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on Network Economics |
| By: | Yunus C. Aybas; Matthew O. Jackson |
| Abstract: | People learn about opportunities and actions by observing the experiences of their friends. We model how homophily -- the tendency to associate with similar others -- affects both the endogenous quality and diversity of the information accessible to decision makers. Homophily provides higher-quality information, since observing the payoffs of another person is more informative the more similar that person is to the decision maker. However, homophily can lead people to take actions that generate less information. We show how network connectivity influences the tradeoff between the endogenous quantity and quality of information. Although homophily hampers learning in sparse networks, it enhances learning in sufficiently dense networks. |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2602.00934 |
| By: | Michael Balzer; Adhen Benlahlou |
| Abstract: | This paper develops a theory of scientific and technological peer effects to study how individuals' productivity responds to the behavior and network positions of their collaborators across both scientific and inventive activities. Building on a simultaneous equation network framework, the model predicts that productivity in each activity increases in a variation of the Katz-Bonacich centrality that captures within-activity and cross-activity strategic complementarities. To test these predictions, we assemble the universe of cancer-related publications and patents and construct coauthorship and coinventorship networks that jointly map the collaboration structure of researchers active in both spheres. Using an instrumental-variables approach based on predicted link formation from exogenous dyadic characteristics, and incorporating community fixed effects to address endogenous network formation, we show that both authors' and inventors' outputs rise with their network centrality, consistent with the theory. Moreover, scientific productivity significantly enhances technological productivity, while technological output does not exert a detectable reciprocal effect on scientific production, highlighting an asymmetric linkage aligned with a science-driven model of innovation. These findings provide the first empirical evidence on the joint dynamics of scientific and inventive peer effects, underscore the micro-foundations of the co-evolution of science and technology, and reveal how collaboration structures can be leveraged to design policies that enhance collective knowledge creation and downstream innovation. |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2602.02403 |
| By: | Feltham, Eric Martin (Columbia University); Christakis, Nicholas |
| Abstract: | Homophily, the tendency for individuals to associate with similar others, has long been treated as a central principle of social organization. Yet people may overestimate its importance in reasoning about their social networks. Here, we investigate individuals’ cognitive expectations of homophily and compare these expectations to actual homophily among 10, 072 adults in 82 isolated Honduras villages. We elicited subjects’ beliefs about whether pairs of people in their village social networks were socially tied. We show that people deploy cognitive heuristics that substantially overestimate homophily, including based on wealth, ethnicity, gender, and religion. We also find that people exploit network structure when predicting ties between others, independent of expectations about homophily. Understanding cognitive homophily has implications for models of network formation, interventions targeting social behavior and information diffusion, and the maintenance of social inequality. |
| Date: | 2026–01–23 |
| URL: | https://d.repec.org/n?u=RePEc:osf:socarx:z4nyq_v2 |
| By: | Heather N. Fogarty; Sooie-Hoe Loke; Nicholas F. Marshall; Enrique A. Thomann |
| Abstract: | This paper studies decentralized risk-sharing on networks. In particular, we consider a model where agents are nodes in a given network structure. Agents directly connected by edges in the network are referred to as friends. We study actuarially fair risk-sharing under the assumption that only friends can share risk, and we characterize the optimal signed linear risk-sharing rule in this network setting. Subsequently, we consider a special case of this model where all the friends of an agent take on an equal share of the agent's risk, and establish a connection to the graph Laplacian. Our results are illustrated with several examples. |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2602.05155 |
| By: | Soria, Chris; Feehan, Dennis |
| Abstract: | Personal networks influence health and mortality at the individual level, but less is known about how population-scale social network structure relates to mortality. This study examines how US county-level social network structure relates to mortality disparities. Using measures from 21 billion Facebook friendships, we investigate how two structural features of population social networks – cohesiveness and diversity – are associated with age-standardized and age-specific mortality rates. Bivariate results show that measures of social network structure rival smoking rates, median income, and educational attainment in their association with mortality rates. Social network structure remains predictive of mortality even after controlling for traditional measures like socioeconomic status and rural/urban classification. Network diversity is associated with lower mortality in both bivariate and multivariate analyses. Network clustering is associated with higher mortality bivariately, but this association reverses after controlling for county-level demographic and socioeconomic factors, revealing a protective effect masked by confounding. Age-stratified analyses further complicate this picture, showing that clustering predicts lower mortality among adults aged 15-64 but higher mortality among those 70 and older. These findings highlight social network structure as an important dimension of place-based health disparities, one not fully captured by conventional measures of socioeconomic composition or spatial segregation. |
| Date: | 2026–01–21 |
| URL: | https://d.repec.org/n?u=RePEc:osf:socarx:kvmx6_v3 |
| By: | Andrei Zeleneev |
| Abstract: | Homophily based on observables is widespread in networks. Therefore, homophily based on unobservables (fixed effects) is also likely to be an important determinant of the interaction outcomes. Failing to properly account for latent homophily (and other complex forms of unobserved heterogeneity) can result in inconsistent estimators and misleading policy implications. To address this concern, we consider a network model with nonparametric unobserved heterogeneity, leaving the role of the fixed effects unspecified. We argue that the interaction outcomes can be used to identify agents with the same values of the fixed effects. The variation in the observed characteristics of such agents allows us to identify the effects of the covariates, while controlling for the fixed effects. Building on these ideas, we construct several estimators of the parameters of interest and characterize their large sample properties. Numerical experiments illustrate the usefulness of the suggested approaches and support the asymptotic theory. |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2602.06885 |
| By: | Ilias Aarab; Thomas Gottron; Andrea Colombo; J\"org Reddig; Annalauro Ianiro |
| Abstract: | Micro-structural models of contagion and systemic risk emphasize that shock propagation is inherently multi-channel, spanning counterparty exposures, short-term funding and roll-over risk, securities cross-holdings, and common-asset (fire-sale) spillovers. Empirical implementations, however, often rely on stylized or simulated networks, or focus on a single exposure dimension, reflecting the practical difficulty of reconciling heterogeneous granular collections into a coherent representation with consistent identifiers and consolidation rules. We close part of this gap by constructing an empirically grounded multilayer network for euro area significant banking groups that integrates several supervisory and statistical datasets into layer-consistent exposure matrices defined on a common node set. Each layer corresponds to a distinct transmission channel, long- and short-term credit, securities cross-holdings, short-term secured funding, and overlapping external portfolios, and nodes are enriched with balance-sheet information to support model calibration. We document pronounced cross-layer heterogeneity in connectivity and centrality, and show that an aggregated (flattened) representation can mask economically relevant structure and misidentify the institutions that are systemically important in specific markets. We then illustrate how the resulting network disciplines standard systemic-risk analytics by implementing a centrality-based propagation measure and a micro-structural agent-based framework on real exposures. The approach provides a data-grounded basis for layer-aware systemic-risk assessment and stress testing across multiple dimensions of the banking network. |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2602.10960 |
| By: | Seong-Young Kim (Rennes SB - Rennes School of Business); Phillip H Kim |
| Abstract: | We study how and why firms shift their interfirm network positions during the routinized regime of a mature high-technology industry. Firms seek benefits from network positions (structural holes or centrality) by forming alliances that move them into these positions and increase their innovation performance. However, during the routinized technology regime, inertia impedes such movements, leading firms a dilemma: whether to continue shifting between two network positions and determine if such shifts yield better outcomes. We analyzed firm network positioning behavior in the semiconductor industry from 1991-2007. Our findings indicate that firms shift toward more central positions, which, in turn, improves innovation performance. These results explain how firms actively shape their network strategy when external conditions discourage such shifts. |
| Keywords: | semiconductor industry, routinized technology regime, high-technology industries, innovation performance, interfirm network positioning |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-05410602 |
| By: | Joy Das Bairagya; Jonathan Newton; Sagar Chakraborty |
| Abstract: | We present a collaboration ring model -- a network of players playing the prisoner's dilemma game and collaborating among the nearest neighbours by forming coalitions. The microscopic stochastic updating of the players' strategies are driven by their innate nature of seeking selfish gains and shared intentionality. Cooperation emerges in such a structured population through non-equilibrium phase transitions driven by propensity of the players to collaborate and by the benefit that a cooperator generates. The robust results are qualitatively independent of number of neighbours and collaborators. |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2602.11601 |
| By: | Yi Xiang; Pascal Welke; Chengzhi Zhang; Jian Wang |
| Abstract: | Scientific novelty is a critical construct in bibliometrics and is commonly measured by aggregating pairwise distances between the knowledge units underlying a paper. While prior work has refined how such distances are computed, less attention has been paid to how dyadic relations are aggregated to characterize novelty at the paper level. We address this limitation by introducing a network-based indicator, Cognitive Traversal Distance (CTD). Conceptualizing the historical literature as a weighted knowledge network, CTD is defined as the length of the shortest path required to connect all knowledge units associated with a paper. CTD provides a paper-level novelty measure that reflects the minimal structural distance needed to integrate multiple knowledge units, moving beyond mean- or quantile-based aggregation of pairwise distances. Using 27 million biomedical publications indexed by OpenAlex and Medical Subject Headings (MeSH) as standardized knowledge units, we evaluate CTD against expert-based novelty benchmarks from F1000Prime-recommended papers and Nobel Prize-winning publications. CTD consistently outperforms conventional aggregation-based indicators. We further show that MeSH-based CTD is less sensitive to novelty driven by the emergence of entirely new conceptual labels, clarifying its scope relative to recent text-based measures. |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2602.06607 |
| By: | Philipp Jonas Kreutzer; Josef Taalbi |
| Abstract: | Collaboration is expected to play a central role in the transition to a bioeconomy - a central pillar of a green economy. Such collaboration is supposed to connect traditional biomass processing firms with diverse actors in fields where biomass ought to substitute existing or create novel products and processes. This study analyzes the network of technology collaborations among innovating firms in Sweden between 1970 and 2021. The results reveal generally positive associations between direct and indirect ties, with meaningful increases in innovation output for each additional direct collaboration partner. Relationships between brokerage positions and innovation output were statistically insignificant, and cognitive proximity - while following theoretical expectations - materially insignificant. These associations are mostly equal between actors heavily invested in the bioeconomy and those focusing on other innovation areas, indicating that these actors operate under largely similar mechanisms linking collaboration and subsequent innovation output. These results suggest that stimulating collaboration broadly - rather than attempting to optimize collaboration compositions - could result in higher number of significant Swedish innovations, for bioeconomy and other sectors alike. |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2602.05112 |