|
on Network Economics |
| By: | Michel Grabisch (Centre d'Economie de la Sorbonne, Université Paris 1 Panthéon-Sorbonne, Paris School of Economics); Antoine Mandel (Centre d'Economie de la Sorbonne, Université Paris 1 Panthéo-Sorbonne, Paris School of Economics, Climate Finance Alpha); Agnieszka Rusinowska (CNRS, Centre d'Economie de la Sorbonne, Université Paris 1 Panthéon-Sorbonne, Paris School of Economics) |
| Abstract: | We investigate algorithmic fairness in a model of network formation governed by recommendation algorithms. The model defines a Markov chain over network configurations, which converges towards a class of efficient networks where each agent maximizes its utility. In this setting, we measure the efficiency of a recommendation algorithm via the speed at which it reaches the recurrent class of efficient networks. We propose a micro-founded measure of fairness that coincides with the entropy of the invariant distribution associated to this Markov chain. We develop analytical and numerical methods for the computation of efficiency and fairness. We find a strong relationship between the structure of users' preferences and the properties of recommendation algorithms. In particular, we show that there is a trade-off between efficiency and fairness as the hierarchical recommendation algorithms that ensure fast convergence to efficient networks are also those that lead to high level of unfairness. We put forward a simple solution to this trade-off where the designer adapts the recommendation algorithm to the different phases of the network formation process |
| Keywords: | network formation; platform; link recommendation; algorithm; markov chain; efficiency; fairness |
| JEL: | D85 C65 D83 |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:mse:cesdoc:25001 |
| By: | Yukun Cheng; Xiaotie Deng; Yunxuan Ma |
| Abstract: | In the digital age, resources such as open-source software and publicly accessible databases form a crucial category of digital public goods, providing extensive benefits for Internet. This paper investigates networked public goods games involving heterogeneous players and convex costs, focusing on the characterization of Nash Equilibrium (NE). In these games, each player can choose her effort level, representing her contributions to public goods. Network structures are employed to model the interactions among participants. Each player's utility consists of a concave value component, influenced by the collective efforts of all players, and a convex cost component, determined solely by the individual's own effort. To the best of our knowledge, this study is the first to explore the networked public goods game with convex costs. Our research begins by examining welfare solutions aimed at maximizing social welfare and ensuring the convergence of pseudo-gradient ascent dynamics. We establish the presence of NE in this model and provide an in-depth analysis of the conditions under which NE is unique. We also delve into comparative statics, an essential tool in economics, to evaluate how slight modifications in the model--interpreted as monetary redistribution--affect player utilities. In addition, we analyze a particular scenario with a predefined game structure, illustrating the practical relevance of our theoretical insights. Overall, our research enhances the broader understanding of strategic interactions and structural dynamics in networked public goods games, with significant implications for policy design in internet economic and social networks. |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2502.01001 |
| By: | Yan Li; Carol Alexander; Michael Coulon; Istvan Kiss |
| Abstract: | This study investigates the inherently random structures of dry bulk shipping networks, often likened to a taxi service, and identifies the underlying trade dynamics that contribute to this randomness within individual cargo sub-networks. By analysing micro-level trade flow data from 2015 to 2023, we explore the evolution of dry commodity networks, including grain, coal, and iron ore, and suggest that the Giant Strongly Connected Components exhibit small-world phenomena, indicative of efficient bilateral trade. The significant heterogeneity of in-degree and out-degree within these sub-networks, primarily driven by importing ports, underscores the complexity of their dynamics. Our temporal analysis shows that while the Covid-19 pandemic profoundly impacted the coal network, the Ukraine conflict significantly altered the grain network, resulting in changes in community structures. Notably, grain sub-networks display periodic changes, suggesting distinct life cycles absent in coal and iron ore networks. These findings illustrate that the randomness in dry bulk shipping networks is a reflection of real-world trade dynamics, providing valuable insights for stakeholders in navigating and predicting network behaviours. |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2502.00877 |
| By: | Sadegh Shirani; Yuwei Luo; William Overman; Ruoxuan Xiong; Mohsen Bayati |
| Abstract: | In experimental settings with network interference, a unit's treatment can influence outcomes of other units, challenging both causal effect estimation and its validation. Classic validation approaches fail as outcomes are only observable under one treatment scenario and exhibit complex correlation patterns due to interference. To address these challenges, we introduce a new framework enabling cross-validation for counterfactual estimation. At its core is our distribution-preserving network bootstrap method -- a theoretically-grounded approach inspired by approximate message passing. This method creates multiple subpopulations while preserving the underlying distribution of network effects. We extend recent causal message-passing developments by incorporating heterogeneous unit-level characteristics and varying local interactions, ensuring reliable finite-sample performance through non-asymptotic analysis. We also develop and publicly release a comprehensive benchmark toolbox with diverse experimental environments, from networks of interacting AI agents to opinion formation in real-world communities and ride-sharing applications. These environments provide known ground truth values while maintaining realistic complexities, enabling systematic examination of causal inference methods. Extensive evaluation across these environments demonstrates our method's robustness to diverse forms of network interference. Our work provides researchers with both a practical estimation framework and a standardized platform for testing future methodological developments. |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2502.01106 |
| By: | David Dekker; Dimitirs Christopoulos; Heather McGregor |
| Abstract: | We explore a dynamic patent citation network model to explain the established link between network structure and technological improvement rate. This model, a type of survival model, posits that the *dynamic* network structure determines the *constant* improvement rate, requiring consistent structural reproduction over time. The model's hazard rate, the probability of a patent being cited, represents "knowledge production, " reflecting the output of new patents given existing ones. Analyzing hydrogen technology patents, we find distinct subdomain knowledge production rates, but consistent development across subdomains. "Distribution" patents show the lowest production rate, suggesting dominant "distribution" costs in $H_2$ pricing. Further modeling shows Katz-centrality predicts knowledge production, outperforming subdomain classification. Lower Katz centrality in "distribution" suggests inherent organizational differences in invention. Exploitative learning (within-subdomain citations) correlates with higher patenting opportunity costs, potentially explaining slower "distribution" development, as high investment needs may incentivize monopolization over knowledge sharing. |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2502.00797 |
| By: | Artur F. Tomeczek |
| Abstract: | Microsoft's acquisition of Activision Blizzard valued at $68.7 billion has drastically altered the landscape of the video game industry. At the time of the takeover, the intellectual properties of Activision Blizzard included World of Warcraft, Diablo, Hearthstone, StarCraft, Overwatch, Battle.net, Candy Crush Saga, and Call of Duty. This article aims to explore the patenting activity of Activision Blizzard between 2008 (the original merger) and 2023 (the Microsoft acquisition). Four IPC code co-occurrence networks (co-classification maps) are constructed and analyzed based on the patent data downloaded from the WIPO Patentscope database. International Patent Classification (IPC) codes are a language agnostic system for the classification of patents. When multiple IPC codes co-occur in a patent, it shows that the technologies are connected. These relationships can be used for patent mapping. The analysis identifies the prolific and bridging technologies of Activision Blizzard and explores its synergistic role as a subsidiary of Microsoft Corporation. |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2502.02259 |
| By: | Colcerasa, Francesco; Giammei, Lorenzo; Subioli, Francesca |
| Abstract: | Restoring the theoretical foundation of John Roemer’s conceptualization of inequality of opportunity (IOp), we introduce an innovative empirical approach to measure unfair inequalities through Bayesian networks. This methodology enhances our understanding of income inequality through structural learning algorithms, generating an IOp index and, most importantly, shedding light on the underlying income formation process. We demonstrate how this proposal relates to established measurement methods through simulated data, and provide an application to five European countries to illustrate the potential of Bayesian networks in the context of measuring inequality of opportunity. |
| Keywords: | inequality of opportunity; EU-SILC; Bayesian networks |
| JEL: | A13 C43 D63 I20 |
| Date: | 2025–02–05 |
| URL: | https://d.repec.org/n?u=RePEc:ehl:lserod:127182 |
| By: | Di Zhang |
| Abstract: | Triangular arbitrage is a profitable trading strategy in financial markets that exploits discrepancies in currency exchange rates. Traditional methods for detecting triangular arbitrage opportunities, such as exhaustive search algorithms and linear programming solvers, often suffer from high computational complexity and may miss potential opportunities in dynamic markets. In this paper, we propose a novel approach to triangular arbitrage detection using Graph Neural Networks (GNNs). By representing the currency exchange network as a graph, we leverage the powerful representation and learning capabilities of GNNs to identify profitable arbitrage opportunities more efficiently. Specifically, we formulate the triangular arbitrage problem as a graph-based optimization task and design a GNN architecture that captures the complex relationships between currencies and exchange rates. We introduce a relaxed loss function to enable more flexible learning and integrate Deep Q-Learning principles to optimize the expected returns. Our experiments on a synthetic dataset demonstrate that the proposed GNN-based method achieves a higher average yield with significantly reduced computational time compared to traditional methods. This work highlights the potential of using GNNs for solving optimization problems in finance and provides a promising approach for real-time arbitrage detection in dynamic financial markets. |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2502.03194 |
| By: | Amiti, Mary; Duprez, Cedric; Konings, Jozef; Van Reenen, John |
| Abstract: | Using firm-to-firm transactions, we show that starting to supply a ‘superstar’ firm (large domestic firms, exporters and multinationals) boosts productivity by 8% after three years. Placebos on starting relationships with smaller firms and novel identification strategies support a causal interpretation of “superstar spillovers”. Consistent with a model of technology transfer, we find bigger treatment effects from technology-intensive superstars and also falls in markups (in order to win superstar contracts). We also show that firms that start supplying superstar firms enjoy a ‘dating agency’ effect — an increase in the number of new buyers that is particularly strong within the superstar firm’s network. Taken together, the results suggest an important role for raising productivity through superstars’ supply chains regardless of multinational status. |
| Keywords: | FDI; productivity; spillovers |
| JEL: | F23 O30 F21 |
| Date: | 2024–11–01 |
| URL: | https://d.repec.org/n?u=RePEc:ehl:lserod:124676 |
| By: | Kubitza, Dennis Oliver; Weßling, Katarina |
| Abstract: | Transitions from school to further education, training, or work are among the most extensively researched topics in the social sciences. Success in such transitions is influenced by predictors operating at multiple levels, such as the individual, the institutional, or the regional level. These levels are intertwined, creating complex inter-dependencies in their influence on transitions. To unravel them, researchers typically apply (multilevel) regression techniques and focus on mediating and moderating relations between distinct predictors. Recent research demonstrates that machine learning techniques can uncover previously overlooked patterns among variables. To detect new patterns in transitions from school to vocational training, we apply artificial neural networks (ANNs) trained on survey data from the German National Educational Panel Study (NEPS) linked with regional data. For an accessible interpretation of complex patterns, we use explainable artificial intelligence (XAI) methods. We establish multiple non-linear interactions within and across levels, concluding that they have the potential to inspire new substantive research questions. We argue that adopting ANNs in the social sciences yields new insights into established relationships and makes complex patterns more accessible |
| Keywords: | school-to-work transitions, VET, machine learning, explainable artificial neuronal networks, SHAP values, rule extraction |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:esprep:310974 |
| By: | Yoshitaka Ogisu (Faculty of Economics, Konan University and Junior Research Fellow, Research Institute for Economics and Business Administration, Kobe University, JAPAN); Shoka Hayaki (Faculty of Economics, Kagawa University and Research Institute for Economics and Business Administration, Kobe University, JAPAN); Masahiko Shibamoto (Research Institute for Economics and Business Administration and Center for Computational Social Science, Kobe University, JAPAN) |
| Abstract: | Relationship lending refers to lending a close relationship between a bank and a borrower, which is expected to help reduce borrowing costs. However, the extent to which they are used is unclear. This measurement difficulty makes it challenging to evaluate its benefits accurately. This paper proposes a novel empirical framework to identify relationship lending in transaction data between banks and borrowers in a more objective manner by determining the set of significant ties from an ensemble of undirected and unweighted bipartite networks. Using the detected relationship lending between banks and borrowers, we estimate the magnitude of additional lending volumes based on relationship lending. From the financial data in Japan from 1977 to 2021, the usage of relationship lending is estimated to be over 50% throughout the sample period but has varied considerably over time. We find that the volume of relationship lending is 34% larger than that of transactional lending. Although the relative volume of relationship lending against transaction lending has been declining, the importance of relationship lending remains substantial in obtaining a larger volume of lending. |
| Keywords: | Relationship lending; Bank-borrower networks; Significant ties |
| JEL: | C81 G12 G21 L14 |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:kob:dpaper:dp2025-02 |
| By: | Antoni Estevadeordal (GU - Georgetown University [Washington], Institut Barcelona d’Estudis Internacionals); Gaston Nievas (PSE - Paris School of Economics - UP1 - Université Paris 1 Panthéon-Sorbonne - ENS-PSL - École normale supérieure - Paris - PSL - Université Paris Sciences et Lettres - EHESS - École des hautes études en sciences sociales - ENPC - École nationale des ponts et chaussées - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement, PJSE - Paris Jourdan Sciences Economiques - UP1 - Université Paris 1 Panthéon-Sorbonne - ENS-PSL - École normale supérieure - Paris - PSL - Université Paris Sciences et Lettres - EHESS - École des hautes études en sciences sociales - ENPC - École nationale des ponts et chaussées - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement) |
| Abstract: | We study the determinants of international cooperation and its effect on trade. We rely on a unique database of 31, 982 International Cooperation Agreements (ICAs) signed between 1945-2022 by 193 countries. Estimating bilateral gravity equations, we find that trade follows the flag: ICAs increase bilateral exports by around 1-3%, with stronger effects for South-South relations. We address potential endogeneity through panel approach and an instrumental variable that exploits the network structure of international relations. Further, using LPM we find that gravity forces explain country pairs entering an ICA. Importantly, we find that ICAs serve as stepping stones towards Regional Trade Agreements, confirming a previous step in Balassa (1961) theory of economic integration. Our results shed new light on the international relations-trade nexus and contribute to the current debate on friendshoring. |
| Keywords: | International cooperation agreements, International trade flows, Regional trade agreements, Gravity equation |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:hal:psewpa:halshs-04721902 |
| By: | Delgado-Téllez, Mar; Quintana, Javier; Santabárbara, Daniel |
| Abstract: | An increase of e100 per tonne in the EU carbon price reduces the carbon footprint but lowers GDP due to higher energy costs and carbon leakage. Using a dynamic multi-sector, multi-country model augmented with an energy block that includes endogenous renewable energy investment, we analyze the macroeconomic and emissions effects of a carbon price. Investment in renewable energy mitigates electricity price increases in the medium term, leading to a smaller GDP loss (up to -0.4%) and a larger emissions reduction (24%) in the EU. Neglecting renewable energy investment overestimates the negative economic impact. We also find that a Carbon Border Adjustment Mechanism (CBAM) reduces carbon leakage but slightly hurts GDP and inflation as the competitive gain is offset by the higher costs of imported intermediate inputs. JEL Classification: C6, H2, Q5 |
| Keywords: | carbon border adjustment, carbon pricing, production networks, renewable energy investment |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:ecb:ecbwps:20253020 |