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on Network Economics |
| By: | Riley James Bendel |
| Abstract: | We study contagion and systemic risk in sparse financial networks with balance-sheet interactions on a directed random graph. Each institution has homogeneous liabilities and equity, and exposures along outgoing edges are split equally across counterparties. A linear fraction of institutions have zero out-degree in sparse digraphs; we adopt an external-liability convention that makes the exposure mapping well-defined without altering propagation. We isolate a single-hit transmission mechanism and encode it by a sender-truncated subgraph G_sh. We define adversarial and random systemic events with shock size k_n = c log n and systemic fraction epsilon n. In the subcritical regime rho_out |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2601.04096 |
| By: | Gurkirat Wadhwa; Priyank Sinha |
| Abstract: | Even large firms such as Walmart, Apple, and Coca-Cola face persistent fluctuations in costs, demand, and raw material availability. These are not \textit{rare events} and cannot be evaluated using traditional disruption models focused on infrequent events. Instead, sustained volatility induces opportunistic behavior, as firms repeatedly reconfigure partners in absence of long-term contracts, often due to trust deficits. The resulting web of transient relationships forms opportunistic supply chains (OSCs). To capture OSC evolution, we develop an integrated mathematical framework combining a Geometric Brownian Motion (GBM) model to represent stochastic price volatility, a Bayesian learning model to describe adaptive belief updates regarding partner reliability, and a Latent Order Logistic (LOLOG) network model for endogenous changes in network structure. This framework is implemented in an agent-based simulation to examine how volatility, trust, and network structure jointly shape SC resilience. Our modeling approach identifies critical volatility threshold; a tipping point beyond which the network shifts from a stable, link-preserving regime to a fragmented regime marked by rapid relationship dissolution. We analytically establish monotonic effects of volatility on profitability, trust, and link activation; derive formal stability conditions and volatility-driven phase transitions, and show how these mechanisms shape node importance and procurement behavior. These theoretical mechanisms are illustrated through computational experiments reflecting industry behaviors in fast fashion, electronics, and perishables. Overall, our contribution is to develop an integrated GBM-Bayesian-LOLOG framework to analyze OSC stability and our model can be extended to other OSCs including humanitarian, pharmaceutical, and poultry networks. |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2601.11566 |
| By: | Yaniv Proselkov; Liming Xu; Alexandra Brintrup |
| Abstract: | We propose a financial liquidity policy sharing method for firm-to-firm supply networks, introducing a scalable autonomous control function for viable complex adaptive supply networks. Cooperation and competition in supply chains is reconciled through overlapping collaborative sets, making firms interdependent and enabling distributed risk governance. How cooperative range - visibility - affects viability is studied using dynamic complex adaptive systems modelling. We find that viability needs cooperation; visibility and viability grow together in scale-free supply networks; and distributed control, where firms only have limited partner information, outperforms centralised control. This suggests that policy toward network viability should implement distributed supply chain financial governance, supporting interfirm collaboration, to enable autonomous control. |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2601.13210 |
| By: | Junnan He; Jean-Marc Robin |
| Abstract: | We study a ridge estimator for the high-dimensional two-way fixed effect regression model with a sparse bipartite network. We develop concentration inequalities showing that when the ridge parameters increase as the log of the network size, the bias, and the variance-covariance matrix of the vector of estimated fixed effects converge to deterministic equivalents that depend only on the expected network. We provide simulations and an application using administrative data on wages for worker-firm matches. |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2601.04101 |
| By: | Mijalche Santa (Faculty of Economics-Skopje, Ss. Cyril and Methodius University in Skopje, North Macedonia); Blerton Zejneli (Faculty of Economics-Skopje, Ss. Cyril and Methodius University in Skopje, North Macedonia) |
| Abstract: | This research explores how social capital supports the adoption of artificial intelligence (AI) in developing countries, focusing on the role of "social brokers." A social broker is a trusted individual who occupies a unique position within a network, connecting individuals from different networks or maintaining connections with a larger number of individuals within the existing network. Based on input from the initial phase of the project, conducted in a developing country with high internet use but low AI adoption, we use qualitative research methods to better understand the practical aspects of AI adoption. Our early findings suggest that AI adoption goes beyond the right technology or skills and is strongly influenced by trusted communities and networks that shape decisions about AI adoption. "Social brokers" play a key role in this process. They help close knowledge gaps, address concerns of people who have not adopted AI or have adopted it at a low level, and show how AI can be relevant and useful for specific jobs and tasks. These "social brokers" are often seen as trusted friends, technology influencers, former colleagues, or respected local industry experts. Their presence and activities in tightly connected social networks appear to be very important for reducing the gap in AI adoption. The next phase of this research will focus on identifying the aspects of social capital that influence AI adoption, understanding the relationships that help overcome resistance to adopting AI, and developing strategies that use social capital to encourage faster AI adoption in developing countries. |
| Keywords: | AI, Technology adoption, Social brokers, Developing countries |
| JEL: | O31 O32 O33 |
| Date: | 2025–12–15 |
| URL: | https://d.repec.org/n?u=RePEc:aoh:conpro:2025:i:6:p:342-347 |
| By: | Elisa Botteghi; Martino S. Centonze; Davide Pastorello; Daniele Tantari |
| Abstract: | Cyber risk has become a critical financial threat in today's interconnected digital economy. This paper introduces a cyber-risk management framework for networked digital systems that combines the strategic behavior of players with contagion dynamics within a security game. We address the problem of optimally allocating cybersecurity resources across a network, focusing on the heterogeneous valuations of nodes by attackers and defenders, some areas may be of high interest to the attacker, while others are prioritized by the defender. We explore how this asymmetry drives attack and defense strategies and shapes the system's overall resilience. We extend a method to determine optimal resource allocation based on simple network metrics weighted by the defender's and attacker's risk profiles. We further propose risk measures based on contagion paths and analyze how propagation dynamics influence optimal defense strategies. Numerical experiments explore risk versus cost efficient frontiers varying network topologies and risk profiles, revealing patterns of resource allocation and cyber deception effects. These findings provide actionable insights for designing resilient digital infrastructures and mitigating systemic cyber risk. |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2601.16805 |
| By: | Anyfantaki, Sofia; Martynova, Natalya; Avramidis, Panagiotis |
| Abstract: | We study how deposit rate shocks transmit across banking markets through digital social ties. Depositors’ inattention implies that households react to outside rate changes only when social networks make these changes salient, inducing connected banks to raise their own rates. Using merger-driven shocks to local deposit rates and county-level social connectedness, we show that small banks increase rates in response to shocks occurring in socially linked but geographically distant counties. Spillovers are economically meaningful, persistent, and stronger in competitive markets and in counties with more financially sophisticated households. Digital social ties therefore activate depositor search and integrate deposit markets across space. JEL Classification: G20, G21, G23, G29 |
| Keywords: | deposit pricing, information transmission, limited attention, social connections, uniform pricing |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:ecb:ecbwps:20263178 |