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on Network Economics |
| By: | Chai, Ziyi; Dietzenbacher, Bas (RS: GSBE other - not theme-related research, QE Math. Economics & Game Theory) |
| Abstract: | This paper studies classes of two-bound core games with communication restrictions modeled by an undirected graph. We focus on unanimity games, bankruptcy games, 1-convex games, big boss games, clan games, compromise stable games, one-bound core games, and the entire class of two-bound core games. For each of these classes, we characterize all communication graphs that guarantee that the graph-restricted game belongs to the same class as the original two-bound core game. |
| Keywords: | two-bound core games, communication situations, graph-restricted games |
| JEL: | C71 |
| Date: | 2026–09–10 |
| URL: | https://d.repec.org/n?u=RePEc:unm:umagsb:2026008 |
| By: | Sylvain Béal (Université Marie et Louis Pasteur, CRESE UR3190, F-25000 Besançon, France); Emmanuelle Lebeuf (Université Marie et Louis Pasteur, F-25000 Besançon, France); Kevin Techer (Université Marie et Louis Pasteur, CRESE UR3190, F-25000 Besançon, France) |
| Abstract: | We introduce a new allocation rule for network games that combines a local component and a global component. The local component depends only on the links incident to each player, whereas the global component allocates a surplus equally among the members of each connected component. We characterize this allocation rule by three classical axioms together with a new axiom, Fairness under Neighborhood Restriction, which requires that two adjacent players experience the same payoff variation when the network is restricted to their local neighborhoods, that is, to the sets of links incident to each player. We also examine an alternative allocation rule that differs only in its global component, distributing the surplus within each connected component in proportion to players’ degrees in the network. |
| Keywords: | Network games, Fairness under Neighborhood Restriction, Neighborhood Equal Surplus Division, axiomatic characterization |
| JEL: | C71 |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:crb:wpaper:2026-08 |
| By: | Yuya Sasaki; Baoning Zheng |
| Abstract: | We develop a novel method of inference for network-dependent high-dimensional random vectors. Dependence is characterized via a functional dependence measure based on graph distance, allowing the approximation theory to capture the interaction between the decay of dependence and the growth of network neighborhoods. We establish Gaussian approximation results for the maximum norm under finite-moment and sub-Weibull conditions, providing explicit conditions under which the dimension may increase with the network size. We also propose a high-dimensional network HAC covariance estimator and establish its convergence properties, yielding a feasible procedure for simultaneous inference. Simulation studies demonstrate favorable finite-sample performance of the proposed method. We apply the procedure to study how spillover effects vary with an index of network homophily by constructing confidence bands for the conditional spillover-effect function. The application reveals heterogeneity and local significance that would be obscured by conventional low-dimensional inference. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.26522 |
| By: | Bastien Buchwalter; Francis X. Diebold; Kamil Yilmaz |
| Abstract: | We extend the clustered connectedness framework of Buchwalter, Diebold and Yilmaz (2026) in two complementary directions that improve the robustness and interpretability of cross-cluster connectedness. First, we develop a diagnostic for residual ordering sensitivity by characterizing the distribution of cluster-level net connectedness across all admissible identification orderings and, in particular, by pairing first- and last-position orderings while holding fixed the relative ordering of all other clusters. Second, we introduce a dedicated cluster of control variables to absorb variation associated with observed common macro-financial factors while preserving the computational scalability of the clustered framework. The control cluster is fixed first, and bank innovations are residualized with respect to it before the remaining bank clusters are permuted and orthogonalized as usual, leaving the number of admissible bank-cluster identification strategies unchanged. Under the maintained recursive assumption that control-cluster innovations are contemporaneously exogenous to bank-cluster innovations, the remaining cross-cluster connectedness among the bank clusters can be interpreted as bank-to-bank transmission net of those observed common-factor shocks. We apply the methodology to seventy-one global banks grouped into seven regional clusters over 2003--2024. The treatment of common macro-financial factors materially affects both system-wide cross-group connectedness and cluster-level net positions. Placing the controls in a dedicated first cluster also substantially reduces paired first-versus-last ordering sensitivity across all seven bank clusters, with especially large reductions for the United States and the European clusters. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.05792 |
| By: | Federico Forte (BBVA Research) |
| Abstract: | This paper introduces a higher-order network framework based on hypergraphs for assessing systemic risk in bank-firm credit relationships. Unlike conventional network approaches, which rely on pairwise connections, hypergraphs explicitly represent groups of financial institutions jointly exposed to the same borrower as a multilateral interaction. We compare traditional centrality metrics with hypergraph-specific measures to identify systemically important financial institutions, applying these techniques empirically to credit registry data from the Central Bank of Argentina. An adjusted version of the H-eigenvector centrality measure is also proposed, which nonlinearly combines each creditor’s lending amount with the centrality of its co-lenders participating in the same higher-order interactions. We then assess the systemic impact associated with distress shocks to the top-ranked entities selected by each metric. The proposed hypergraph-based measure consistently identifies sets of institutions whose distress generates greater amplified systemic impact than those selected by traditional centrality measures. To the best of our knowledge, this paper provides the first application of hypergraphs to modern bank-firm credit networks for systemic risk assessment. The results show that explicitly accounting for higher-order interactions reveals dimensions of systemic relevance not fully captured by dyadic metrics, providing financial supervisors with a complementary tool for identifying systemically important institutions. This framework can also be readily applied to different countries and financial systems. |
| Keywords: | Systemic risk, Hypergraphs, Financial institutions, Credit networks, Financial stability |
| JEL: | D85 G21 G28 C63 |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:aoz:wpaper:406 |
| By: | Sultan Amed; Sayantan Banerjee |
| Abstract: | Economic networks are often estimated separately over two periods, and changes in their edge sets are interpreted as structural rewiring. Since both networks are estimated, observed turnover also reflects graph-selection error. We study the two-snapshot Hamming-turnover functional under a homogeneous edge-misclassification model. With known sensitivity and specificity and conditional independence of the estimated edge indicators across periods, latent turnover admits a closed-form unbiased adjustment based only on observed turnover and the two estimated graph sizes. We then examine the effects of sparsity, calibration error and dependence across periods. When the number of true links is proportional to $p$, a false-positive probability of order $p^{-1}$ generates expected spurious turnover of order $p$. An error of the same order in calibrating the false-positive probability can likewise leave order-$p$ bias after adjustment. We also derive the bias induced by cross-period dependence and give sufficient conditions for consistency relative to network size. Numerical results illustrate the finite-sample implications. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.20044 |
| By: | Marc-Antoine Faure; César Ducruet |
| Abstract: | Container shipping has become a cornerstone of global supply chains, regional integration, and port-city development. The globalization of trade and, at the same time, the increased vulnerability of supply chains have created a more efficient yet fragile system. In this chapter, using a unique dataset of more than four million ship movements, we uncover the local and global dynamics of container flows. Starting in the 1970s, with the diffusion of container technology in international trade, we analyse fifty years of global trade through the lens of network structures and community detection. We document (i) an almost continuous expansion in nodes and links with improving topological efficiency up to the mid-2010s, followed by (ii) the consequences of recent shocks, such as COVID-19, Red Sea disruptions, and the Panama Canal drought, which highlights the vulnerability of the container shipping network. Using single linkage analysis, we also show the evolution of trade dependencies, the emergence of China particularly towards Africa over the last decade, and the persistence of certain port systems such as the European system around Rotterdam. We open a new avenue of research by combining global and local, systemic and long-term quantitative analyses to understand the dynamics of international trade. |
| Keywords: | complex networks; international trade; container shipping |
| JEL: | F14 R40 L91 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:drm:wpaper:2026-19 |
| By: | Eleanor A. Power; Monique Borgerhoff Mulder; Samuel Bowles; Matthew O. Jackson; Jeremy Koster; Daniel Redhead; Thomas Rutter; Sahana Subramanyam; Justin Weltz; Nurul Alam; Sarah Alami; Alexandra Alvergne; Curtis Atkisson; Michele Barnes; Bret Beheim; Christine M. Beitl; Madeline Brown; Mark Caudell; Wendy Ch\'{a}vez-P\'{a}ez; Komal Chauhan; Joshua Cinner; Siobh\'{a}n Cully; Augusto Dalla Ragione; Angelina L. DeMarco; Ivan Deschenaux; Federico Fernandez; Juan Pablo Ferreiro; Drew Gerkey; Matthew Gervais; Christopher Golden; Gianluca Grimalda; Werner Hertzog; Paul L. Hooper; Karen Kramer; Geoff Kushnick; Banrida Langstieh; Rodrigo Lazo; Sheina Lew-Levy; Shane Macfarlan; Emmanuel Maliti; Karl J. Mertens; Madalena Monteban; Rafael Morais Chiaravalloti; Daniel Murphy; Kathryn Oths; Alejandro P\'{e}rez Velilla; Emily Post; Sean Prall; Cody Ross; Anirudh Sankar; Brooke Scelza; Michael Schnegg; Edmond Seabright; Mary K. Shenk; Kathrine E. Starkweather; Chun-Yi Sum; Bram Tucker; Bapu Vaitla; Vivek Venkataraman; John P. Ziker |
| Abstract: | Despite theory tying wealth inequality to social structure, empirical evidence has been limited to a few studies based on online social media data. This study uses a very different type of data, expands the global coverage to very different types of societies, and investigates new questions. In particular, we collect data from ~3500 sharing units (households) in 46 communities across the globe, representing considerable human social and cultural diversity. In each, we analyze the relationship between people's material wealth and the structure of social networks: borrowing money, sharing food, working together, socializing, etc. In almost all communities, a sharing unit's material wealth is positively associated with the number of other sharing units it both helps and is helped by. A sharing unit's wealth is also associated with the relative wealth of the sharing units to which it is linked---a form of economic homophily. Notably, communities with greater wealth inequality are also characterized by a network structure in which poorer sharing units are less well connected to wealthier ones. We augment our unique cross-cultural data with other community-level environmental, institutional, and economic attributes, opening new avenues for future research into the co-determination of wealth and social networks. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.25488 |