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on Network Economics |
| By: | Ashwin Bhattathiripad; Vipin P Veetil |
| Abstract: | This paper develops an algorithm to reconstruct large weighted firm-to-firm networks using information about the size of the firms and sectoral input-output flows. Our algorithm is based on a four-step procedure. We first generate a matrix of probabilities of connections between all firms in the economy using an augmented gravity model embedded in a logistic function that takes firm size as mass. The model is parameterized to allow for the probability of a link between two firms to depend not only on their sizes but also on flows across the sectors to which they belong. We then use a Bernoulli draw to construct a directed but unweighted random graph from the probability distribution generated by the logistic-gravity function. We make the graph aperiodic by adding self-loops and irreducible by adding links between Strongly Connected Components while limiting distortions to sectoral flows. We convert the unweighted network to a weighted network by solving a convex quadratic programming problem that minimizes the Euclidean norm of the weights. The solution preserves the observed firm sizes and sectoral flows within reasonable bounds, while limiting the strength of the self-loops. Computationally, the algorithm is O(N2) in the worst case, but it can be evaluated in O(N) via sector-wise binning of firm sizes, albeit with an approximation error. We implement the algorithm to reconstruct the full US production network with more than 5 million firms and 100 million buyer-seller connections. The reconstructed network exhibits topological properties consistent with small samples of the real US buyer-seller networks, including fat-tails in degree distribution, mild clustering, and near-zero reciprocity. We provide open-source code of the algorithm to enable researchers to reconstruct large-scale granular production networks from publicly available data. |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2512.02362 |
| By: | Wen Jiang; Yachen Wang; Zeqi Wu; Xingbai Xu |
| Abstract: | This paper develops limit theorems for random variables with network dependence, without requiring that individuals in the network to be located in a Euclidean or metric space. This distinguishes our approach from most existing limit theorems in network econometrics, which are based on weak dependence concepts such as strong mixing, near-epoch dependence, and $\psi$-dependence. By relaxing the assumption of an underlying metric space, our theorems can be applied to a broader range of network data, including financial and social networks. To derive the limit theorems, we generalize the concept of functional dependence (also known as physical dependence) from time series to random variables with network dependence. Using this framework, we establish several inequalities, a law of large numbers, and central limit theorems. Furthermore, we verify the conditions for these limit theorems based on primitive assumptions for spatial autoregressive models, which are widely used in network data analysis. |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2511.17928 |
| By: | Wenbin Wu; Kejiang Qian; Alexis Lui; Christopher Jack; Yue Wu; Peter McBurney; Fengxiang He; Bryan Zhang |
| Abstract: | We curate the DeXposure dataset, the first large-scale dataset for inter-protocol credit exposure in decentralized financial networks, covering global markets of 43.7 million entries across 4.3 thousand protocols, 602 blockchains, and 24.3 thousand tokens, from 2020 to 2025. A new measure, value-linked credit exposure between protocols, is defined as the inferred financial dependency relationships derived from changes in Total Value Locked (TVL). We develop a token-to-protocol model using DefiLlama metadata to infer inter-protocol credit exposure from the token's stock dynamics, as reported by the protocols. Based on the curated dataset, we develop three benchmarks for machine learning research with financial applications: (1) graph clustering for global network measurement, tracking the structural evolution of credit exposure networks, (2) vector autoregression for sector-level credit exposure dynamics during major shocks (Terra and FTX), and (3) temporal graph neural networks for dynamic link prediction on temporal graphs. From the analysis, we observe (1) a rapid growth of network volume, (2) a trend of concentration to key protocols, (3) a decline of network density (the ratio of actual connections to possible connections), and (4) distinct shock propagation across sectors, such as lending platforms, trading exchanges, and asset management protocols. The DeXposure dataset and code have been released publicly. We envision they will help with research and practice in machine learning as well as financial risk monitoring, policy analysis, DeFi market modeling, amongst others. The dataset also contributes to machine learning research by offering benchmarks for graph clustering, vector autoregression, and temporal graph analysis. |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2511.22314 |
| By: | Paolo Bartesaghi; Rosanna Grassi; Pierpaolo Uberti |
| Abstract: | The global balance is a well-known indicator of the behavior of a signed network. Recent literature has introduced the concept of local balance as a measure of the contribution of a single node to the overall balance of the network. In the present research, we investigate the potential of using deviations of local balance from global balance as a criterion for selecting outperforming assets. The underlying idea is that, during financial crises, most assets in the investment universe behave similarly: losses are severe and widespread, and the global balance of the correlation-based signed network reaches its maximum value. Under such circumstances, standard diversification (mainly related to portfolio size) is unable to reduce risk or limit losses. Therefore, it may be useful to concentrate portfolio exposures on the few assets - if such assets exist-that behave differently from the rest of the market. We argue that these assets are those for which the local balance strongly departs from the global balance of the underlying signed network. The paper supports this hypothesis through an application using real financial data. The results, in both descriptive and predictive contexts, confirm the proposed intuition. |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2512.10606 |
| By: | Jordana Blazek; Frederick C. Harris Jr |
| Abstract: | We consider a bipartite network of buyers and sellers, where the sellers run locally independent Progressive Second-Price (PSP) auctions, and buyers may participate in multiple auctions, forming a multi-auction market with perfect substitute. The paper develops a projection-based influence framework for decentralized PSP auctions. We formalize primary and expanded influence sets using projections on the active bid index set and show how partial orders on bid prices govern allocation, market shifts, and the emergence of saturated one-hop shells. Our results highlight the robustness of PSP auctions in decentralized environments by introducing saturated components and a structured framework for phase transitions in multi-auction dynamics. This structure ensures deterministic coverage of the strategy space, enabling stable and truthful embedding in the larger game. We further model intra-round dynamics using an index to capture coordinated asynchronous seller updates coupled through buyers' joint constraints. Together, these constructions explain how local interactions propagate across auctions and gives premise for coherent equilibria--without requiring global information or centralized control. |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2511.19225 |
| By: | Jeff Murugan |
| Abstract: | Information transmitted across modern communication platforms is degraded not only by intentional manipulation (disinformation) but also by intrinsic cognitive decay and topology-dependent social averaging (misinformation). We develop a continuous-fidelity field theory on multiplex networks with distinct layers representing private chats, group interactions, and broadcast channels. Our analytic solutions reveal three universal mechanisms controlling information quality: (i) groupthink blending, where dense group coupling drives fidelity to the initial group mean; (ii) bridge-node bottlenecks, where cross-community flow produces irreversible dilution; and (iii) a network-wide fidelity landscape set by a competition between broadcast truth-injection and structural degradation pathways. These results demonstrate that connectivity can reduce information integrity and establish quantitative control strategies to enhance fidelity in large-scale communication systems. |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2511.18733 |
| By: | Jan Majewski; Francesca Giardini |
| Abstract: | Gossip has been shown to be a relatively efficient solution to problems of cooperation in reputation-based systems of exchange, but many studies don't conceptualize gossiping in a realistic way, often assuming near-perfect information or broadcast-like dynamics of its spread. To solve this problem, we developed an agent-based model that pairs realistic gossip processes with different variants of Trust Game. The results show that cooperators suffer when local interactions govern spread of gossip, because they cannot discriminate against defectors. Realistic gossiping increases the overall amount of resources, but is more likely to promote defection. Moreover, even partner selection through dynamic networks can lead to high payoff inequalities among agent types. Cooperators face a choice between outcompeting defectors and overall growth. By blending direct and indirect reciprocity with reputations we show that gossiping increases the efficiency of cooperation by an order of magnitude. |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2511.20248 |
| By: | Charlie Joyez (Université Côte d'Azur, CNRS, GREDEG, France) |
| Abstract: | We introduce complexity, a Stata command available on SSC that computes generalized complexity indices for specialization matrices. Originally developed for assessing economic complexity with global trade data (Hidalgo and Hausmann, 2009), these metrics have since been extended to various domains including regional development, innovation, and labor economics. The complexity command implements three core methodologies: the eigenvector method (Hausmann et al., 2011a), the Method of Reflection (Hidalgo and Hausmann, 2009), and the fitness-complexity approach (Tacchella et al., 2012). It also computes relatedness metrics such as coherence, adjacency matrices of the activity space network, and the complexity outlook as a measure of complexity potential. We describe the syntax and options, review the underlying algorithms, and provide applied examples |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:gre:wpaper:2025-50 |
| By: | Huan Wu |
| Abstract: | This paper develops a general framework for identifying causal effects in settings with spillovers, where both outcomes and endogenous treatment decisions are influenced by peers within a known group. It introduces the generalized local average controlled spillover and direct effects (LACSEs and LACDEs), which extend the local average treatment effect framework to settings with spillovers and establish sufficient conditions for their point identification without restricting the cardinality of the support of instrumental variables. These conditions clarify the necessity of commonly imposed restrictions to achieve point identification with binary instruments in related studies. The paper then defines the marginal controlled spillover and direct effects (MCSEs and MCDEs), which naturally extend the marginal treatment effect framework to settings with spillovers and are nonparametrically point identified from continuous variation in instruments. These marginal effects serve as building blocks for a broad class of policy-relevant treatment effects, including some causal spillover parameters in the related literature. Semiparametric and parametric estimators are developed, and an application using Add Health data reveals heterogeneity in education spillovers within best-friend networks. |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2511.22643 |