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on Network Economics |
| By: | Ben D'Exelle; Christine Gutekunst; Arno Riedl |
| Abstract: | We conduct an artefactual field experiment in real-existing trade networks to analyze how individual network degree affects bargaining demands and outcomes. We combine data from a bilateral bargaining experiment with data of trade networks in 24 villages in Uganda. To identify the effect of individual degree in the village trade network we experimentally vary the disclosure of participants’ identities in a bargaining pair. We derive hypotheses on how degree should affect behavior and find partial support for them. Specifically, we observe that individual degree affects bargaining demands in the predicted direction when one of the bargainers is informed about the network positions but not when both sides are informed. Moreover, network degree affects the likelihood of agreements and earnings, irrespective of the knowledge of the network positions of bargaining partners. |
| Keywords: | bargaining, social networks, network degree, experiments. |
| JEL: | C78 C90 L14 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_11832 |
| By: | Michael Koch; Antonella Nocco |
| Abstract: | This paper introduces a novel mechanism by emphasizing benefits for firms through participation in buyer networks among firms that source the same locally produced inputs. In a first step, we utilize register-based data from Denmark to generate a firm-specific buyer network variable which relies on firms’ industrial input structures and imports. Utilizing this proxy we provide evidence of cost savings from network participation, as larger buyer networks reduce firms’ input demand. Subsequently, we develop a trade model incorporating vertical linkages and introduce network effects that result in savings in intermediate costs. Our theory posits that the magnitude of these savings may be associated with the effectiveness of knowledge transmission among network participants. Consequently, firms operating in regions with efficient knowledge transmission networks may realize greater savings in intermediate input costs, leading to increased profits from local and export sales. In a last step, we provide empirical evidence supporting our theoretical predictions by demonstrating the positive impact of buyer networks based on relationship-specific products on domestic firm revenues. |
| Keywords: | new trade theory, vertical linkages, network effects. |
| JEL: | F12 F15 R12 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_11815 |
| By: | Robert Ghrist; Julian Gould; Miguel Lopez; Hans Riess |
| Abstract: | Modern financial networks involve complex obligations that transcend simple monetary debts: multiple currencies, prioritized claims, supply chain dependencies, and more. We present a mathematical framework that unifies and extends these scenarios by recasting the classical Eisenberg-Noe model of financial clearing in terms of lattice liability networks. Each node in the network carries a complete lattice of possible states, while edges encode nominal liabilities. Our framework generalizes the scalar-valued clearing vectors of the classical model to lattice-valued clearing sections, preserving the elegant fixed-point structure while dramatically expanding its descriptive power. Our main theorem establishes that such networks possess clearing sections that themselves form a complete lattice under the product order. This structure theorem enables tractable analysis of equilibria in diverse domains, including multi-currency financial systems, decentralized finance with automated market makers, supply chains with resource transformation, and permission networks with complex authorization structures. We further extend our framework to chain-complete lattices for term structure models and multivalued mappings for complex negotiation systems. Our results demonstrate how lattice theory provides a natural language for understanding complex network dynamics across multiple domains, creating a unified mathematical foundation for analyzing systemic risk, resource allocation, and network stability. |
| Date: | 2025–03 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2503.17836 |
| By: | Giacomo Zelbi; Leonardo Niccol\`o Ialongo; Stefan Thurner |
| Abstract: | The networked nature of supply chains makes them susceptible to systemic risk, where local firm failures can propagate through firm interdependencies that can lead to cascading supply chain disruptions. The systemic risk of supply chains can be quantified and is closely related to the topology and dynamics of supply chain networks (SCN). How different network properties contribute to this risk remains unclear. Here, we ask whether systemic risk can be significantly reduced by strategically rewiring supplier-customer links. In doing so, we understand the role of specific endogenously emerged network structures and to what extent the observed systemic risk is a result of fundamental properties of the dynamical system. We minimize systemic risk through rewiring by employing a method from statistical physics that respects firm-level constraints to production. Analyzing six specific subnetworks of the national SCNs of Ecuador and Hungary, we demonstrate that systemic risk can be considerably mitigated by 16-50% without reducing the production output of firms. A comparison of network properties before and after rewiring reveals that this risk reduction is achieved by changing the connectivity in non-trivial ways. These results suggest that actual SCN topologies carry unnecessarily high levels of systemic risk. We discuss the possibility of devising policies to reduce systemic risk through minimal, targeted interventions in supply chain networks through market-based incentives. |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2504.12955 |
| By: | Jens Leth Hougaard (University of Copenhagen, DK-1958 Frederiksberg C, Denmark); Mich Tvede (School of Economics, University of Sheffield, Sheffield S1 4DT, UK) |
| Abstract: | We consider network formation. A set of locations can be connected in various network configurations. Every network has a cost and every agent has an individual value of every network. A planner aims at implementing a welfare maximizing network and allocating the resulting cost, but information is asymmetric: agents are fully informed and the planner is ignorant. Full implementation in Nash and strong Nash equilibria is studied. We show the correspondence consisting of welfare maximizing networks and individually rational cost allocations is implementable. We construct a minimal Nash implementable, welfare maximizing, and individually rational solution in the set of upper hemi-continuous and Nash implementable solutions. It is not possible to have full implementation single valued solutions such as the Shapley value. |
| Keywords: | Networks; Welfare maximization; Nash Implementation; Strong Nash Implementation |
| JEL: | C70 C72 D71 D85 |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:shf:wpaper:2025005 |
| By: | Michael J. Yuan; Carlos Campoy; Sydney Lai; James Snewin; Ju Long |
| Abstract: | Decentralized AI agent networks, such as Gaia, allows individuals to run customized LLMs on their own computers and then provide services to the public. However, in order to maintain service quality, the network must verify that individual nodes are running their designated LLMs. In this paper, we demonstrate that in a cluster of mostly honest nodes, we can detect nodes that run unauthorized or incorrect LLM through social consensus of its peers. We will discuss the algorithm and experimental data from the Gaia network. We will also discuss the intersubjective validation system, implemented as an EigenLayer AVS to introduce financial incentives and penalties to encourage honest behavior from LLM nodes. |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2504.13443 |
| By: | Tobias Reisch; Andr\'as Borsos; Stefan Thurner |
| Abstract: | Supply chain networks (SCN) form the structural backbone of any society. They constitute the societal metabolism that literally produces everything for everybody by coordinating practically every single person on the planet. SCNs are by no means static but undergo permanent change through the entry and exit of firms and the re-arrangement of supply relations. Here we use a unique dataset to explore the temporal evolution of firms and their supplier-buyer relations of a national SCN. Monthly reported value added tax data from Hungary from 2014 to 2022 allows us to reconstruct the entire economy with 711, 248 companies and 38, 644, 400 connections, covering practically every re-structuring event of an entire economy at firm-level resolution. We find that per year about 25\% of firms exit the SCN while 28\% new ones enter. On average, 55\% of all supply-links present in one year will not be present in the next. We report the half-life time of supply-links to be 13 months. New links attach super-preferentially to firms with a probability, $p(i)\propto k_i^{1.08}$, with $k_i$ firm $i$'s number of supply-connections. We calibrate a simple statistical network generation model that reproduces the stylized characteristics of the dominant Hungarian SCN. The model not only reproduces local network features such as in- and out-degree distributions, assortativity and clustering structure, but also captures realistic systemic risk profiles. We discuss the present model in how rewiring dynamics of the economy is essential for quantifying its resilience and to estimate shock propagation. |
| Date: | 2025–03 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2503.20594 |
| By: | Matteo Orlandini (Université Côte d'Azur, CNRS, GREDEG, France; Institute of Economics, Scuola Superiore Sant'Anna, Italy); Sebastiano Michele Zema (Scuola Normale Superiore, Italy); Mauro Napoletano (Université Côte d'Azur, CNRS, GREDEG, France; Sciences Po, OFCE, France; Institute of Economics, Scuola Superiore Sant'Anna, Italy); Giorgio Fagiolo (Institute of Economics, Scuola Superiore Sant'Anna, Italy) |
| Abstract: | The node degree distribution of an inferred financial network is often characterized by a small number of nodes with a large number of connections and many nodes with few connections. To date, there is no empirical evidence on how this stylized statistical fact can be useful in predicting fluctuations of financial assets. In this paper, we explore this possibility by modifying well-known time-series models and augmenting them with covariates from a reconstructed network, selecting nodes that are identified as the most connected to the index of interest. We then analyze the out-of-sample performance of these models across different volatility proxies. The results show that nodes belonging to the right tail of the degree distribution possess high predictive power over financial aggregates, independently of the volatility measure used. Our findings suggest that incorporating the topological information that arises from this statistical regularity in financial networks can enhance the accuracy of traditional predictive models. |
| Keywords: | Volatility forecasting, Network-augmented models, Cross-border volatility spillovers, Equity indexes |
| JEL: | G17 G11 |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:gre:wpaper:2025-19 |
| By: | Castells-Quintana, David; Dominguez, Alvaro; Santos-Marquez, Felipe |
| Abstract: | We study the diffusion of adoptions of green technologies in Japan after the 2011 Fukushima incident. We find that, on average, municipalities within a 120 km radius of a given nuclear power plant adopted green technology at a higher rate than those outside that radius. We then rely on a network diffusion model to analyze the direction, speed, and order in which municipalities adopted said technology. Next, we perform a counterfactual analysis by targeting key spreaders to alter the diffusion process. Finally, we propose a novel targeting method accounting for possible "bottlenecks" preventing the propagation process in the network. |
| Keywords: | Energy Transition, Networks, Technology Diffusion |
| JEL: | C15 O33 P11 P18 Q42 |
| URL: | https://d.repec.org/n?u=RePEc:agi:wpaper:02000083 |
| By: | Tuong Manh Vu; Ernesto Carrella; Robert Axtell; Omar A. Guerrero |
| Abstract: | We develop a model where firms determine the price at which they sell their differentiable goods, the volume that they produce, and the inputs (types and amounts) that they purchase from other firms. A steady-state production network emerges endogenously without resorting to assumptions such as equilibrium or perfect knowledge about production technologies. Through a simple version of reinforcement learning, firms with heterogeneous technologies cope with uncertainty and maximize profits. Due to this learning process, firms can adapt to shocks such as demand shifts, suppliers/clients closure, productivity changes, and production technology modifications; effectively reshaping the production network. To demonstrate the potential of this model, we analyze the upstream and downstream impact of demand and productivity shocks. |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2504.16010 |
| By: | Han-Yu Zhu; Yin-Ting Zhang; Wen-Jie Xie; Wei-Xing Zhou |
| Abstract: | The stability of the global food supply network is critical for ensuring food security. This study constructs an aggregated international food supply network based on the trade data of four staple crops and evaluates its structural robustness through network integrity under accumulating external shocks. Network integrity is typically quantified in network science by the relative size of the largest connected component, and we propose a new robustness metric that incorporates both the broadness p and severity q of external shocks. Our findings reveal that the robustness of the network has gradually increased over the past decades, punctuated by temporary declines that can be explained by major historical events. While the aggregated network remains robust under moderate disruptions, extreme shocks targeting key suppliers such as the United States and India can trigger systemic collapse. When the shock broadness p is less than about 0.3 and the shock severity q is close to 1, the structural robustness curves S(p, q) decrease linearly with respect to the shock broadness p, suggesting that the most critical economies have relatively even influence on network integrity. Comparing the robustness curves of the four individual staple foods, we find that the soybean supply network is the least robust. Furthermore, regression and machine learning analyses show that increaseing food (particularly rice and soybean) production enhances network robustness, while rising food prices significantly weaken it. |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2504.08857 |
| By: | Darpoe, Erik; Dominguez, Alvaro; Martin-Rodriguez, Maria |
| Abstract: | n a scenario featuring two distinct player types, we examine the pairwise stability of stationary networks where agents engage in infinite-horizon bargaining games akin to Manea's framework. Link formation and maintenance costs are contingent upon communication ease and complementarities, with connections between individuals of different types becoming less expensive when complementarities are sufficiently strong. In such instances, various bipartite components emerge as stable, characterized by a lack of direct connections between players of the same type. These components exhibit inequitable disributions of surplus, resulting in asymmetric splits among linked individuals. This contrasts with scenarios where connections between individuals of the same type are less costly, leading to predominantly equitable stable components. OUr findings highlight how complementarities and the relative scarcity of certain types can influence the fairness of bargaining outcomes within networks. |
| Keywords: | Bargaining, Heterogeneity, Networks, Pairwise stability |
| JEL: | C72 C78 D85 |
| URL: | https://d.repec.org/n?u=RePEc:agi:wpaper:02000085 |
| By: | Florian Brandl |
| Abstract: | We consider long-lived agents who interact repeatedly in a social network. In each period, each agent learns about an unknown state by observing a private signal and her neighbors' actions in the previous period before taking an action herself. Our main result shows that the learning rate of the slowest learning agent is bounded from above independently of the number of agents, the network structure, and the agents' strategies. Applying this result to equilibrium learning with rational agents shows that the learning rate of all agents in any equilibrium is bounded under general conditions. This extends recent findings on equilibrium learning and demonstrates that the limitation stems from an inherent tradeoff between optimal action choices and information revelation rather than strategic considerations. |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2504.12136 |
| By: | Elena Prager; Nicholas Tilipman |
| Abstract: | Recent policy proposals seek to regulate out-of-network hospital prices. We study how such regulation affects equilibrium prices, network formation, and hospital exit. We estimate a structural model of insurer-hospital bargaining that allows for out-of-network transactions between non-contracting parties. These transactions generate a notion of exit by rendering hospitals unprofitable under some regulations. Estimation relies on a novel measure of out-of-network prices. We find that reducing out-of-network prices would also lower negotiated prices, but potentially at the cost of narrower hospital networks. Aggressive regulation could induce substantial hospital exit, but only under the restrictive assumption that negotiators cannot anticipate the exits. |
| JEL: | C78 I11 L13 |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:33727 |
| By: | Dror, David Mark |
| Abstract: | This paper develops a rigorous mathematical framework for analyzing trust dynamics and statistical properties in small-scale risk-sharing communities. We establish that small pools with interdependent risks exhibit fundamentally different mathematical properties than large insurance systems, with volatility exceeding stability thresholds by a factor of √(N/Nc) and correlation structures reducing effective pool size by up to 89%. We formalize trust as a mathematically tractable variable with threshold stability properties, proving the existence of critical values TRcritical ∈ [0.65, 0.75] that create bifurcation points in system behavior. Our mathematical analysis demonstrates that network density directly determines trust propagation speed according to precise mathematical relationships. We prove that trust response exhibits asymmetric properties, with negative experiences having 1.5-2.5 times stronger impact than positive experiences of equal magnitude, creating hysteresis effects in system stability. By developing differential equations governing trust evolution and applying network diffusion models, we establish exact conditions for system stability and characterize phase transitions under parameter variation. The mathematical framework enables precise quantification of correlation penalties, network effects, and trust thresholds with applications to community-based risk-sharing systems where conventional statistical approaches fail. Our results transform qualitative concepts of trust and social capital into quantifiable mathematical variables with specific dynamics and stability properties. |
| Abstract: | Diese Arbeit entwickelt ein rigoroses mathematisches Rahmenwerk zur Analyse von Vertrauensdynamiken und statistischen Eigenschaften in kleinskaligen Risikoausgleichsgemeinschaften. Wir zeigen, dass kleine Risikopools mit interdependenten Risiken grundlegend andere mathematische Eigenschaften aufweisen als groß angelegte Versicherungssysteme: Die Volatilität überschreitet die Stabilitätsschwellen um den Faktor √(N/Nc), und Korrelationsstrukturen reduzieren die effektive Poolgröße um bis zu 89 %. Wir formalisieren Vertrauen als mathematisch behandelbare Variable mit stabilitätstheoretischen Schwellenwerten und weisen die Existenz kritischer Werte TR_critical ∈ [0, 65; 0, 75] nach, die als Bifurkationspunkte im Systemverhalten fungieren. Unsere mathematische Analyse zeigt, dass die Netzwerkkonnektivität die Geschwindigkeit der Vertrauensausbreitung direkt bestimmt, gemäß exakt ableitbaren mathematischen Beziehungen. Es wird nachgewiesen, dass die Vertrauensreaktion asymmetrisch verläuft: Negative Erfahrungen wirken sich 1, 5- bis 2, 5-mal stärker aus als gleichwertige positive Erlebnisse, was Hystereseeffekte in der Systemstabilität verursacht. Durch die Entwicklung von Differentialgleichungen zur Beschreibung der Vertrauensentwicklung sowie die Anwendung von Netzwerkdiffusionsmodellen definieren wir exakte Stabilitätsbedingungen und charakterisieren Phasenübergänge bei Parameterveränderungen. Das mathematische Rahmenwerk ermöglicht eine präzise Quantifizierung von Korrelationsverlusten, Netzwerkeffekten und Vertrauensschwellen – insbesondere in gemeinschaftsbasierten Risikoausgleichssystemen, in denen konventionelle statistische Verfahren versagen. Unsere Ergebnisse überführen qualitative Konzepte wie Vertrauen und Sozialkapital in quantifizierbare mathematische Variablen mit spezifischen Dynamiken und Stabilitätseigenschaften. |
| Keywords: | trust dynamics, small risk pools, network diffusion, copula theory, correlation structures, threshold stability |
| JEL: | Z13 G22 D85 C46 C58 C22 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:esprep:316140 |
| By: | Yasushi Sakai; Parfait Atchade-Adelomou; Ryan Jiang; Luis Alonso; Kent Larson; Ken Suzuki |
| Abstract: | This paper proposes a voting process in which voters allocate fractional votes to their expected utility in different domains: over proposals, other participants, and sets containing proposals and participants. This approach allows for a more nuanced expression of preferences by calculating the result and relevance within each node. We modeled this by creating a voting matrix that reflects their preference. We use absorbing Markov chains to gain the consensus, and also calculate the influence within the participating nodes. We illustrate this method in action through an experiment with 69 students using a budget allocation topic. |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2504.13641 |