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on Network Economics |
| By: | Marcos R. Fernandes |
| Abstract: | This study investigates the practice of experts aggregating forecasts before informing a decision-maker. The significance of this subject extends to various contexts where experts inform their assessments to a decision-maker following discussions with peers. My findings show that, irrespective of the information structure, aggregation rules introduce no bias to decision-making in expected terms. Nevertheless, the concern revolves around variance. In situations where experts are equally precise, and pair-wise correlation of forecasts is the same across all pairs of experts, the network structure plays a pivotal role in decision-making variance. For classical structures, I show that star networks exhibit the highest variance, contrasting with $d$-regular networks that achieve zero variance, emphasizing their efficiency. Additionally, by employing the Poisson random graph model under the assumptions of a large network size and a small connection probability, the results indicate that both the expected Network Bias and its variance converge to zero as the network size becomes sufficiently large. These insights enhance the understanding of decision-making under different information, network structures and aggregation rules. They enrich the literature on combining forecasts by exploring the effects of prior network communication on decision-making. |
| Date: | 2024–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2406.13749&r= |
| By: | Bjerre-Nielsen, Andreas (University of Copenhagen); Cremers, Jolien; Einsiedler, Johanna; Christensen, Frederik Kølby; Eriksen, Stine Nymann; Kohler, Sören Benjamin Albrecht; Mortensen, Laust Hvas |
| Abstract: | This paper describes a register based multilayered network of the whole Danish population 2008-2021. The network maps potential relationships though family, households, neighbourhoods, colleagues and classmates. In a population of around 6 million we are able to map and track the evolution of an extremely dense network of connections through multiple layers. The paper briefly describes the construction of the networks and provides some details on the properties of the network. |
| Date: | 2023–12–14 |
| URL: | https://d.repec.org/n?u=RePEc:osf:socarx:9wsx7&r= |
| By: | René Van Den Brink (Department of Economics and Tinbergen Institute, VU University, Amsterdam, The Netherlands); Agnieszka Rusinowska (Centre d'Economie de la Sorbonne, CNRS, Université Paris 1 Panthéon-Sorbonne, Paris School of Economics) |
| Abstract: | This paper aims to connect the social network literature on centrality measures with the economic literature on von Neumann-Morgenstern expected utility functions using cooperative game theory. The social network literature studies various concepts of network centrality, such as degree, betweenness, connectedness, and so on. This resulted in a great number of network centrality measures, each measuring centrality in a different way. In this paper, we aim to explore which centrality measures can be supported as von Neumann-Morgenstern expected utility functions, reflecting preferences over different network positions in different networks. Besides standard axioms on lotteries and preference relations, we consider neutrality to ordinary risk. We show that this leads to a class of centrality measures that is fully determined by the degrees (i.e. the numbers of neighbours) of the positions in a network. Although this allows for externalities, in the sense that the preferences of a position might depend on the way how other positions are connected, these externalities can be taken into account only by considering the degrees of the network positions. Besides bilateral networks, we extend our result to general cooperative TU-games to give a utility foundation of a class of TU-game solutions containing the Shapley value |
| Keywords: | group decisions and negotiations; weighted graph; degree centrality; von Neumann-Morgenstern expected utility function; cooperative game |
| JEL: | D85 D81 C02 |
| Date: | 2023–08 |
| URL: | https://d.repec.org/n?u=RePEc:mse:cesdoc:23012r&r= |
| By: | Danyang Jia (School of Cybersecurity, Northwestern Polytechnical University and School of Artificial Intelligence, OPtics and ElectroNics (iOPEN), Northwestern Polytechnical University, CHINA); Ivan Romic (School of Artificial Intelligence, OPtics and ElectroNics (iOPEN), Northwestern Polytechnical University, CHINA, Center for Computational Social Science, Kobe University, and Research Institute for Economics and Business Administration, Kobe University, JAPAN); Lei Shi (School of Statistics and Mathematics, Yunnan University of Finance and Economics, CHINA); Qi Su (Department of Automation, Shanghai Jiao Tong University, Key Laboratory of System Control and Information Processing, Ministry of Education of China, and Shanghai Engineering Research Center of Intelligent Control and Management, CHINA); Chen Liu (School of Ecology and Environmental Sciences, Northwestern Polytechnical University, CHINA); Jinzhuo Liu (School of Software, Yunnan University, CHINA); Petter Holme (Center for Computational Social Science, Kobe University, JANPAN and Department of Computer Science, Aalto University, FINLAND); Xuelong Li (School of Cybersecurity, Northwestern Polytechnical University and School of Artificial Intelligence, OPtics and ElectroNics (iOPEN), Northwestern Polytechnical University, CHINA); Zhen Wang (School of Cybersecurity, Northwestern Polytechnical University and School of Artificial Intelligence, OPtics and ElectroNics (iOPEN), Northwestern Polytechnical University, CHINA) |
| Abstract: | The awareness of individuals regarding their social network surroundings and their capacity to use social connections to their advantage are well-established human characteristics. Economic games, incorporated with network science, are frequently used to examine social behaviour. Traditionally, such game models and experiments artificially limit players' abilities to take varied actions toward distinct social neighbours (i.e., to operate their social networks). We designed an experimental paradigm that alters the degree of social network agency to interact with individual neighbours, and applied it to the prisoner's dilemma (N = 735), trust game (N = 735), and ultimatum game (N = 735) to investigate cooperation, trust, and fairness. The freedom to interact led to more prosocial behaviour across all three economic games and resulted in higher wealth and lower inequality compared to controls without such freedom. These findings suggest that human behaviour is more prosocial than current science indicates. |
| Keywords: | Behavioural science; Networks; Cooperation; Prosociality |
| Date: | 2023–03 |
| URL: | https://d.repec.org/n?u=RePEc:kob:dpaper:dp2023-11&r= |
| By: | Steven Wilkins Reeves; Shane Lubold; Arun G. Chandrasekhar; Tyler H. McCormick |
| Abstract: | The stable unit treatment value assumption states that the outcome of an individual is not affected by the treatment statuses of others, however in many real world applications, treatments can have an effect on many others beyond the immediately treated. Interference can generically be thought of as mediated through some network structure. In many empirically relevant situations however, complete network data (required to adjust for these spillover effects) are too costly or logistically infeasible to collect. Partially or indirectly observed network data (e.g., subsamples, aggregated relational data (ARD), egocentric sampling, or respondent-driven sampling) reduce the logistical and financial burden of collecting network data, but the statistical properties of treatment effect adjustments from these design strategies are only beginning to be explored. In this paper, we present a framework for the estimation and inference of treatment effect adjustments using partial network data through the lens of structural causal models. We also illustrate procedures to assign treatments using only partial network data, with the goal of either minimizing estimator variance or optimally seeding. We derive single network asymptotic results applicable to a variety of choices for an underlying graph model. We validate our approach using simulated experiments on observed graphs with applications to information diffusion in India and Malawi. |
| Date: | 2024–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2406.11940&r= |
| By: | P.Delle Site; André de Palma; Samarth Ghoslya (CY Cergy Paris Université, THEMA) |
| Abstract: | The paper deals with matching and fair pricing in urban peer-to-peer ride-sharing schemes where the following desirable properties hold: (i) matchings between passengers and drivers are decided by a social planner to minimize total car-kilometers travelled, (ii) matchings are stable, i.e. no pair of passenger and driver can both increase their fuel cost-related surplus from breaking the current partnership, and (iii) the scheme is financially sustainable, i.e. there is no need of subsidy. The case where travel times are affected by matchings, in the light of the reduced number of cars travelling on the network, is unexplored. The paper fills this gap. The matching optimization problem is formulated as linear programming problem with nonlinear equilibrium constraints and node-link network representation. Solution to the approximately equivalent mixed-integer linear programming formulation is obtained by available efficient off-the-shelf solvers. Duality theory is used to specify a stability compliant pricing scheme based on fair surplus division: the surplus gained by each traveler is exactly half way between the minimum and the maximum she can obtain from any stable solution. Computation of prices requires solution of two linear programming problems. The price paid by the passenger is received by the driver. Since surplus of each traveler is nonnegative, subsidies are not needed. A toy network and a small network are used to illustrate the theoretical findings, and to appraise the pricing-induced shares of trip cost that accrue to each traveler. |
| Keywords: | Equilibrium, matching, pricing, ride-sharing, stability |
| JEL: | C78 R40 R48 |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:ema:worpap:2024-06&r= |
| By: | Badi H. Baltagi (Center for Policy Research, Maxwell School, Syracuse University, 426 Eggers Hall, Syracuse, NY 13244); Long Liu (Department of Economics, Florida Atlantic University, 777 Glades Road, Boca Raton, FL 33431) |
| Abstract: | This note shows that for a spatial regression with a weight matrix depicting a complete bipartite network, the Moran I test for zero spatial correlation is never rejected when the alternative is positive spatial correlation no matter how large the true value of the spatial correlation coefficient. In contrast, the null hypothesis of zero spatial correlation is always rejected (with probability one asymptotically) when the alternative is negative spatial correlation and the true value of the spatial correlation coefficient is near -1. |
| Keywords: | Spatial Error Model, Moran I Test, Complete Bipartite Network. |
| JEL: | C12 C21 C31 |
| Date: | 2024–07 |
| URL: | https://d.repec.org/n?u=RePEc:max:cprwps:264&r= |
| By: | Shi, Xiangyu |
| Abstract: | In this paper, I argue that in situations of complex network dependence, the traditional and widely used Hausman-style instrumental variable estimation may not be valid for causal identification. This is the case for inter-regional migration networks when evaluating place-based labor market policies, and for correlated unobserved consumer tastes in the product and geographic space in demand estimation. I build an economic model for these two cases, respectively, to derive the estimating equation and to shed light on the fallacy---omitted variable bias and the resulting violation of exclusion restriction---of the traditional econometric framework. I then build an alternative econometric framework and propose a new approach to estimation that exploits higher-order network neighbors and, then, I establish its desirable properties. I conduct Monte Carlo simulations and two empirical analyses that each correspond to the two economic models to validate this new approach of estimation. |
| Keywords: | treatment effect; network; instrumental variable; Hausman IV; spatial linkages; migration network; demand estimation |
| JEL: | C0 C1 C3 |
| Date: | 2024–06 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:121349&r= |
| By: | Fausto Gozzi; Marta Leocata; Giulia Pucci |
| Abstract: | This paper studies a model for the optimal control (by a centralized economic agent which we call the planner) of pollution diffusion over time and space. The controls are the investments in production and depollution and the goal is to maximize an intertemporal utility function. The main novelty is the fact that the spatial component has a network structure. Moreover, in such a time-space setting we also analyze the trade-off between the use of green or non-green technologies: this also seems to be a novelty in such a setting. Extending methods of previous papers, we can solve explicitly the problem in the case of linear costs of pollution. |
| Date: | 2024–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2406.15338&r= |
| By: | Diegmann, André; Pohlan, Laura; Weber, Andrea |
| Abstract: | We study how connections to German federal parliamentarians affect firm dynamics by constructing a novel dataset to measure connections between politicians and the universe of firms. To identify the causal effect of access to political power, we exploit (i) new appointments to the company leadership team and (ii) discontinuities around the marginal seat of party election lists. Our results reveal that connections lead to reductions in firm exits, gradual increases in employment growth without improvements in productivity. The economic effects are mediated by better credit ratings while access to subsidies or procurement contracts are documented to be of lower importance. |
| Keywords: | Politicians, Firm Performance, Identification, Political Connections |
| JEL: | O43 L25 D72 |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:zewdip:300014&r= |