for low or high initial fractions of cooperators. We also extend our study by considering complex heterogeneous topologies, and show that the nature of the selected equilibria does not change for the best-shot game. However for coordination games we reveal an important difference, namely that on infinitely large scale-free networks cooperation arises for any value of the incentive to cooperate