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on Network Economics |
| By: | Yang Sun; Wei Zhao; Junjie Zhou |
| Abstract: | We study dynamic network formation from a centralized perspective. In each period, the social planner builds a single link to connect previously unlinked pairs. The social planner is forward-looking, with instantaneous utility monotonic in the aggregate number of walks of various lengths. We show that, forming a nested split graph at each period is optimal, regardless of the discount function. When the social planner is sufficiently myopic, it is optimal to form a quasi-complete graph at each period, which is unique up to permutation. This finding provides a micro-foundation for the quasi-complete graph, as it is formed under a greedy policy. We also investigate the robustness of these findings under non-linear best response functions and weighted networks. |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2409.14136 |
| By: | W. Ahmed; D. Önkal; R. Das (Audencia Business School); S. Krishnan; F. Olan; M. Mariann Hardey; A. Alex Fenton |
| Abstract: | Given the role of technology and social media during the COVID-19 pandemic, the aim of this paper is to conduct a social network analysis of four COVID-19 conspiracy theories that were spread during the pandemic between March to June 2020. Specifically, the paper examines the 5G, Film Your Hospital, Expose Bill Gates, and the Plandemic conspiracy theories. Identifying disinformation campaigns on social media and studying their tactics and composition is an essential step toward counteracting such campaigns. The current study draws upon data from the Twitter Search API and uses social network analysis to examine patterns of disinformation that may be shared across social networks with sabotaging ramifications. The findings are used to generate the Framework of Disinformation Seeding and Information Diffusion for understanding disinformation and the ideological nature of conspiracy networks that can support and inform future pandemic preparedness and counteracting disinformation. Furthermore, a Digital Mindfulness Toolbox (DigiAware) is developed to support individuals and organisations with their information management and decision-making both in times of crisis and as strategic tools for potential crisis preparation. |
| Keywords: | COVID-19, Misinformation, Fake news, Twitter, Data Analytics, Mindfulness |
| Date: | 2023–05 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-04693779 |
| By: | Daniele Condorelli; Massimiliano Furlan |
| Abstract: | We train two neural networks adversarially to play normal-form games. At each iteration, a row and column network take a new randomly generated game and output individual mixed strategies. The parameters of each network are independently updated via stochastic gradient descent to minimize expected regret given the opponent's strategy. Our simulations demonstrate that the joint behavior of the networks converges to strategies close to Nash equilibria in almost all games. For all $2 \times 2$ and in 80% of $3 \times 3$ games with multiple equilibria, the networks select the risk-dominant equilibrium. Our results show how Nash equilibrium emerges from learning across heterogeneous games. |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2409.15197 |
| By: | Alvaro Silva (Federal Reserve Bank of Boston) |
| Abstract: | This paper studies inflation in small open economies with production networks. I show that the production network alters the elasticity of the consumer price index (CPI) to changes in sectoral technology, factor prices, and import prices. Sectors can import and export directly but also indirectly through domestic intermediate inputs. Indirect exporting dampens the inflationary pressure from domestic forces, while indirect importing increases the inflation sensitivity to import price changes. Computing these CPI elasticities requires knowledge of the production network structure as these do not coincide with typical sufficient statistics used in the literature, such as sectoral sales-to-GDP ratios, factor shares, or imported consumption shares. Using input-output tables, I provide empirical evidence that adjusting CPI elasticities for indirect exports and imports matters quantitatively for small open economies. I use the model to illustrate the importance of production networks during the recent COVID-19 inflation in Chile and the United Kingdom. |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2410.00705 |
| By: | Bingyao Liu; Iris Li; Jianhua Yao; Yuan Chen; Guanming Huang; Jiajing Wang |
| Abstract: | This paper takes the graph neural network as the technical framework, integrates the intrinsic connections between enterprise financial indicators, and proposes a model for enterprise credit risk assessment. The main research work includes: Firstly, based on the experience of predecessors, we selected 29 enterprise financial data indicators, abstracted each indicator as a vertex, deeply analyzed the relationships between the indicators, constructed a similarity matrix of indicators, and used the maximum spanning tree algorithm to achieve the graph structure mapping of enterprises; secondly, in the representation learning phase of the mapped graph, a graph neural network model was built to obtain its embedded representation. The feature vector of each node was expanded to 32 dimensions, and three GraphSAGE operations were performed on the graph, with the results pooled using the Pool operation, and the final output of three feature vectors was averaged to obtain the graph's embedded representation; finally, a classifier was constructed using a two-layer fully connected network to complete the prediction task. Experimental results on real enterprise data show that the model proposed in this paper can well complete the multi-level credit level estimation of enterprises. Furthermore, the tree-structured graph mapping deeply portrays the intrinsic connections of various indicator data of the company, and according to the ROC and other evaluation criteria, the model's classification effect is significant and has good "robustness". |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2409.17909 |
| By: | Christian Bontemps (ENAC-LAB - Laboratoire de recherche ENAC - ENAC - Ecole Nationale de l'Aviation Civile, TSE-R - Toulouse School of Economics - UT Capitole - Université Toulouse Capitole - UT - Université de Toulouse - EHESS - École des hautes études en sciences sociales - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement); Cristina Gualdani (QMUL - Queen Mary University of London); Kevin Remmy (Universität Mannheim) |
| Abstract: | We develop a two-stage game in which competing airlines first choose the networks of markets to serve in the first stage before competing in price in the second stage. Spillovers in entry decisions across markets are allowed, which accrue on the demand, marginal cost, and fixed cost sides. We show that the second-stage parameters are point identified, and we design a tractable procedure to set identify the first-stage parameters and to conduct inference. Further, we estimate the model using data from the domestic US airline market and find significant spillovers in entry. In a counterfactual exercise, we evaluate the 2013 merger between American Airlines and US Airways. Our results highlight that spillovers in entry and post-merger network readjustments play an important role in shaping post-merger outcomes. |
| Keywords: | Endogenous market structure, Networks, Airlines, Oligopoly, Product repositioning, Mergers, Remedies |
| Date: | 2023–06–14 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-04709707 |
| By: | Qian Hui; Tiandong Wang |
| Abstract: | In financial markets marked by inherent volatility, extreme events can result in substantial investor losses. This paper proposes a portfolio strategy designed to mitigate extremal risks. By applying extreme value theory, we evaluate the extremal dependence between stocks and develop a network model reflecting these dependencies. We use a threshold-based approach to construct this complex network and analyze its structural properties. To improve risk diversification, we utilize the concept of the maximum independent set from graph theory to develop suitable portfolio strategies. Since finding the maximum independent set in a given graph is NP-hard, we further partition the network using either sector-based or community-based approaches. Additionally, we use value at risk and expected shortfall as specific risk measures and compare the performance of the proposed portfolios with that of the market portfolio. |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2409.12208 |
| By: | Gustavo de Freitas Fonseca; Lucas Coelho e Silva; Paulo Andr\'e Lima de Castro |
| Abstract: | In Reinforcement Learning (RL), multi-armed Bandit (MAB) problems have found applications across diverse domains such as recommender systems, healthcare, and finance. Traditional MAB algorithms typically assume stationary reward distributions, which limits their effectiveness in real-world scenarios characterized by non-stationary dynamics. This paper addresses this limitation by introducing and evaluating novel Bandit algorithms designed for non-stationary environments. First, we present the \textit{Adaptive Discounted Thompson Sampling} (ADTS) algorithm, which enhances adaptability through relaxed discounting and sliding window mechanisms to better respond to changes in reward distributions. We then extend this approach to the Portfolio Optimization problem by introducing the \textit{Combinatorial Adaptive Discounted Thompson Sampling} (CADTS) algorithm, which addresses computational challenges within Combinatorial Bandits and improves dynamic asset allocation. Additionally, we propose a novel architecture called Bandit Networks, which integrates the outputs of ADTS and CADTS, thereby mitigating computational limitations in stock selection. Through extensive experiments using real financial market data, we demonstrate the potential of these algorithms and architectures in adapting to dynamic environments and optimizing decision-making processes. For instance, the proposed bandit network instances present superior performance when compared to classic portfolio optimization approaches, such as capital asset pricing model, equal weights, risk parity, and Markovitz, with the best network presenting an out-of-sample Sharpe Ratio 20\% higher than the best performing classical model. |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2410.04217 |
| By: | César Ducruet (CNRS - Centre National de la Recherche Scientifique, EconomiX - EconomiX - UPN - Université Paris Nanterre - CNRS - Centre National de la Recherche Scientifique); In Joo Yoon (KMI - Korea Maritime Institute - Korea Maritime Institute) |
| Abstract: | The North Korean economy is experiencing a deepening economic and political crisis since the early 1990s. Although North Korea is not commonly seen as a shipping nation, its major cities are coastal, and it hosts nine international trading ports. However, little is known about the role of maritime transport in its development. This article uses vessel movement data to reconstitute the maritime network linking North Korean ports and other ports, over the period 1977-2021. Besides the drastic connectivity loss, main results conclude about a limited role of maritime transport in economic development, except for its participation to China's increasing grip on North Korea. This research brings new knowledge about North Korea and contributes to advance maritime network studies in general. |
| Keywords: | multivariate analysis, international trade, maritime connectivity, network analysis |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-04689246 |
| By: | Orhan, Mehmet A. (EM Normandie Business School); van Rossenberg, Yvonne; Bal, P. Matthijs |
| Abstract: | Ideally, the academic publication process should be meritocratic, fair, and open to diverse groups of researchers. Yet, many scholarly disciplines are far from this ideal. To investigate the extent and nature of overrepresentation in management and organizational research, we examined 60-year publication trends in three closely related subfields: Management (MNGT), Human Resource Management (HRM), and Industrial-Organizational Psychology (IOP). Analyzing over 60, 000 publications from 42 top-tier journals, our study reveals an increasing trend in authorship inequalities and a growing dominance of the scientific elite. Individual-level analyses, along with journal and field-level comparisons, show that a select group of researchers has become more influential over time, leading to rising disparities in authorship. Field-level comparisons also show that the most productive IOP researchers publish significantly more articles than those in other fields. Besides rising numbers of publications, the super-elite of IOP are found to dominate more journals, as evidenced by a higher frequency of the same authors appearing on the top-10 most productive list in IOP than in the other two fields. Through network analyses, we revealed that IOP consistently shows a large giant component, indicating that a large portion of IOP authors is part of the “same connected network, ” reflecting a highly collaborative field where even smaller groups are connected to the broader network. We recommend future advancements in theory, practice, and policy to address these inequalities and promote a more inclusive and equitable research environment. |
| Date: | 2024–10–01 |
| URL: | https://d.repec.org/n?u=RePEc:osf:osfxxx:tzx92 |
| By: | Monica Plechero (Dept. of Management, Venice School of Management, Università Ca' Foscari Venice); Erica Santini (Dept. of Economics and Management, Università di Trento); Giancarlo Coro' (Dept. of Economics, Università Ca' Foscari Venice) |
| Abstract: | How do small and medium-sized enterprises (SMEs) set up their technological portfolio, orient their future technological choices, and contribute to shaping the evolution of a specific economic structure? Technology adoption in SMEs has been recognized as a learning path but how this process is characterized in the digital era remains rather unclear. The paper aims to address this research gap by analysing how a population of manufacturing SMEs builds and follows its technological trajectories. By taking advantage of data on a large sample of manufacturing firms, we employ network analysis to map the evolution in the adoption of digital technologies. Findings show a common path of adoption and learning within the population of manufacturing SMEs. However, while some firms are on the edge, riding the learning curve and providing key meaning to the contextual setting of operations and strategies commonly taken under the technological evolution, others lag in their learning process, risking digital devices. |
| Keywords: | SMEs, technological trajectory, manufacturing, digital era |
| Date: | 2023–09 |
| URL: | https://d.repec.org/n?u=RePEc:vnm:wpdman:208 |
| By: | Gabriele Camera (Economic Science Institute, Chapman University, One University Dr., Orange, CA 92866); Gary Charness (University of California, Santa Barbara, Department of Economics, North Hall 3032 , Santa Barbara, CA 93106); Nir Chemaya (University of California, Santa Barbara, Department of Economics, North Hall 2049, Santa Barbara, CA 93106) |
| Abstract: | We study trading networks where the apportioning of participants’ payments flows, or, validation, relies on one of two governance architectures: centralized, validation authority is concentrated in a single participant, or decentralized, authority is distributed among all participants. Both architectures support multiple Pareto-ranked equilibria, with and without failure to properly apportion payments. In the experiment, decentralization never promoted validation failures, and in fact discouraged them—boosting trading activity—when we introduced pre-play communication, a natural feature of a trading environment. This governance advantage shows that there is scope for decentralization in innovating monetary and financial networks. |
| Keywords: | communication, digital currencies, group decision-making, payments systems. |
| JEL: | D81 |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:net:wpaper:2407 |
| By: | Eugene Dettaa; Endong Wang |
| Abstract: | This paper presents a Wald test for multi-horizon Granger causality within a high-dimensional sparse Vector Autoregression (VAR) framework. The null hypothesis focuses on the causal coefficients of interest in a local projection (LP) at a given horizon. Nevertheless, the post-double-selection method on LP may not be applicable in this context, as a sparse VAR model does not necessarily imply a sparse LP for horizon h>1. To validate the proposed test, we develop two types of de-biased estimators for the causal coefficients of interest, both relying on first-step machine learning estimators of the VAR slope parameters. The first estimator is derived from the Least Squares method, while the second is obtained through a two-stage approach that offers potential efficiency gains. We further derive heteroskedasticity- and autocorrelation-consistent (HAC) inference for each estimator. Additionally, we propose a robust inference method for the two-stage estimator, eliminating the need to correct for serial correlation in the projection residuals. Monte Carlo simulations show that the two-stage estimator with robust inference outperforms the Least Squares method in terms of the Wald test size, particularly for longer projection horizons. We apply our methodology to analyze the interconnectedness of policy-related economic uncertainty among a large set of countries in both the short and long run. Specifically, we construct a causal network to visualize how economic uncertainty spreads across countries over time. Our empirical findings reveal, among other insights, that in the short run (1 and 3 months), the U.S. influences China, while in the long run (9 and 12 months), China influences the U.S. Identifying these connections can help anticipate a country's potential vulnerabilities and propose proactive solutions to mitigate the transmission of economic uncertainty. |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2410.04330 |