nep-net New Economics Papers
on Network Economics
Issue of 2023‒08‒14
ten papers chosen by
Alfonso Rosa García, Universidad de Murcia

  1. Theory of networks and processes: A first foundation of process networks By Seidlmeier, Heinrich
  2. Market Design for Dynamic Pricing and Pooling in Capacitated Networks By Saurabh Amin; Patrick Jaillet; Haripriya Pulyassary; Manxi Wu
  3. Social segregation, misperceptions, and emergent cyclical choice patterns By Mayerhoffer, Daniel; Schulz-Gebhard, Jan
  4. Neural networks can detect model-free static arbitrage strategies By Ariel Neufeld; Julian Sester
  5. Transaction Fraud Detection via Spatial-Temporal-Aware Graph Transformer By Yue Tian; Guanjun Liu
  6. Top wealth and its historical origins: An analysis of Germany's largest privately held fortunes in 2019 By Tisch, Daria; Ischinsky, Emma
  7. Analysis of the Dynamics of Social Interactions in an Associative Society: A Case Study in Pedagung Village, Bantarbolang District By Hasanah, Hilmatul
  8. Universities that matter for regional knowledge base renewal - the role of multilevel embeddedness By Nils Grashof; Holger Graf
  9. Policy Influence and Influencers Online and Off By Kotkaniemi, Anniina; Ylä-Anttila, Tuomas; Chen, Ted Hsuan Yun
  10. Contagious McKean–Vlasov systems with heterogeneous impact and exposure By Sojmark, Andreas; Feinstein, Zachary

  1. By: Seidlmeier, Heinrich
    Abstract: The impulse to think about process-induced social networks (in short: process networks) comes from the discipline of "Process Mining" (e.g. van der Aalst et al. 2005). The relevant literature refers to an "organizational view" or "organizational mining" in process mining. In essence, process mining is about creating a time-logical chain of related tasks from automatically logged user activities on a computer. In this way, real processes can be mapped and analyzed as models. As a "by-product", task-related social networks are created between the process participants through the predecessor/successor relationships in the workflow. However, it should be noted and criticized that process mining research neglects the potential of social network analysis. The extensive findings of classical network research are not taken up further. An organizational and social scientific deepening of the data-driven preliminary work is missing in this discipline. Process mining, which tends to be mathematical and technical, has not yet developed the ambition to ally itself with empirical organizational and social research. This working paper tries to counteract this. It presents a first, social science-based approach to theoretically grounding process networks.
    Keywords: Business Process, Process Management, Social Network, Process Theory
    Date: 2023
  2. By: Saurabh Amin; Patrick Jaillet; Haripriya Pulyassary; Manxi Wu
    Abstract: We study a market mechanism that sets edge prices to incentivize strategic agents to organize trips that efficiently share limited network capacity. This market allows agents to form groups to share trips, make decisions on departure times and route choices, and make payments to cover edge prices and other costs. We develop a new approach to analyze the existence and computation of market equilibrium, building on theories of combinatorial auctions and dynamic network flows. Our approach tackles the challenges in market equilibrium characterization arising from: (a) integer and network constraints on the dynamic flow of trips in sharing limited edge capacity; (b) heterogeneous and private preferences of strategic agents. We provide sufficient conditions on the network topology and agents' preferences that ensure the existence and polynomial-time computation of market equilibrium. We identify a particular market equilibrium that achieves maximum utilities for all agents, and is equivalent to the outcome of the classical Vickery Clark Grove mechanism. Finally, we extend our results to general networks with multiple populations and apply them to compute dynamic tolls for efficient carpooling in San Francisco Bay Area.
    Date: 2023–07
  3. By: Mayerhoffer, Daniel; Schulz-Gebhard, Jan
    Abstract: This paper examines the puzzle of why economic inequality has not resulted in political countermeasures to mitigate it, and proposes that the reason is due to misperceptions of economic inequality caused by segregation in social networks. We model taxation and voting behavior with an exponential income distribution and a Random Geometric Graph-type model to represent homophily, which leads to people perceiving their own income rank and income to be close to the middle. We find that people base their beliefs about mean income on a compound of the true mean and their local perception in the network, and that higher homophily causes lower implemented tax rates, which explains why redistribution preferences appear decoupled from actual inequality. In a dynamic extension, we also demonstrate that a rich set of dynamic behaviours can emerge from rational updating beliefs about efficiency. Misperceptions not only decrease redistribution in a static setting, they also hinder agents from adapting and learning towards the unbiased tax rate in a dynamic sense. As policy implications, we suggest two measures to counteract this: educating people about the actual income distribution and promoting diversity to reduce homophily.
    Keywords: Inequality, redistribution, perception, bias, networks
    Date: 2023
  4. By: Ariel Neufeld; Julian Sester
    Abstract: In this paper we demonstrate both theoretically as well as numerically that neural networks can detect model-free static arbitrage opportunities whenever the market admits some. Due to the use of neural networks, our method can be applied to financial markets with a high number of traded securities and ensures almost immediate execution of the corresponding trading strategies. To demonstrate its tractability, effectiveness, and robustness we provide examples using real financial data. From a technical point of view, we prove that a single neural network can approximately solve a class of convex semi-infinite programs, which is the key result in order to derive our theoretical results that neural networks can detect model-free static arbitrage strategies whenever the financial market admits such opportunities.
    Date: 2023–06
  5. By: Yue Tian; Guanjun Liu
    Abstract: How to obtain informative representations of transactions and then perform the identification of fraudulent transactions is a crucial part of ensuring financial security. Recent studies apply Graph Neural Networks (GNNs) to the transaction fraud detection problem. Nevertheless, they encounter challenges in effectively learning spatial-temporal information due to structural limitations. Moreover, few prior GNN-based detectors have recognized the significance of incorporating global information, which encompasses similar behavioral patterns and offers valuable insights for discriminative representation learning. Therefore, we propose a novel heterogeneous graph neural network called Spatial-Temporal-Aware Graph Transformer (STA-GT) for transaction fraud detection problems. Specifically, we design a temporal encoding strategy to capture temporal dependencies and incorporate it into the graph neural network framework, enhancing spatial-temporal information modeling and improving expressive ability. Furthermore, we introduce a transformer module to learn local and global information. Pairwise node-node interactions overcome the limitation of the GNN structure and build up the interactions with the target node and long-distance ones. Experimental results on two financial datasets compared to general GNN models and GNN-based fraud detectors demonstrate that our proposed method STA-GT is effective on the transaction fraud detection task.
    Date: 2023–07
  6. By: Tisch, Daria; Ischinsky, Emma
    Abstract: Rising wealth inequality is both a topic in recent policy discussion and in the social sciences. Despite the general interest in wealth concentration, we know only little about the largest privately held fortunes. To help fill this gap we analyze the historical origins of Germany's 1, 032 largest fortunes in 2019. In particular, we identify the share of entrenched fortunes - fortunes which date back to the beginning of the twentieth century - and ask to what extent they differ from more recently established ones. Furthermore, we examine in an exploratory way if entrenched fortunes are connected to fortunes with more recent origins through family lines. We use a journalistic rich list published by the manager magazin in 2019, which we link with both rich lists from 1912/1914 and Wikidata. We find that about eight percent of today's fortunes can be traced back to fortunes held by the same families in 1913. Regression analyses show that entrenched fortunes rank on average higher on the rich list than the remaining ones. Descriptive network analyses indicate that some of today's largest fortunes are intertwined through marital lines, hinting at social closure at the top. Our findings indicate that the accumulation and perpetuation of fortunes over many generations is an important feature of top wealth in Germany.
    Keywords: elite, family, inheritance, network analysis, super-rich, wealth perpetuation, Elite, Erbschaft, Familie, Netzwerkanalyse, Reichtum, Vermögen
    Date: 2023
  7. By: Hasanah, Hilmatul
    Abstract: This research examines the dynamics of social interaction in a community association located in Pedagung Village, Bantarbolang Subdistrict, Pemalang Regency. Data researcher collected through participant observation, in-depth interviews, and documentation. The results showed that social interaction in the community association of Pedagung village consists of various types of social interaction, such as cooperation, assimilation, and acculturation. This social interaction is well-established and sustainable because it is based on actual needs, effectiveness, efficiency, and self-adjustment to the truth. Furthermore, social interaction in the associative society of Pedagung village affects the social dynamics of the community by creating solidarity and cohesion.
    Date: 2023–06–16
  8. By: Nils Grashof (Friedrich Schiller University Jena, School of Economics and Business Administration); Holger Graf (Friedrich Schiller University Jena, School of Economics and Business Administration)
    Abstract: We analyze the role of universities or, more generally higher education institutions (HEIs), in terms of their regional and international embeddedness for regional knowledge base renewal. We assume that the introduction of radical patents in the sense of novel technological combinations contributes to the renewal of the knowledge base. For our empirical study, we combine information from patent applications, scientific publications and higher education statistics. We find that HEIs contribute most to knowledge base renewal if they have a strong research output and are locally embedded. International research embeddedness of HEIs benefits regional development only if combined with a central position in the regional network.
    Keywords: higher education institutions, universities, knowledge base renewal, radical innovation, SNA, embeddedness
    JEL: I20 I23 I25 O3 R11
    Date: 2023–07–20
  9. By: Kotkaniemi, Anniina; Ylä-Anttila, Tuomas; Chen, Ted Hsuan Yun
    Abstract: Social media is an important arena of contestation for policy actors. Yet, little research has explored the relationship between policy actors’ behaviour online and offline. In this study, we focus on actor influence, a key aspect of policy systems, by exploring four types of policy influence. We ask 1) are actors influential in policy-making central in social media networks? and 2) are they able to shape the structure of policy communication on social media? Using exponential random graph models on survey and Twitter data from the Finnish climate policy domain, we find that reputationally influential actors in offline policy-making are also central online, but the pattern does not hold for those with offline formal-institutional influence. Further, offline influence does not translate to being an online influencer; actors influential offline do not shape the structure of the Twitter network. Our results suggest that online influence is partially distinct from influence offline.
    Date: 2023–07–05
  10. By: Sojmark, Andreas; Feinstein, Zachary
    Abstract: We introduce a particular heterogeneous formulation of a class of contagious McKean–Vlasov systems, whose inherent heterogeneity comes from asymmetric interactions with a natural and highly tractable structure. It is shown that this formulation characterises the limit points of a finite particle system, deriving from a balance-sheet-based model of solvency contagion in interbank markets, where banks have heterogeneous exposure to and impact on the distress within the system. We also provide a simple result on global uniqueness for the full problem with common noise under a smallness condition on the strength of interactions, and we show that in the problem without common noise, there is a unique differentiable solution up to an explosion time. Finally, we discuss an intuitive and consistent way of specifying how the system should jump to resolve an instability when the contagious pressures become too large. This is known to happen even in the homogeneous version of the problem, where jumps are specified by a ‘physical’ notion of solution, but no such notion currently exists for a heterogeneous formulation of the system.
    Keywords: mean-field limit; contagion; heterogeneous network; default cascades; dynamic interbank model; systemic risk; Springer deal
    JEL: G21 G32
    Date: 2023–06–26

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