|
on Network Economics |
| By: | Joseph Root; Evan Sadler |
| Abstract: | We demonstrate that a ubiquitous feature of network games, bilateral strategic interactions, is equivalent to having player utilities that are additively separable across opponents. We distinguish two formal notions of bilateral strategic interactions. Opponent independence means that player i's preferences over opponent j's action do not depend on what other opponents do. Strategic independence means that how opponent j's choice influences i's preference between any two actions does not depend on what other opponents do. If i's preferences jointly satisfy both conditions, then we can represent her preferences over strategy profiles using an additively separable utility. If i's preferences satisfy only strategic independence, then we can still represent her preferences over just her own actions using an additively separable utility. Common utilities based on a linear aggregate of opponent actions satisfy strategic independence and are therefore strategically equivalent to additively separable utilities--in fact, we can assume a utility that is linear in opponent actions. |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2602.16071 |
| By: | Raman Ebrahimi; Sepehr Ilami; Babak Heydari; Isabel Trevino; Massimo Franceschetti |
| Abstract: | Standard models of bounded rationality typically assume agents either possess accurate knowledge of the population's reasoning abilities (Cognitive Hierarchy) or hold dogmatic, degenerate beliefs (Level-$k$). We introduce the ``Connected Minds'' model, which unifies these frameworks by integrating iterative reasoning with a parameterized network bias. We posit that agents do not observe the global population; rather, they observe a sample biased by their network position, governed by a locality parameter $p$ representing algorithmic ranking, social homophily, or information disclosure. We show that this parameter acts as a continuous bridge: the model collapses to the myopic Level-$k$ recursion as networks become opaque ($p \to 0$) and recovers the standard Cognitive Hierarchy model under full transparency ($p=1$). Theoretically, we establish that network opacity induces a \emph{Sophisticated Bias}, causing agents to systematically overestimate the cognitive depth of their opponents while preserving the log-concavity of belief distributions. This makes $p$ an actionable lever: a planner or platform can tune transparency, globally or by segment (a personalized $p_k$), to shape equilibrium behavior. From a mechanism design perspective, we derive the \emph{Escalation Principle}: in games of strategic complements, restricting information can maximize aggregate effort by trapping agents in echo chambers where they compete against hallucinated, high-sophistication peers. Conversely, we identify a \emph{Transparency Reversal} for coordination games, where maximizing network visibility is required to minimize variance and stabilize outcomes. Our results suggest that network topology functions as a cognitive zoom lens, determining whether agents behave as local imitators or global optimizers. |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2602.10053 |
| By: | Simon Finster; Paul W. Goldberg; Edwin Lock; Matilde Tullii |
| Abstract: | We explore stability and fairness considerations in decentralized networked markets with bilateral contracts, building on the trading networks framework [Hatfield et al., 2013]. In our trading network game, we show that a well-defined subset of Nash equilibria can be supported as competitive equilibria. Considering an offer-based trading dynamic as well as a stochastic price clock market, we prove new convergence results to Nash equilibrium and competitive equilibrium, providing a rationale for stability properties in decentralized, dynamic trading networks. Turning to the tension between fairness and (core) stability, we prove several negative results: inessential agents always receive zero utility in any core outcome, and even essential agents can get zero utility in all core outcomes. |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2602.20868 |
| By: | Stylianos Asimakopoulos; George Kapetanios; Vasilis Sarafidis; Alexia Ventouri |
| Abstract: | We study spillover effects in corporate toxic emissions using a heterogeneous panel network of U.S. industrial facilities from 2000-2023. Rather than imposing a network structure a priori, we uncover an unobserved web of influence directly from the data using recent advances in high-dimensional network econometrics. Indirect effects transmitted through the estimated network account for about 28% of the total impact of key firm balance-sheet characteristics. By contrast, distance-based networks generate no statistically discernible spillovers, while a priori firm- or industry-based networks substantially overstate within-group spillins relative to the data-driven network. These findings show that who is linked to whom, and with what strength, matters critically for assessing systemic environmental risk and for designing targeted regulation. Methodologically, the paper provides a flexible framework for quantifying facility-level emissions spillovers and their consequences in financial and policy settings. |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2602.21434 |
| By: | Jose De Leon Miranda; Marina Dolfin; George Kapetanios; Leone Leonida |
| Abstract: | We introduce a multiscale measure of network instability based on the joint use of Detrended Cross-Correlation Analysis (DCCA) and Minimum Spanning Tree (MST) filtering. The proposed metric, the Elastic Detrended Cross-Correlation Ratio (Elastic DCCR), is defined as a finite-difference measure of the logarithmic sensitivity of the average MST length to the observation scale. It captures how the structure of cross-correlation networks deforms across different investment horizons. When applied to a network of global equity indices, the Elastic DCCR rises sharply during episodes of financial stress, reflecting increased short-term coordination among investors and a contraction of correlation distances. The measure reveals scale-dependent reconfigurations in network topology that are not visible in single-scale analyses, and highlights clear differences between stressed and stable market regimes. The approach does not assume covariance stationarity and relies only on scale-dependent detrended correlations; as a result, it is broadly applicable to other complex systems in which interaction strength varies with scale. |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2602.10174 |
| By: | Carlos Bianchi (Universidad de la República (Uruguay). Facultad de Ciencias Económicas y de Administración. Instituto de Economía); Pablo Galaso (Universidad de la República (Uruguay). Facultad de Ciencias Económicas y de Administración. Instituto de Economía); Sofía Maio (Universidad de la República (Uruguay). Facultad de Ciencias Económicas y de Administración. Instituto de Economía); Sergio Palomeque (Universidad de la República (Uruguay). Facultad de Ciencias Económicas y de Administración. Instituto de Economía) |
| Abstract: | We analyse advanced ICTs acquisition in the territorial innovation system of the forestry and ecotourism industry in Uruguay, considering both firms and support organizations related to these sectors. Using social network analysis, we identify that firms follow different strategies of collaboration which generate distinct collective outcomes and imply varing individual costs and require diverse firms’ capabilities. We capture two different firms’ collaborative strategies by means of centrality indexes: intermediary (betweenness centrality) and well-connected (eigenvector centrality). While the latter captures a highly connected position in the network but without the costs of intermediating between third parties, the former does capture an intermediary role, which implies a central position in the network, but may entail costs for the holder. We estimate the effects of these collaboration strategies on the firms’ probability to adopt advanced ICTs. Our results show a positive effect of the well- connected collaboration strategy on the adoption of advanced ICTs while intermediary strategy has a negative effect on the probability to adopt advanced ICT. At the same time, a critical role of support organisations, mainly public, in the structure of the network can be observed. Taken together, these results show the relevance of collaboration as well as the trade-offs faced by intermediaries, highlighting the importance of public organisations in fostering knowledge flows between firms. |
| Keywords: | forestry, ecotourism, local innovation system, advanced ICTs, network analysis |
| JEL: | O14 O33 L14 |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:ulr:wpaper:dt-19-25 |
| By: | Wojciech Misiak; Marcin Dziubi\'nski |
| Abstract: | We study convergence rates of random-order best-response dynamics in games on networks with linear best responses and strategic substitutes. Combining formal analysis with numerical simulations we identify phenomena that lead to slow convergence. One of the key such phenomena is convergence to stable strategy profiles in parts of the network neighboring sets of nodes which remain inactive until the dynamics is close to converging and then switch to activity, initiating convergence to profiles with a new set of active agents and possibly leading to another iteration of such behavior. We identify structural properties of graphs which make such phenomena more likely. These properties go beyond the spectrum of a graph, which we demonstrate analyzing convergence rates on co-spectral mates. |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2602.15986 |
| By: | Alessandro Ferrari; Andreas Freitag; Eric Kammerlander; Sarah Lein; Frank Pisch |
| Abstract: | We study exchange-rate pass-through and currency choice in international transactions, focusing on bilateral bargaining power in relationships between domestic buyers and foreign suppliers. Using detailed transaction-level data on Swiss imports from 2014-2023 identifying buyers and suppliers, we show that exchange rate pass-through is lower for economically important suppliers within a buyer's network. This pattern is explained by a higher likelihood of invoicing in the buyer's currency, consistent with a bilateral bargaining model of price setting and endogenous currency choice. Our results imply that bilateral bargaining power shapes how foreign shocks affect prices and external adjustment, making policy transmission network dependent. |
| Keywords: | international economics, trade, finance, markets, manufacturing |
| Date: | 2026–02–19 |
| URL: | https://d.repec.org/n?u=RePEc:cep:cepdps:dp2154 |
| By: | Leander Besting; Martin Hoefer; Lars Huth |
| Abstract: | Modern financial networks are highly connected and result in complex interdependencies of the involved institutions. In the prominent Eisenberg-Noe model, a fundamental aspect is clearing -- to determine the amount of assets available to each financial institution in the presence of potential defaults and bankruptcy. A clearing state represents a fixed point that satisfies a set of natural axioms. Existence can be established (even in broad generalizations of the model) using Tarski's theorem. While a maximal fixed point can be computed in polynomial time, the complexity of computing other fixed points is open. In this paper, we provide an efficient algorithm to compute a minimal fixed point that runs in strongly polynomial time. It applies in a broad generalization of the Eisenberg-Noe model with any monotone, piecewise-linear payment functions and default costs. Moreover, in this scenario we provide a polynomial-time algorithm to compute a maximal fixed point. For networks without default costs, we can efficiently decide the existence of fixed points in a given range. We also study claims trading, a local network adjustment to improve clearing, when networks are evaluated with minimal clearing. We provide an efficient algorithm to decide existence of Pareto-improving trades and compute optimal ones if they exist. |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2602.16387 |
| By: | Mattia Marzi; Tiziano Squartini |
| Abstract: | Networks underpin systems that range from finance to biology, yet their structure is often only partially observed. Current reconstruction methods typically fit the parameters of a model anew to each snapshot, thus offering no guidance to predict future configurations. Here, we develop a Bayesian approach that uses the information about past network snapshots to inform a prior and predict the subsequent ones, while quantifying uncertainty. Instantiated with a single-parameter fitness model, our method infers link probabilities from node strengths and carries information forward in time. When applied to the Electronic Market for Interbank Deposit across the years 1999-2012, our method accurately recovers the number of connections per bank at subsequent times, outperforming probabilistic benchmarks designed for analogous, link prediction tasks. Notably, each predicted snapshot serves as a reliable prior for the next one, thus enabling self-sustained, out-of-sample reconstruction of evolving networks with a minimal amount of additional data. |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2602.21869 |
| By: | Francesca Grassetti; Rossana Mastrandrea |
| Abstract: | Community detection based on modularity maximization is one of the most widely used approaches for uncovering mesoscale structures in complex networks. However, it is well known that the modularity function exhibits a highly degenerate optimization landscape: a large number of structurally distinct partitions attain close modularity values. This degeneracy raises issues of instability, reproducibility, and interpretability of the detected communities. We propose a simple and user-friendly post-processing method to address this problem by selecting a representative partition among the set of high-modularity solutions. The proposed approach is model-agnostic and can be applied a posteriori to the output of any modularity-based community detection algorithm. Rather than seeking the optimal partition in terms of modularity, our method aims to identify a solution that best represents the structural features shared across degenerate partitions. We compare our approach with consensus clustering methods, which pursue a similar objective, and show that the resulting partitions are highly consistent, while being obtained through a substantially simpler procedure that does not require additional optimization steps or external software packages. Moreover, unlike standard consensus clustering techniques, the proposed method can be applied to networks with both positive and negative edge weights, making it suitable for a wide range of applications involving signed networks and correlation-based systems, such as social, financial, and neuroscience networks. Overall, the method provides a practical and robust tool for handling degeneracy in modularity-based community detection, combining simplicity with broad applicability across different types of networks and real-world problems. |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2602.21838 |
| By: | Laura Alfaro; Paola Conconi; Fariha Kamal; Zachary Kroff |
| Abstract: | Traditional theories of firm boundaries predict trade between vertically related units of the same firm. Using novel data that combine a comprehensive mapping of U.S. multinationals' production networks with their customs filings, we uncover a strong positive relationship between input-output linkages and trade between parents and their affiliates. We also find that intrafirm trade is prevalent, particularly between geographically proximate units: three-quarters of affiliates in North America trade with their U.S. parent. These results overturn prior findings based on survey data on intrafirm trade. Administrative intrafirm records enable correcting measurement errors in survey data, reconciling traditional theories with empirical evidence. |
| Keywords: | multinational enterprises, intrafirm trade, input-output linkages |
| Date: | 2026–02–18 |
| URL: | https://d.repec.org/n?u=RePEc:cep:cepdps:dp2153 |
| By: | Martín Leites (Universidad de la República (Uruguay). Facultad de Ciencias Económicas y de Administración. Instituto de Economía); Joan Vilá (Universidad de la República (Uruguay). Facultad de Ciencias Económicas y de Administración. Instituto de Economía) |
| Abstract: | This paper provides evidence on the empirical relevance of the inheritance of employers and parental networks as a potential mechanisms of transmission of economic advantage across generations in a segmented labor market as the Uruguayan case. The large size and the high-quality dataset -based on matched administrative tax and social security records- allow us to analyze in detail these channels. The estimates are based on OLS and to identify the causal e!ect of parents’ informal networks on children’s permanent earnings we employed instrumental variable approach. Our main findings are that the incidence of employer inheritance is economically significant, is positively associated with parents’ earnings, and is particularly high at the top of parental distribution. Furthermore, the transmission of the employer is one of the main drivers of intergenerational income mobility. Finally parental informal network has an asymmetric e!ect on children permanent earnings: it becomes an advantage when the parents are at the top of the distribution -via wage premium-, but it is a disadvantage for those children at the bottom of the distribution. |
| Keywords: | Intergenerational mobility, informal networks, inheritance of employers, non-linearities |
| JEL: | J62 J64 J24 D31 |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:ulr:wpaper:dt-04-25 |
| By: | Robert Stehrer (The Vienna Institute for International Economic Studies, wiiw) |
| Abstract: | Global value chains (GVCs) are intricate international networks in which the production and distribution of goods and services across multiple economies and industries is coordinated. Their complexity introduces strategic dependencies when economies or industries rely heavily on a limited number of foreign suppliers. Such dependencies can also create additional vulnerabilities, particularly at choke points (i.e. key links or nodes in the chain) whose disruption – whether due to political instability and geopolitical tensions, natural disasters, pandemics or policy shocks and trade restrictions – can halt production. This study builds on previous research by examining two factors (i) size dependencies arising when an importing economy-industry pair relies largely on the inputs of a partner economies, and (ii) choke dependencies, where imports from one economy pass through another, creating potential choke points. Choke dependency is particularly concerning, as disruptions in the choke economy can impact not only its direct exports but also the flow of goods from other suppliers. Using the multi-country input-output tables (MC IOTs), this study introduces two indicators to assess dependencies (i) ‘size dependency’, based on the share of an economy-industry’s foreign output sourced from a specific partner, and (2) ‘choke dependency’, based on the pass-through frequency (ptf) indicator, which reveals how often inputs from third economies are routed through a particular partner. An economy-industry pair is considered dependent if it meets thresholds for size dependency, choke dependency or both. This comprehensive approach aims to offer a deeper understanding of systemic vulnerabilities in global trade networks. |
| Keywords: | Global value chains, choke points, dependencies, vulnerabilities, GVC metrics |
| JEL: | C67 F14 F15 |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:wii:rpaper:rr:480 |
| By: | Doron Avramov; Xin He |
| Abstract: | This paper develops a unified framework that links firm-level predictive signals, cross-asset spillovers, and the stochastic discount factor (SDF). Signals and spillovers are jointly estimated by maximizing the Sharpe ratio, yielding an interpretable SDF that both ranks characteristic relevance and uncovers the direction of predictive influence across assets. Out-of-sample, the SDF consistently outperforms self-predictive and expected-return benchmarks across investment universes and market states. The inferred information network highlights large, low-turnover firms as net transmitters. The framework offers a clear, economically grounded view of the informational architecture underlying cross-sectional return dynamics. |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2602.20856 |