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on Network Economics |
| By: | Rhys Murrian (Department of Economics and SoDa Labs, Monash University); Paul A. Raschky (Department of Economics and SoDa Labs, Monash University); Klaus Ackermann (Department of Econometrics and Business Statistics and SoDa Labs, Monash University) |
| Abstract: | This paper empirically investigates how an individual’s network influences their purchase and subsequent use of experience goods. Utilising data on the network and game-ownership of over 108 million users from the world’s largest video game platform, we analyse whether a user’s friendship network influences their decision to purchase single-player video games. Our identification strategy uses an instrumental variable (IV) approach that employs the temporal lag of purchasing decisions from second degree friends. We find strong peer effects in the individual game adoption in the contemporary week. The effect is stronger if the friend who purchased the game is an old friend compared to a key player in the friendship network. Comparing the results to adoption decisions for a major label game, we find peer effects of a similar size and duration. However, the time subsequently spent playing the games is higher for players who were neither influenced by a peer who is a key player nor an old friend. Considering the increasing importance of online networks on consumption decisions, our findings offer some first insights on the heterogeneity of peer effects between old and key player friends and also provide evidence in consumers' biases in social learning. |
| Keywords: | networks, experience goods, product adoption, taste projection |
| JEL: | D12 Z13 |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:ajr:sodwps:2024-03 |
| By: | Shu Takahashi; Kento Yamamoto; Shumpei Kobayashi; Ryoma Kondo; Ryohei Hisano |
| Abstract: | The prediction of both the existence and weight of network links at future time points is essential as complex networks evolve over time. Traditional methods, such as vector autoregression and factor models, have been applied to small, dense networks, but become computationally impractical for large-scale, sparse, and complex networks. Some machine learning models address dynamic link prediction, but few address the simultaneous prediction of both link presence and weight. Therefore, we introduce a novel model that dynamically predicts link presence and weight by dividing the task into two sub-tasks: predicting remittance ratios and forecasting the total remittance volume. We use a self-attention mechanism that combines temporal-topological neighborhood features to predict remittance ratios and use a separate model to forecast the total remittance volume. We achieve the final prediction by multiplying the outputs of these models. We validated our approach using two real-world datasets: a cryptocurrency network and bank transfer network. |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2409.08718 |
| By: | Yifan Jia; Yanbin Wang; Jianguo Sun; Yiwei Liu; Zhang Sheng; Ye Tian |
| Abstract: | Ethereum faces growing fraud threats. Current fraud detection methods, whether employing graph neural networks or sequence models, fail to consider the semantic information and similarity patterns within transactions. Moreover, these approaches do not leverage the potential synergistic benefits of combining both types of models. To address these challenges, we propose TLMG4Eth that combines a transaction language model with graph-based methods to capture semantic, similarity, and structural features of transaction data in Ethereum. We first propose a transaction language model that converts numerical transaction data into meaningful transaction sentences, enabling the model to learn explicit transaction semantics. Then, we propose a transaction attribute similarity graph to learn transaction similarity information, enabling us to capture intuitive insights into transaction anomalies. Additionally, we construct an account interaction graph to capture the structural information of the account transaction network. We employ a deep multi-head attention network to fuse transaction semantic and similarity embeddings, and ultimately propose a joint training approach for the multi-head attention network and the account interaction graph to obtain the synergistic benefits of both. |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2409.07494 |
| By: | Mogens Fosgerau; Nikolaj Nielsen; Mads Paulsen; Thomas Kj{\ae}r Rasmussen; Rui Yao |
| Abstract: | This paper considers substitution patterns in the perturbed utility route choice model. We provide a general result that determines the marginal change in link flows following a marginal change in link costs across the network. We give a general condition on the network structure under which all paths are necessarily substitutes and an example in which some paths are complements. The presence of complementarity contradicts a result in a previous paper in this journal; we point out and correct the error. |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2409.08347 |
| By: | Margherita Comola (Université Paris-Saclay (RITM), 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 des Ponts ParisTech - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement); Agnieszka Rusinowska (CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique, 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 des Ponts ParisTech - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement); Marie Claire Villeval (GATE Lyon Saint-Étienne - Groupe d'Analyse et de Théorie Economique Lyon - Saint-Etienne - UL2 - Université Lumière - Lyon 2 - UJM - Université Jean Monnet - Saint-Étienne - EM - EMLyon Business School - CNRS - Centre National de la Recherche Scientifique, IZA - Forschungsinstitut zur Zukunft der Arbeit - Institute of Labor Economics) |
| Abstract: | We experimentally investigate how players with opposing views compete for influence through strategic targeting in networks. We varied the network structure, the relative influence of the opponent, and the heterogeneity of the nodes' initial opinions. Although most players adopted a best-response strategy based on their relative influence, we also observed behaviors deviating from this strategy, such as the tendency to target central nodes and avoid nodes targeted by the opponent. Targeting is also affected by affinity and opposition biases, the strength of which depends on the distribution of initial opinions. |
| Keywords: | Network, Influence, Targeting, Competition, Experiment |
| Date: | 2024–09–23 |
| URL: | https://d.repec.org/n?u=RePEc:hal:cesptp:hal-04706311 |
| By: | Sule Alan; Michela Carlana; Marinella Leone |
| Abstract: | We evaluate an intervention designed to increase teachers’ awareness of social isolation by providing them with their own students’ social network and information on developmental risks associated with social exclusion. Using friendship data and incentive-compatible measures of antisocial and prosocial behavior, we find that the intervention reduces social isolation and antisocial behavior without improving prosocial behavior. The reduction in antisocial behavior leads to better economic outcomes in treated classrooms, measured by average payoffs and the Gini coefficient. Our findings highlight the personal and communal benefits of alleviating social exclusion and antisocial peer relationships in schools. |
| JEL: | C93 I24 I28 |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:32954 |
| By: | Hsu, Wen-Yi |
| Abstract: | Structural hole theory can be used to explain the advantages of an individual or firms in the economic structure. From a network perspective, this study analyzes the relationship between local OTT operators in Taiwan, the relationship with large international OTTs, and the bridges or structural hole spanners. The researcher conducted semi-structured interviews with major players in Taiwan's OTT industry, and employs the grounded theory approach, systematically inducting and analyzing data. Based on the perspectives of local OTT operators describing the main competitors in the Taiwanese OTT market, the research identifies major international OTT, particularly Netflix, as the hub and structural hole spanner. This study maps 'Taiwan's OTT industry structural holes', and further explore whether Taiwan's local OTT operators are able to act as bridges or structural hole spanners. This research found that relationships among local Taiwanese players are loose, with most situated at the periphery of the industry network; Netflix serves as a network bridge. Netflix occupies the central position in Taiwan's OTT industry network as a structural hole spanner. Netflix benefits from being a structural hole spanner., while local Taiwanese players do not form mutually beneficial partnerships. The connection between local Taiwanese OTT players and international operator increasingly exhibits reinforced structural holes. The existence of structural holes in Taiwan's OTT industry and the possibility for local operators to gain advantages. This study also proposes types and analysis of structural holes in Taiwan's OTT industry and analysis of structural holes for local OTT operators in Taiwan. This underscores the limited key resources of local operators. Overall, there is no significant advantage for domestic operators. To remain competitive, local OTT operators must seek new opportunities within these structural holes |
| Keywords: | OTT, center-periphery theory, structural hole theory, qualitative structural analysis |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:itsb24:302473 |
| By: | Diegert, Paul; Jochmans, Koen |
| Abstract: | Consider dyadic random variables on units from a given population. It is common to assume that these variables are jointly exchangeable and dissociated. In this case they admit a non-separable specification with two-way unobserved heterogeneity. The analysis of this type of structure is of considerable interest but little is known about their nonparametric identifiability, especially when the unobserved heterogeneity is continuous. We provide conditions under which both the distribution of the observed random variables conditional on the unit-specific heterogeneity and the distribution of the unit-specific heterogeneity itself are uniquely recoverable from knowledge of the joint marginal distribution of the observable random variables alone without imposing parametric restrictions. |
| Keywords: | Exchangeability; conditional independence; dyadic data; network; two-way; heterogeneity |
| Date: | 2024–09–17 |
| URL: | https://d.repec.org/n?u=RePEc:tse:wpaper:129722 |