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on Network Economics |
| By: | Francis Bloch (Universite´ Paris 1 and Paris School of Economics); Bhaskar Dutta (Ashoka University); Marcin Dziubi´nski (Institute of Informatics, University of Warsaw) |
| Abstract: | We propose and study a model of strategic network design and exploration where the hider, subject to a budget constraint restricting the number of links, chooses a connected network and the location of an object. Meanwhile, the seeker, not observing the network and the location of the object, chooses a network exploration strategy starting at a fixed node in the network. The network exploration follows the expanding search paradigm of Alpern and Lidbetter (2013). We obtain a Nash equilibrium and characterize equilibrium payoffs in the case of linking budget allowing for trees only. We also give an upper bound on the expected number of steps needed to find the hider for the case where the linking budget allows for at most one cycle in the network. |
| Keywords: | Network exploration; networks; Strategic hiding |
| Date: | 2024–04–12 |
| URL: | http://d.repec.org/n?u=RePEc:ash:wpaper:112&r= |
| By: | Aristide Houndetoungan; Cristelle Kouame; Michael Vlassopoulos |
| Abstract: | Peer influence on effort devoted to some activity is often studied using proxy variables when actual effort is unobserved. For instance, in education, academic effort is often proxied by GPA. We propose an alternative approach that circumvents this approximation. Our framework distinguishes unobserved shocks to GPA that do not affect effort from preference shocks that do affect effort levels. We show that peer effects estimates obtained using our approach can differ significantly from classical estimates (where effort is approximated) if the network includes isolated students. Applying our approach to data on high school students in the United States, we find that peer effect estimates relying on GPA as a proxy for effort are 40% lower than those obtained using our approach. |
| Date: | 2024–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2405.06850&r= |
| By: | Ge, S.; Li, S.; Linton, O. B.; Liu, W.; Su, W. |
| Abstract: | In this paper, we propose two novel frameworks to incorporate auxiliary information about connectivity among entities (i.e., network information) into the estimation of large covariance matrices. The current literature either completely ignores this kind of network information (e.g., thresholding and shrinkage) or utilizes some simple network structure under very restrictive settings (e.g., banding). In the era of big data, we can easily get access to auxiliary information about the complex connectivity structure among entities. Depending on the features of the auxiliary network information at hand and the structure of the covariance matrix, we provide two different frameworks correspondingly —the Network Guided Thresholding and the Network Guided Banding. We show that both Network Guided estimators have optimal convergence rates over a larger class of sparse covariance matrix. Simulation studies demonstrate that they generally outperform other pure statistical methods, especially when the true covariance matrix is sparse, and the auxiliary network contains genuine information. Empirically, we apply our method to the estimation of the covariance matrix with the help of many financial linkage data of asset returns to attain the global minimum variance (GMV) portfolio. |
| Keywords: | Banding, Big Data, Large Covariance Matrix, Network, Thresholding |
| JEL: | C13 C58 G11 |
| Date: | 2024–05–20 |
| URL: | http://d.repec.org/n?u=RePEc:cam:camjip:2416&r= |
| By: | Nils Breitmar; Matthew C. Harding; Hanqiao Zhang |
| Abstract: | Despite the earlier claim of "Death of Distance", recent studies revealed that geographical proximity still greatly influences link formation in online social networks. However, it is unclear how physical distances are intertwined with users' online behaviors in a virtual world. We study the role of spatial dependence on a global online social network with a dyadic Logit model. Results show country-specific patterns for distance effect on probabilities to build connections. Effects are stronger when the possibility for two people to meet in person exists. Relative to weak ties, dependence on proximity is looser for strong social ties. |
| Date: | 2024–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2404.18979&r= |
| By: | Kundan Mukhia; Anish Rai; SR Luwang; Md Nurujjaman; Sushovan Majhi; Chittaranjan Hens |
| Abstract: | This paper identifies the cryptocurrency market crashes and analyses its dynamics using the complex network. We identify three distinct crashes during 2017-20, and the analysis is carried out by dividing the time series into pre-crash, crash, and post-crash periods. Partial correlation based complex network analysis is carried out to study the crashes. Degree density ($\rho_D$), average path length ($\bar{l}$), and average clustering coefficient ($\overline{cc}$) are estimated from these networks. We find that both $\rho_D$ and $\overline{cc}$ are smallest during the pre-crash period, and spike during the crash suggesting the network is dense during a crash. Although $\rho_D$ and $\overline{cc}$ decrease in the post-crash period, they remain higher than pre-crash levels for the 2017-18 and 2018-19 crashes suggesting a market attempt to return to normalcy. We get $\bar{l}$ is minimal during the crash period, suggesting a rapid flow of information. A dense network and rapid information flow suggest that during a crash uninformed synchronized panic sell-off happens. However, during the 2019-20 crash, the values of $\rho_D$, $\overline{cc}$, and $\bar{l}$ did not vary significantly, indicating minimal change in dynamics compared to other crashes. The findings of this study may guide investors in making decisions during market crashes. |
| Date: | 2024–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2405.05642&r= |
| By: | Alberto Baccini; Cristina Re |
| Abstract: | In research evaluation, the fair representation of panels is usually defined in terms of observable characteristics of scholars such as gender or affiliations. An an empirical strategy is proposed for exploring hidden connections between panellists such that, despite the respect of formal requirements, the panel could be considered alike as unfair with respect to the representation of diversity of research approaches and methodologies. The case study regards the three panels selected to evaluate research in economics, statistics and business during the Italian research assessment exercises. The first two panels were appointed directly by the governmental agency responsible for the evaluation, while the third was randomly selected. Hence the third panel can be considered as a control for evaluating about the fairness of the others. The fair representation is explored by comparing the networks of panellists based on their co-authorship relations, the networks based on journals in which they published and the networks based on their affiliated institutions (universities, research centres and newspapers). The results show that the members of the first two panels had connections much higher than the members of the control group. Hence the composition of the first two panels should be considered as unfair, as the results of the research assessments. |
| Date: | 2024–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2405.06476&r= |
| By: | Yifan Zhang |
| Abstract: | Political and advertising campaigns increasingly exploit social networks to spread information and persuade people. This paper studies a persuasion model to examine whether such a strategy is better than simply sending public signals. Receivers in the model have heterogeneous priors and will pass on a signal if they are persuaded by it. I show that a risk neutral or risk loving sender prefers to use public signals, unless more sceptical receivers are sufficiently more connected in the social network. A risk averse sender may prefer to exploit the network. These results still hold when networks exhibit homophily. |
| Date: | 2024–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2404.18965&r= |
| By: | Hanqiao Zhang; Joy D. Xiuyao Yang |
| Abstract: | The COVID-19 pandemic has disrupted traditional academic collaboration patterns, prompting a unique opportunity to analyze the influence of peer effects and coauthorship dynamics on research output. Using a novel dataset, this paper endeavors to make a first cut at investigating the role of peer effects on the productivity of economics scholars, measured by the number of publications, in both pre-pandemic and pandemic times. Results show that peer effect is significant for the pre-pandemic time but not for the pandemic time. The findings contribute to our understanding of how research collaboration influences knowledge production and may help guide policies aimed at fostering collaboration and enhancing research productivity in the academic community. |
| Date: | 2024–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2404.18980&r= |
| By: | Elliott, M.; Jackson, M. O. |
| Abstract: | We introduce a parsimonious multi-sector model of international production and use it to study how a disruption in the production of intermediate goods propagates through to final goods, and how that impact depends on the goods’ positions in, and overall structure of, the production network. We show that the short-run disruption can be dramatically larger than the long-run disruption. The short-run disruption depends on the value of all of the final goods whose supply chains involve a disrupted good, while by contrast the long-run disruption depends only on the cost of the disrupted goods. We use the model to show how increased complexity of supply chains leads to increased fragility in terms of the probability and expected short-run size of a disruption. We also show how decreased transportation costs can lead to increased specialization in production, with lower chances for disruption but larger impacts conditional upon disruption. |
| Keywords: | Supply Chains, Globalization, Fragility, Production Networks, International Trade |
| JEL: | D85 E23 E32 F44 F60 L14 |
| Date: | 2024–05–14 |
| URL: | http://d.repec.org/n?u=RePEc:cam:camdae:2424&r= |
| By: | Sofia Priazhkina; Samuel Palmer; Pablo Martín-Ramiro; Román Orús; Samuel Mugel; Vladimir Skavysh |
| Abstract: | We build a network formation game of firms with trade flows to study the adoption and usage of a new digital currency as an alternative to correspondent banking. We document endogenous heterogeneity and inefficiency in adoption outcomes and explain why higher usage may correspond to lower adoption. Next, we frame the model as a quadratic unconstrained binary optimization (QUBO) problem and apply it to data. Method-wise, QUBO presents an extension to the potential function approach and makes broadly defined network games applicable and empirically feasible, as we demonstrate with a quantum computer. |
| Keywords: | Central bank research; Digital currencies and fintech; Digitalization; Economic models; Financial institutions; Payment clearing and settlement systems; Sectoral balance sheet |
| JEL: | E21 E44 E62 G51 |
| Date: | 2024–05 |
| URL: | http://d.repec.org/n?u=RePEc:bca:bocawp:24-17&r= |
| By: | Daniel de Souza Santos; Tiago Alessandro Espinola Ferreira |
| Abstract: | One of the most discussed problems in the financial world is stock option pricing. The Black-Scholes Equation is a Parabolic Partial Differential Equation which provides an option pricing model. The present work proposes an approach based on Neural Networks to solve the Black-Scholes Equations. Real-world data from the stock options market were used as the initial boundary to solve the Black-Scholes Equation. In particular, times series of call options prices of Brazilian companies Petrobras and Vale were employed. The results indicate that the network can learn to solve the Black-Sholes Equation for a specific real-world stock options time series. The experimental results showed that the Neural network option pricing based on the Black-Sholes Equation solution can reach an option pricing forecasting more accurate than the traditional Black-Sholes analytical solutions. The experimental results making it possible to use this methodology to make short-term call option price forecasts in options markets. |
| Date: | 2024–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2405.05780&r= |