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on Network Economics |
| By: | Kohmei Makihara |
| Abstract: | We study how to allocate a good with positive externalities among agents in an information network, without monetary transfers. Each agent observes their own valuation and those of their neighbors. A principal seeks to allocate the good to the highest-valuation agent through a mechanism robust to agents' heterogeneous belief hierarchies, leveraging the network structure and the partial incentive alignment created by allocative externalities. We characterize the networks for which a robustly efficient mechanism exists: it exists if and only if at least two agents are connected to all others. Relaxing robustness to rule out weakly dominated strategies, one universally connected agent suffices. We generalize the results to a broader class of utility functions, show that robust welfare maximization is impossible unless the network is complete, and establish that an ex post incentive compatible mechanism that is efficient under truthful reports exists if and only if no agent is isolated. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.35907 |
| By: | Dongshuang Hou (School of Mathematics and Statistics, Northwestern Polytechnical University, Xi’an, China); Aymeric Lardon (Université Jean Monnet Saint-Etienne, Université Lumière Lyon 2, emlyon business school, GATE UMR 5824, F-42023 Saint-Etienne, France) |
| Abstract: | We introduce similarity Gaussian games, transferable-utility games in which the worth of a coalition is the total pairwise similarity among its members, measured by a Gaussian kernel. Similarity Gaussian games are convex weighted graph games, so the Shapley value is always a core allocation; it also has two complementary interpretations: it is weighted degree centrality in the associated similarity network and, up to a common factor, a Gaussian kernel density estimate evaluated at each player’s characteristic. We derive the Shapley value in closed form and study how it changes with the bandwidth. For large bandwidths, we establish an exact asymptotic affine relationship between the Shapley values of similarity Gaussian games and squared deviations games, providing a precise link between local similarity and dispersion. |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:gat:wpaper:2609 |
| By: | Mattia Marzi; Frank P. Pijpers; Diego Garlaschelli |
| Abstract: | Network reconstruction from partial information is usually performed at the same resolution level at which constraints are observable. This becomes problematic when only coarse-grained information is available, while the relevant process occurs at a finer scale. Here we employ the multiscale model of weighted networks introduced in a companion paper and turn it into a probabilistic framework for reconstructing weighted networks across arbitrary aggregation levels. The model is built to preserve its functional form under coarse-graining, so that global parameters calibrated on an observable aggregate layer can be transferred to finer layers without refitting. We test the method on two empirical systems with different aggregation mechanisms. In the International Trade Network, countries are aggregated into geographic macro-regions and the observed coarse-grained layer is used to infer the underlying country-level network. In the Dutch production network, sectoral flows are reconstructed across the hierarchical industrial classification, using coarser sectoral layers to infer finer ones. In both geographical and sectoral settings, we benchmark our genuinely multiscale reconstruction method against a state-of-the-art weighted reconstruction model calibrated directly at the target resolution, and therefore using additional information available at the same (finer) scale at which performance is evaluated. Remarkably, despite this informational disadvantage, our method recovers the fine-scale binary structure with high accuracy, improving precision, specificity, accuracy and maximum degree-error diagnostics, while remaining nearly equivalent in sensitivity. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.37957 |
| By: | Donghang Li; Yunhan Zheng; Alok Prakash; Shenhao Wang; Jinhua Zhao |
| Abstract: | Artificial intelligence capabilities are often measured as resources accumulated within firms, yet they also circulate across organizational boundaries through worker mobility. Drawing on a database of approximately 535 million employment records across 58 countries, we reconstruct the inter-firm network of AI talent flows over 2010--2022. We find that AI talent inflows are becoming increasingly concentrated among a small set of leading firms, with stronger preferential attachment than in general labor mobility. Yet the network core remains contestable: central positions in AI networks change hands far more often than in non-AI networks. Network position also carries information that goes beyond workforce size. This network position is economically consequential: AI network centrality is associated with higher enterprise value, after accounting for workforce scale, assets, and fixed effects. Event-study estimates further indicate that sharp improvements in network position are followed by higher enterprise value relative to matched firms with similar pre-event trajectories. Together, these findings suggest that corporate AI advantage depends not only on how much talent firms accumulate, but also on where they sit in its circulation. |
| Date: | 2026–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2610.08264 |
| By: | MohammadHossein Bateni; Zahra Hadizadeh; MohammadTaghi Hajiaghayi; Mahdi JafariRaviz; Shayan Taherijam |
| Abstract: | We study information aggregation in the networked learning model introduced by Kearns, Roth, and Ryu (SODA 2026). There is a fixed distribution over $d$ features and a common label. Agents learn in topological order on a directed acyclic graph. Each observes a subset of the features and its parents' predictions, fits a linear predictor to minimize mean squared error, and passes only its prediction forward. The global predictor is the best linear predictor using all features. Kearns, Roth, and Ryu show that the output agent's error approaches the global predictor's error along sufficiently deep paths with suitable feature coverage, while insufficient depth can prevent aggregation even in large networks. In contrast to their main focus on a given graph and feature allocation, we consider the limits of the model under two settings. In the adaptive designer setting, a designer chooses the graph, feature allocation, and output agent knowing the distribution. In the oblivious designer setting, the designer fixes all three before an adversary chooses the distribution. Each agent observes one feature and receives predictions from a limited number of parents. We call the aggregation exact when the output agent matches the global predictor exactly. For $d\ge3$, we show that no finite depth guarantees exact aggregation for every distribution with one parent per agent, even when the designer knows the distribution. In contrast, two parents per agent suffice for exact aggregation even in the oblivious designer setting. A fixed graph, feature allocation, and output agent achieve this for every distribution at depth $O(d\log d)$. Knowing the distribution reduces the depth to $O(d)$. Both constructions use $O(d^2)$ agents, with a very large constant for two parents. We show the bounds on the depth and number of agents are all optimal up to constant factors. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.35537 |
| By: | Rafael Gonçalves; Ina Hajdini |
| Abstract: | Relying on a micro-level dataset of US firms' inflation expectations, we document that firms in industries with larger Domar weights and more flexible prices have more accurate inflation forecasts. To explain these patterns, we develop a rational inattention model of price-setting firms within a production network. In equilibrium, firms' inflation forecast accuracy depends on their attention to marginal costs, the comovement between their marginal cost and aggregate inflation, and the change in the marginal cost. When calibrated to US input-output data, the model replicates the cross-sectoral relationship between forecast accuracy, Domar weights, and price flexibility. Quantitatively, we find that production networks endogenously increase nominal rigidity. This channel accounts for about 30 percent of the networks' attenuation of the inflation response to monetary shocks, deepening the standard flattening of the Phillips curve from input-output linkages. |
| Keywords: | inflation expectations; rational inattention; production networks; inflation dynamics; monetary policy |
| JEL: | E31 E32 E71 |
| Date: | 2026–09–21 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedcwq:103851 |
| By: | Bernard Herskovic; Gill Segal |
| Abstract: | Time fixed effects are not a general-equilibrium control. In network economies, aggregate shocks do not enter firm outcomes uniformly: they propagate through equilibrium prices, costs, and demand, leaving a residual network gradient after time demeaning. When an instrument, treatment, or exposure design even partially aligns with this gradient, standard panel estimators recover the direct effect plus an endogenous network-leakage component. We develop a tractable framework that signs and quantifies this Network-SUTVA bias in closed form, showing how network topology governs its magnitude. Weak instruments magnify the leakage. Difference-in-differences designs are similarly contaminated because controls can be untreated by assignment but treated in equilibrium. Calibrated to the U.S. production network, the distortion can exceed 130% of the true direct effect. We propose observed- and hidden-network diagnostics and remedies, and test them on canonical difference-in-differences designs, showing that standard estimators can confound direct policy responses with systemic network leakage. |
| JEL: | C23 D85 G30 L14 |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:35807 |
| By: | Miguel Risco (IGIER, Bocconi University, Milan, Italy) |
| Abstract: | Interoperability has been proposed to make social media contestable. But platforms are no longer purely network-based: personalization algorithms select much of what users see beyond their network, and an interoperability mandate opens the network but not the discovery layer. We study two platforms, each allocating resources between its network and discovery layers. A reciprocal mandate can raise the leading platform's size even if it is more productive in both, because its comparative advantage in discovery makes the challenger the network specialist, whose specialty the mandate opens. This effect can reverse once investment adjusts. Opening only the leader's network lowers its size, so any increase in concentration under a reciprocal mandate comes from opening the challenger's network. Concentration, challenger profit and user surplus can diverge. Whether recommendation technology, now largely driven by artificial intelligence, becomes a commodity or a moat determines whether the mandate reaches the source of the leader's advantage. |
| Keywords: | interoperability; social media; discovery; recommender systems; platform competition |
| JEL: | L13 L41 L51 L86 |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:net:wpaper:2605 |
| By: | Michael J. Andrews; Ryan R. Hill; Joseph Price; Riley Wilson |
| Abstract: | Occupation choice is a key determinant of economic mobility. This paper studies how childhood neighbors influence occupational choices and later-life earnings. Using linked historical U.S. census records for over six million boys and four million girls, we reconstruct neighborhood microgeography to identify childhood neighbors and their occupations. We find that living next door to someone in a given occupation increases the probability of working in that same occupation 30 years later by about 10 percent relative to other children on the same street. The transmission effect is higher with more intense exposure, in more connected neighborhoods, and when neighboring families are more similar. Exposure to high-income or highly educated neighbors has lasting economic effects, leading to significant gains in adult income and education even compared to other children from the same street. These findings suggest that neighborhood networks play an important role in shaping intergenerational economic mobility. |
| JEL: | J24 J62 N32 |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:35810 |