Reconstruction of production networks and other studies in complexity economics: Reconstruction of production networks

Recent research portrays the economy as a complex, adaptive system composed of heterogeneous agents interacting with one another and their environment. This thesis contributes to this research effort, focusing on production networks and financial markets. International supply chains are a remarkable example of complex networks, and their investigation is crucial to understanding our economies. The first part of this thesis is devoted to the reconstruction and modelling of these networks. In Chapter 1, we briefly survey the literature on network reconstruction and how it has been applied to the case of production networks. In Chapter 2, we use Machine Learning to reconstruct the production network -i.e., to infer which firms are linked by commercial relationships. Using some sensible economic and financial properties as inputs, we show that our algorithm outperforms some well-known benchmarks, investigate which features are important for accurate predictions, and study to what extent our approach can be used in real-world tasks. In Chapter 3, we focus on firms' sales time series and show that the production network has a visible impact on their correlation structure. We build on this finding and develop a method to reconstruct production networks from firms' dynamics. Chapter 4 outlines an agent-based model designed to study the impact of exogenous shocks on the real production network. The primary agents of our model are firms connected by trade and credit relationships. We explain the model, provide some analytical results and simulations, and describe a simple approach to generate weighted production networks that match some aggregate (and observable) properties of global trade. In the second part of the thesis, we pivot our focus away from production networks and onto financial markets. Chapter 5, provides empirical evidence for the Market Ecology hypothesis. This hypothesis models financial markets as ecologies where trading strategies compete to generate returns. Using a large dataset of U.S. stock prices, funds' portfolios, and funds' trading strategies, we find a correlation between wealth allocation across such strategies and market volatility, as recently proposed in simple stylized models. In Chapter 6, we analyze the cryptocurrency market through the framework of its investors' network. Our findings reveal that the structure of this network closely reflects the correlation patterns within the cryptocurrency market

Cryptocurrency co-investment network: token returns reflect investment patterns

Since the introduction of Bitcoin in 2009, the dramatic and unsteady evolution of the cryptocurrency market has also been driven by large investments by traditional and cryptocurrency-focused hedge funds. Notwithstanding their critical role, our understanding of the relationship between institutional investments and the evolution of the cryptocurrency market has remained limited, also due to the lack of comprehensive data describing investments over time. In this study, we present a quantitative study of cryptocurrency institutional investments based on a dataset collected for 1324 currencies in the period between 2014 and 2022 from Crunchbase, one of the largest platforms gathering business information. We show that the evolution of the cryptocurrency market capitalization is highly correlated with the size of institutional investments, thus confirming their important role. Further, we find that the market is dominated by the presence of a group of prominent investors who tend to specialise by focusing on particular technologies. Finally, studying the co-investment network of currencies that share common investors, we show that assets with shared investors tend to be characterized by similar market behaviour. Our work sheds light on the role played by institutional investors and provides a basis for further research on their influence in the cryptocurrency ecosystem

Best-Response Dynamics, Playing Sequences, and Convergence to Equilibrium in Random Games

We analyze the performance of the best-response dynamic across all normal-form games using a random games approach. The playing sequence -- the order in which players update their actions -- is essentially irrelevant in determining whether the dynamic converges to a Nash equilibrium in certain classes of games (e.g. in potential games) but, when evaluated across all possible games, convergence to equilibrium depends on the playing sequence in an extreme way. Our main asymptotic result shows that the best-response dynamic converges to a pure Nash equilibrium in a vanishingly small fraction of all (large) games when players take turns according to a fixed cyclic order. By contrast, when the playing sequence is random, the dynamic converges to a pure Nash equilibrium if one exists in almost all (large) games.Comment: JEL codes: C62, C72, C73, D83 Keywords: Best-response dynamics, equilibrium convergence, random games, learning models in game

Downregulation of miR-223 Expression Is an Early Event during Mammary Transformation and Confers Resistance to CDK4/6 Inhibitors in Luminal Breast Cancer

miR-223 is an anti-inflammatory miRNA that in cancer acts either as an oncosuppressor or oncopromoter, in a context-dependent manner. In breast cancer, we demonstrated that it dampens the activation of the EGF pathway. However, little is known on the role of miR-223 during breast cancer onset and progression. miR-223 expression was decreased in breast cancer of luminal and HER2 subtypes and inversely correlated with patients' prognosis. In normal luminal mammary epithelial cells, miR-223 acted cell autonomously in the control of their growth and morphology in three-dimensional context. In the MMTV-Δ16HER2 transgenic mouse model, oncogene transformation resulted in a timely abrogation of miR-223 expression, likely due to activation of E2F1, a known repressor of miR-223 transcription. Accordingly, treatment with CDK4/6 inhibitors, which eventually results in restraining E2F1 activity, restored miR-223 expression and miR-223 ablation induced luminal breast cancer resistance to CDK4/6 inhibition, both in vitro and in vivo. Notably, miR-223 expression was lost in microdissected ductal carcinoma in situ (DCIS) from patients with luminal and HER2-positive breast cancer. Altogether, these results identify downmodulation of miR-223 as an early step in luminal breast cancer onset and suggest that it could be used to identify aggressive DCIS and predict the response to targeted therapy. SIGNIFICANCE: miR-223 may represent a predictive biomarker of response to CDK4/6 inhibitors and its loss could identify DCIS lesions that are likely to progress into invasive breast cancer

Loss of CDKN1B induces an age‐related clonal hematopoietic disorder via Notch2 activity dysregulation

No abstract availabl

Muon reconstruction and identification efficiency in ATLAS using the full Run 2 pp collision data set at \sqrt{s}=13 TeV

This article documents the muon reconstruction and identification efficiency obtained by the ATLAS experiment for 139 \hbox {fb}^{-1} of pp collision data at \sqrt{s}=13 TeV collected between 2015 and 2018 during Run 2 of the LHC. The increased instantaneous luminosity delivered by the LHC over this period required a reoptimisation of the criteria for the identification of prompt muons. Improved and newly developed algorithms were deployed to preserve high muon identification efficiency with a low misidentification rate and good momentum resolution. The availability of large samples of Z\rightarrow \mu \mu and J/\psi \rightarrow \mu \mu decays, and the minimisation of systematic uncertainties, allows the efficiencies of criteria for muon identification, primary vertex association, and isolation to be measured with an accuracy at the per-mille level in the bulk of the phase space, and up to the percent level in complex kinematic configurations. Excellent performance is achieved over a range of transverse momenta from 3 GeV to several hundred GeV, and across the full muon detector acceptance of |\eta |<2.7

Measurement of the tt¯tt¯ production cross section in pp collisions at √s=13 TeV with the ATLAS detector

A measurement of four-top-quark production using proton-proton collision data at a centre-of-mass energy of 13 TeV collected by the ATLAS detector at the Large Hadron Collider corresponding to an integrated luminosity of 139 fb−1 is presented. Events are selected if they contain a single lepton (electron or muon) or an opposite-sign lepton pair, in association with multiple jets. The events are categorised according to the number of jets and how likely these are to contain b-hadrons. A multivariate technique is then used to discriminate between signal and background events. The measured four-top-quark production cross section is found to be 26+17−15 fb, with a corresponding observed (expected) significance of 1.9 (1.0) standard deviations over the background-only hypothesis. The result is combined with the previous measurement performed by the ATLAS Collaboration in the multilepton final state. The combined four-top-quark production cross section is measured to be 24+7−6 fb, with a corresponding observed (expected) signal significance of 4.7 (2.6) standard deviations over the background-only predictions. It is consistent within 2.0 standard deviations with the Standard Model expectation of 12.0 ± 2.4 fb

Measurements of Higgs bosons decaying to bottom quarks from vector boson fusion production with the ATLAS experiment at √=13TeV

The paper presents a measurement of the Standard Model Higgs Boson decaying to b-quark pairs in the vector boson fusion (VBF) production mode. A sample corresponding to 126 fb−1 of s√=13TeV proton–proton collision data, collected with the ATLAS experiment at the Large Hadron Collider, is analyzed utilizing an adversarial neural network for event classification. The signal strength, defined as the ratio of the measured signal yield to that predicted by the Standard Model for VBF Higgs production, is measured to be 0.95+0.38−0.36 , corresponding to an observed (expected) significance of 2.6 (2.8) standard deviations from the background only hypothesis. The results are additionally combined with an analysis of Higgs bosons decaying to b-quarks, produced via VBF in association with a photon

Search for neutral long-lived particles in pp collisions at √s = 13 TeV that decay into displaced hadronic jets in the ATLAS calorimeter

A search for decays of pair-produced neutral long-lived particles (LLPs) is presented using 139 fb−1 of proton-proton collision data collected by the ATLAS detector at the LHC in 2015–2018 at a centre-of-mass energy of 13 TeV. Dedicated techniques were developed for the reconstruction of displaced jets produced by LLPs decaying hadronically in the ATLAS hadronic calorimeter. Two search regions are defined for different LLP kinematic regimes. The observed numbers of events are consistent with the expected background, and limits for several benchmark signals are determined. For a SM Higgs boson with a mass of 125 GeV, branching ratios above 10% are excluded at 95% confidence level for values of c times LLP mean proper lifetime in the range between 20 mm and 10 m depending on the model. Upper limits are also set on the cross-section times branching ratio for scalars with a mass of 60 GeV and for masses between 200 GeV and 1 TeV. [Figure not available: see fulltext.

Measurement and interpretation of same-sign W boson pair production in association with two jets in pp collisions at s = 13 TeV with the ATLAS detector

This paper presents the measurement of fducial and diferential cross sections for both the inclusive and electroweak production of a same-sign W-boson pair in association with two jets (W±W±jj) using 139 fb−1 of proton-proton collision data recorded at a centre-of-mass energy of √s = 13 TeV by the ATLAS detector at the Large Hadron Collider. The analysis is performed by selecting two same-charge leptons, electron or muon, and at least two jets with large invariant mass and a large rapidity diference. The measured fducial cross sections for electroweak and inclusive W±W±jj production are 2.92 ± 0.22 (stat.) ± 0.19 (syst.)fb and 3.38±0.22 (stat.)±0.19 (syst.)fb, respectively, in agreement with Standard Model predictions. The measurements are used to constrain anomalous quartic gauge couplings by extracting 95% confdence level intervals on dimension-8 operators. A search for doubly charged Higgs bosons H±± that are produced in vector-boson fusion processes and decay into a same-sign W boson pair is performed. The largest deviation from the Standard Model occurs for an H±± mass near 450 GeV, with a global signifcance of 2.5 standard deviations