Network Economics 2025-07-21

This paper develops a mathematical framework to study signal networks, in which nodes can be active or inactive, and their activation or deactivation is driven by external signals and the states of the nodes to which they are connected via links. The focus is on determining the optimal number of key nodes (= highly connected and structurally important nodes) required to represent the global activation state of the network accurately. Motivated by neuroscience, medical science, and social science examples, we describe the node dynamics as a continuous-time inhomogeneous Markov process. Under mean-field and homogeneity assumptions, appropriate for large scale-free and disassortative signal networks, we derive differential equations characterising the global activation behaviour and compute the expected hitting time to network triggering. Analytical and numerical results show that two or three key nodes are typically sufficient to approximate the overall network state well, balancing sensitivity and robustness. Our findings provide insight into how natural systems can efficiently aggregate information by exploiting minimal structural components. Bernd Heidergott Frank den Hollander Ines Lindner Azadeh Parvaneh 2025-05-30 "Using deal-level micro data from the Dealroom database, we construct a dynamic co-investment syndication network to examine the influence of cultural proximity and geospatial proximity between investors and start-ups, as well as the network position of global VC firms on investment decisions in European-based start-ups. By applying a linear probability regression model with high-dimensional fixed effects over the period 2015-2022, we confirm that both cultural and spatial proximity significantly facilitate VC investment. Moreover, our analysis reveals that a prominent network position â characterized by how well-connected (degree centrality) and how influential (Katz centrality) within the co-investment networkâ substantially enhances VC investments on account of the facilitated sharing of information, contacts, and resources among investors. Furthermore, our findings reveal that small-world networks, characterized by high clustering coefficients, facilitate investments in distant start-ups, helping to overcome spatial constraintsâan aspect largely overlooked in the literature. Small-world syndication networks foster trust among members, complementing each other through differentiation and specialization in industrial knowledge and local markets, potentially altering risk-averse behaviour and enabling investments that transcend geographical boundaries." Zhen Ni Testa Giuseppina Compano Ramon 2025-06 We study how uncertainty propagates through production networks. First, we construct a highly disaggregated, forward-looking measure of industry-level uncertainty using option-implied volatility data for U.S. firms. Second, we identify the effects of higher uncertainty within industries, across the supply chain, and at the aggregate level. We find that heightened uncertainty in upstream industries (e.g., chemical manufacturing, iron and steel mills) behaves like a negative supply shock—raising prices and lowering employment across the production network. In contrast, greater uncertainty in downstream industries (e.g., automotive manufacturing, insurance carriers) behaves like an adverse demand shock, reducing both prices and employment. At the aggregate level, the inflation response depends on where uncertainty originates within the supply chain. A multi-sector model with time-varying sectoral uncertainty demonstrates that production linkages play a central role in explaining these empirical findings. Matteo Cacciatore Giacomo Candian 2025-06 This project uses unique Chilean administrative data to shed light on how production networks might play a key role in shaping the macroeconomic impacts of green transition policies. First, using customs and firm-to-firm transaction data that covers the universe of firms in Chile, we build the fossil fuel consumption and the direct CO2 emissions at the firm, sectoral, and aggregate levels. In line with the official national sources, the electricity generation sector is the most important contributor to aggregate CO2 emissions, followed by the manufacturing, transport, and mining sectors. Then, we study the role of input-output linkages in propagating CO2 emissions to the rest of the economy. To do so, we construct the production network and the carbon footprint at the firm level using firm-to-firm transaction data from the Chilean IRS, and we validate our results with the input-output tables approach used in the literature. The results show that the electricity generation sector is central in the network, with potentially important downstream spillover effects, while the mining sector is located in the outer part of the network with rich upstream connections. Also, we show that the copper mining industry is the most exposed one to a carbon tax scheme implemented on all the firms in the economy and also to one that only targets the electricity generation sector. Pablo Acevedo Elias Albagli Gonzalo García-Trujillo María Antonia Yung carbon emissions, production network, carbon footprint, downstream and upstream propagation, administrative firm-level data 2025-07 This paper presents a cyber-physical systems (CPS) framework to model the interplay between market price dynamics and social belief formation in a decentralized setting. The physical layer captures the evolution of prices through a networked market system governed by linear supply, demand, and crossprice elasticity relationships. The cyber layer represents belief formation via a hypergraph-structured learning model, where agents update expectations through distributed Kalman filters based on noisy price observations and group-level interactions. We analyze how informational frictions—driven by social structure, media influence, or cognitive limitations—induce delays in belief con-vergence to equilibrium prices. These delays, in turn, generate dynamic welfare losses due to suboptimal economic decisions. By linking convergence rates to hypergraph Laplacian spectra, we show how group-level information structures determine the speed and equity of learning processes. Our findings provide a theoretical foundation for studying misinformation and its economic costs in markets shaped by decentralized learning and social influence. Papastaikoudis, I. Watson, J. Lestas, I. Cybernetics of Economic Networks, Distributed Kalman filter, Social Welfare 2025-06-20 This paper investigates International Research Collaboration (IRC) among European Union (EU) countries from 2011 to 2022, with emphasis on gender-based authorship patterns. Drawing from the Web of Science Social Science Citation Index (WoS-SSCI) database, a large dataset of IRC articles was constructed, annotated with categories of authorship based on gender, author affiliation, and COVID-19 subject as topic. Using network science, the study maps collaboration structures and reveals gendered differences in co-authorship networks. Results highlight a substantial rise in IRC over the decade, particularly with the USA and China as key non-EU partners. Articles with at least one female author were consistently less frequent than those with at least one male author. Notably, female-exclusive collaborations showed distinctive network topologies, with more centralized (star-like) patterns and shorter tree diameters. The COVID-19 pandemic further reshaped collaboration dynamics, temporarily reducing the gender gap in IRC but also revealing vulnerabilities in female-dominated research networks. These findings underscore both progress and persistent disparities in the gender dynamics of EU participation in IRC. Elsa Fontainha Tanya Araújo International Research Collaboration, European Union, Network Analysis, Gender differences, Scientometrics. 2025-07 Peer grading is an educational system in which students assess each other's work. It is commonly applied under Massive Open Online Course (MOOC) and offline classroom settings. With this system, instructors receive a reduced grading workload, and students enhance their understanding of course materials by grading others' work. Peer grading data have a complex dependence structure, for which all the peer grades may be dependent. This complex dependence structure is due to a network structure of peer grading, where each student can be viewed as a vertex of the network, and each peer grade serves as an edge connecting one student as a grader to another student as an examinee. This article introduces a latent variable model framework for analyzing peer grading data and develops a fully Bayesian procedure for its statistical inference. This framework has several advantages. First, when aggregating multiple peer grades, the average score and other simple summary statistics fail to account for grader effects and, thus, can be biased. The proposed approach produces more accurate model parameter estimates and, therefore, more accurate aggregated grades by modeling the heterogeneous grading behavior with latent variables. Second, the proposed method provides a way to assess each student's performance as a grader, which may be used to identify a pool of reliable graders or generate feedback to help students improve their grading. Third, our model may further provide insights into the peer grading system by answering questions such as whether a student who performs better in coursework also tends to be a more reliable grader. Finally, thanks to the Bayesian approach, uncertainty quantification is straightforward when inferring the student-specific latent variables as well as the structural parameters of the model. The proposed method is applied to two real-world datasets. Mignemi, Giuseppe Chen, Yunxiao Moustaki, Irini peer grading; rating model; cross-classified model; Bayesian modeling 2025-06-16 The paper studies the development of the Economics of Science as a new emerging field in the social sciences during the period 1994-2023. To identify the community of scholars working on this new scientific topic, we examine authors citing two seminal papers and use network analysis to investigate the cognitive and organizational characteristics of the community of authors. Our findings suggest that the Economics of Science is still in the process of becoming an independent and cohesive field, exhibiting a highly fragmented structure. We also study the role of the "Workshop on the Organisation, Economics, and Policy of Scientific Research" (WOEPS), initiated in 2007, for the Economics of Science community. We show that WOEPS presenters have more economists of science as coauthors and are better positioned to connect different clusters of authors in the wider Economics of Science network than other members of the network, highlighting its importance for linking scholars in the field. We also show that WOEPS papers are published in higher "quality" journals, receive relatively more citations, and significantly more citations from within the Economics of Science field compared to other Economics of Science papers. Daniel Souza Aldo Geuna Cornelia Lawson Economics of Science, Scientific Communities, Network Analysis, Field Formation 2025 Using a large-scale real effort experiment, we explore whether and how different peer assignment mechanisms affect worker performance and stress. Letting individuals choose whom to compare to increases productivity to the same extent as a targeted exogenous matching policy designed to maximize motivational spillovers. These effects are significantly larger than those obtained through random assignment and their magnitude is comparable to the impact of an increase in pay of about 10 percent. A downside of targeted peer assignment is that, unlike endogenous peer selection, it leads to a large increase in stress. The key advantage of letting workers choose whom to compare to is that it allows those workers who want to be motivated to compare to a motivating peer while also permitting those for whom social comparisons have little benefits or are too stressful to avoid them. Finally, we show that social comparisons yield stronger motivational effects than comparable non-social goals. Julien Senn Jan Schmitz Christian Zehnder social comparisons, productivity, stress, incentives, real effort 2025