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on Network Economics |
| By: | Saeed Badri (Vrije Universiteit Amsterdam); Bernd Heidergott (Vrije Universiteit Amsterdam); Ines Lindner (Vrije Universiteit Amsterdam) |
| Abstract: | We study the impact of bots on social learning in a social network setting. Regular agents receive independent noisy signals about the true value of a variable and then communicate in a network. They na¨?vely update beliefs by repeatedly taking weighted averages of neighbors’ opinions. Bots are agents in the network that spread fake news by disseminating biased information. Our main contributions are threefold. (1) We show that the consensus of the network is a mapping of the interaction rate between the agents and bots and is discontinuous at zero mass of bots. This implies that even a comparatively “infinitesimal” small number of bots still has a sizeable impact on the consensus and hence represents an obstruction to the “wisdom of crowds”. (2) We prove that the consensus gap induced by the marginal presence of bots depends neither on the agent network or bot layout nor on the assumed connection structure between agents and bots. (3) We show that before the ultimate (and bot-infected) consensus is reached, the network passes through a quasi-stationary phase which has the potential to mitigate the harmful impact of bots. |
| Keywords: | Fake news, Misinformation, Social networks, Social Media, Wisdom of Crowds |
| JEL: | D83 D85 Z13 |
| Date: | 2022–12–22 |
| URL: | http://d.repec.org/n?u=RePEc:tin:wpaper:20220097&r=net |
| By: | Matthew O. Jackson; Stephen M. Nei; Erik Snowberg; Leeat Yariv |
| Abstract: | We examine friendships and study partnerships among university students over several years. At the aggregate level, connections increase over time, but homophily on gender and ethnicity is relatively constant across time, university residences, and different network layers. At the individual level, homophilous tendencies are persistent across time and network layers. Furthermore, we see assortativity in homophilous tendencies. There is weaker, albeit significant, homophily over malleable characteristics---risk preferences, altruism, study habits, and so on. We find little evidence of assimilation over those characteristics. We also document the nuanced impact of network connections on changes in Grade Point Average. |
| JEL: | D85 I21 J15 J16 Z13 |
| Date: | 2022–12 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:30815&r=net |
| By: | Yann Bramoullé (AMSE - Aix-Marseille Sciences Economiques - EHESS - École des hautes études en sciences sociales - AMU - Aix Marseille Université - ECM - École Centrale de Marseille - CNRS - Centre National de la Recherche Scientifique); Renaud Bourlès (AMSE - Aix-Marseille Sciences Economiques - EHESS - École des hautes études en sciences sociales - AMU - Aix Marseille Université - ECM - École Centrale de Marseille - CNRS - Centre National de la Recherche Scientifique, IUF - Institut Universitaire de France - M.E.N.E.S.R. - Ministère de l'Education nationale, de l’Enseignement supérieur et de la Recherche); Eduardo Perez-Richet (Sciences Po - Sciences Po, CEPR - Center for Economic Policy Research - CEPR) |
| Abstract: | We provide the first analysis of the risk-sharing implications of altruism networks. Agents are embedded in a fixed network and care about each other. We explore whether altruistic transfers help smooth consumption and how this depends on the shape of the network. We find that altruism networks have a first-order impact on risk. Altruistic transfers generate efficient insurance when the network of perfect altruistic ties is strongly connected. We uncover two specific empirical implications of altruism networks. First, bridges can generate good overall risk sharing, and, more generally, the quality of informal insurance depends on the average path length of the network. Second, large shocks are well-insured by connected altruism networks. By contrast, large shocks tend to be badly insured in models of informal insurance with frictions. We characterize what happens for shocks that leave the structure of giving relationships unchanged. We further explore the relationship between consumption variance and centrality, correlation in consumption streams across agents, and the impact of adding links. |
| Keywords: | Altruism, Networks, Risk Sharing, Informal Insurance |
| Date: | 2021–06 |
| URL: | http://d.repec.org/n?u=RePEc:hal:spmain:hal-02563135&r=net |
| By: | Michel Grabisch (CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique); Agnieszka Rusinowska; Xavier Venel |
| Abstract: | We investigate the phenomenon of diffusion in a countably infinite society of individuals interacting with their neighbors in a network. At a given time, each individual is either active or inactive. The diffusion is driven by two characteristics: the network structure and the diffusion mechanism represented by an aggregation function. We distinguish between two diffusion mechanisms (probabilistic, deterministic) and focus on two types of aggregation functions (strict, Boolean). Under strict aggregation functions, polarization of the society cannot happen, and its state evolves towards a mixture of infinitely many active and infinitely many inactive agents, or towards a homogeneous society. Under Boolean aggregation functions, the diffusion process becomes deterministic and the contagion model of Morris (2000) becomes a particular case of our framework. Polarization can then happen. Our dynamics also allows for cycles in both cases. The network structure is not relevant for these questions, but is important for establishing irreducibility, at the price of a richness assumption: the network should contain at least one complex star and have enough space for storing local configurations. Our model can be given a game-theoretic interpretation via a local coordination game, where each player would apply a best-response strategy in a random neighborhood. |
| Keywords: | diffusion, countable network, aggregation function, polarization, convergence, bestresponse |
| Date: | 2022 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:halshs-03881455&r=net |
| By: | Michel Grabisch (CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique); Antoine Mandel; Agnieszka Rusinowska |
| Abstract: | We propose a model of the joint evolution of opinions and social relationships in a setting where social influence decays over time. The dynamics are based on bounded confidence: social connections between individuals with distant opinions are severed while new connections are formed between individuals with similar opinions. Our model naturally gives raise to strong diversity, i.e., the persistence of heterogeneous opinions in connected societies, a phenomenon that most existing models fail to capture. The intensity of social interactions is the key parameter that governs the dynamics. First, it determines the asymptotic distribution of opinions. In particular, increasing the intensity of social interactions brings society closer to consensus. Second, it determines the risk of polarization, which is shown to increase with the intensity of social interactions. Our results allow to frame the problem of the design of public debates in a formal setting. We hence characterize the optimal strategy for a social planner who controls the intensity of the public debate and thus faces a trade-off between the pursuit of social consensus and the risk of polarization. We also consider applications to political campaigning and show that both minority and majority candidates can have incentives to lead society towards polarization. |
| Keywords: | opinion dynamics, network formation, network fragility, polarization, institution design, political campaign |
| Date: | 2022 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:halshs-03881461&r=net |
| By: | Matilde Giaccherini; Joanna Kopinska |
| Abstract: | We quantify the effects of online vaccine skepticism on vaccine uptake and health complications for individuals not targeted by immunization campaigns. We collect the universe of Italian vaccine-related tweets for 2013-2018, label anti-vax stances using NLP, and match them with vaccine coverage and vaccine-preventable hospitalizations at the most granular level (municipal-ity and year). We propose a model of opinion dynamics on social networks that matches the observed data and shows that a vaccine mandate increases the average vaccination rate, but it also increases the controversialness around the topic, endogenously fueling polarization of opinions among users. We then leverage the intransitivity in network connections with “friends of friends” to isolate the exogenous source of variation for users’ vaccine-related stances and implement an IV strategy. We find that a 10pp increase in the municipality anti-vax stance causes a 0.43pp de-crease in coverage of the Measles-Mumps-Rubella vaccine, 2.1 additional hospitalizations every 100k residents among individuals untargeted by the immunization (newborns, the immunosup-pressed, pregnant women) and an excess expenditure of 7, 311 euro, representing an 11% increase in health expenses. |
| Keywords: | social media, Twitter, vaccines, controversialness, polarization, text analysis |
| JEL: | I18 L82 Z18 |
| Date: | 2022 |
| URL: | http://d.repec.org/n?u=RePEc:ces:ceswps:_10184&r=net |
| By: | Marcos Ross Fernandes |
| Abstract: | In this study, I propose a theoretical social learning model to investigate how confirmation bias affects opinions when agents exchange information over a social network. Hence, besides exchanging opinions with friends, agents observe a public sequence of potentially ambiguous signals and interpret it according to a rule that includes confirmation bias. First, this study shows that regardless of level of ambiguity both for people or networked society, only two types of opinions can be formed, and both are biased. However, one opinion type is less biased than the other depending on the state of the world. The size of both biases depends on the ambiguity level and relative magnitude of the state and confirmation biases. Hence, long-run learning is not attained even when people impartially interpret ambiguity. Finally, analytically confirming the probability of emergence of the less-biased consensus when people are connected and have different priors is difficult. Hence, I used simulations to analyze its determinants and found three main results: i) some network topologies are more conducive to consensus efficiency, ii) some degree of partisanship enhances consensus efficiency even under confirmation bias and iii) open-mindedness (i.e. when partisans agree to exchange opinions with opposing partisans) might inhibit efficiency in some cases. |
| Keywords: | Social Networks; Social Learning; Misinformation; Confirmation Bias |
| JEL: | C11 D83 D85 |
| Date: | 2023–01–09 |
| URL: | http://d.repec.org/n?u=RePEc:spa:wpaper:2023wpecon02&r=net |
| By: | Stefano Carattini; Kenneth Gillingham; Xiangyu Meng; Erez Yoeli |
| Abstract: | Observability has been demonstrated to influence the adoption of pro-social behavior in a variety of contexts. This study implements a field experiment to examine the influence of observability in the context of a novel pro-social behavior: peer-to-peer solar. Peer-to-peer solar offers an opportunity to households who cannot have solar on their homes to access solar energy from their neighbors. However, unlike solar installations, peer-to-peer solar is an invisible form of pro-environmental behavior. We implemented a set of randomized campaigns using Facebook ads in the Massachusetts cities of Cambridge and Somerville, in partnership with a peer-to-peer company. In the campaigns, treated customers were informed that they could share “green reports” online, providing information to others about their greenness. We find that interest in peer-to-peer solar increases by up to 30% when “green reports, ” which would make otherwise invisible behavior visible, are mentioned in the ads. |
| Keywords: | peer to peer solar, pro-environmental behavior, social rewards, visibility, Facebook |
| JEL: | C93 D91 Q20 |
| Date: | 2022 |
| URL: | http://d.repec.org/n?u=RePEc:ces:ceswps:_10173&r=net |
| By: | Caroline Hillairet (CREST - Centre de Recherche en Économie et Statistique - ENSAI - Ecole Nationale de la Statistique et de l'Analyse de l'Information [Bruz] - X - École polytechnique - ENSAE Paris - École Nationale de la Statistique et de l'Administration Économique - CNRS - Centre National de la Recherche Scientifique); Olivier Lopez (LPSM (UMR_8001) - Laboratoire de Probabilités, Statistique et Modélisation - SU - Sorbonne Université - CNRS - Centre National de la Recherche Scientifique - UPCité - Université Paris Cité); Louise d'Oultremont; Brieuc Spoorenberg |
| Abstract: | In this paper, we provide a model that aims to describe the impact of a massive cyber attack on an insurance portfolio, taking into account the structure of the network. Due to the contagion, such an event can rapidly generate consequent damages, and mutualization of the losses may not hold anymore. The composition of the portfolio should therefore be diversified enough to prevent or reduce the impact of such events, with the difficulty that the relationships between actor is difficult to assess. Our approach consists in introducing a multi-group epidemiological model which, apart from its ability to describe the intensity of connections between actors, can be calibrated from a relatively small amount of data, and through fast numerical procedures. We show how this model can be used to generate reasonable scenarios of cyber events, and investigate the response to different types of attacks or behavior of the actors, allowing to quantify the benefit of an efficient prevention policy. |
| Keywords: | Cyber insurance, cyber risk, compartmental models, multi-SIR, network structures |
| Date: | 2022–11–01 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:hal-03388840&r=net |
| By: | Pierre Cotterlaz (ECON - Département d'économie (Sciences Po) - Sciences Po - Sciences Po - CNRS - Centre National de la Recherche Scientifique) |
| Abstract: | Spatial frictions are key to explain many economic phenomena. This thesis provides three pieces of evidence on the origins, prevalence and consequences of such frictions.In the first chapter, we focus on spatial frictions in the diffusion of knowledge. We explain the puzzling persistence and stability of the spatial decay in patent citation flows by innovator networks. We establish that knowledge percolates: firms disproportionately cite new patents from prior contacts, and form links with contacts of their contacts. Embedding this percolation into a network formation model is sufficient to rationalize the negative link between aggregate knowledge flows and distance.In the second chapter, we shed some light on the role of spatial information frictions in shaping international trade flows. We use the specific context of the XIXth Century, during which the creation of international news agencies facilitated the transmission of information across countries. We show that trade between a pair of countries increases when both are covered by a news agency. The reduction in information friction was therefore one of the many factors behind the First Globalization.The last chapter investigates whether transport costs are the main component of within-country trade costs. While it is well-established that international trade costs are not limited to transport costs, evidence is much scarcer for intra-national trade flows. We use hurricane Sandy as a natural experiment shifting upwards transport costs in some areas of the US to establish that if transport costs were the sole driver of the distance elasticity of trade flows within the US, this distance elasticity would be much lower. |
| Abstract: | Les frictions spatiales jouent un rôle crucial dans l'explication de nombreux phénomènes économiques. Dans cette thèse, nous étudions les origines, la prévalence et les conséquences de telles frictions à travers trois exemples. Dans le premier chapitre, nous nous intéressons aux frictions spatiales pesant sur la diffusion de la connaissance. Nous expliquons l'effet négatif de la distance sur les flux de citations entre brevets par la structure des réseaux d'innovation. Nous montrons que la connaissance percole: les entreprises tendent à citer davantage les nouveaux brevets de leurs contacts existants, et à former de nouveaux liens avec des contacts de leurs contacts. Dans le second chapitre, nous explorons les liens entre frictions informationnelles et commerce international. Nous utilisons le contexte spécifique du XIXe siècle, au cours duquel émergent des agences de presse mondiales, facilitant le partage d'informations sur les marchés étrangers. Nous montrons que deux pays commercent davantage une fois qu'ils bénéficient de ce choc positif sur la capacité à obtenir de l'information. Les agences de presse s'insèrent donc parmi les nombreux facteurs explicatifs de la Première Mondialisation. Le dernier chapitre cherche à déterminer si les coûts de transport constituent l'essentiel des obstacles au commerce à l'intérieur d'un pays. Nous utilisons l'ouragan Sandy comme une expérience naturelle à l'origine d'une hausse des coûts de transport pour les flux transitant par certaines zones, et montrons que l'élasticité intra-USA des flux commerciaux à la distance serait bien plus faible si les coûts de transport étaient les seuls responsables de cette élasticité. |
| Keywords: | Trade costs, Innovation, Networks, News agencies, Coûts du commerce, Réseaux, Agences de presse |
| Date: | 2021–06–03 |
| URL: | http://d.repec.org/n?u=RePEc:hal:spmain:tel-03436173&r=net |
| By: | Matteo Burzoni; Alessandro Doldi; Enea Monzio Compagnoni |
| Abstract: | We consider the problem of optimally sharing a financial position among agents with potentially different reference risk measures. The problem is equivalent to computing the infimal convolution of the risk metrics and finding the so-called optimal allocations. We propose a neural network-based framework to solve the problem and we prove the convergence of the approximated inf-convolution, as well as the approximated optimal allocations, to the corresponding theoretical values. We support our findings with several numerical experiments. |
| Date: | 2022–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2212.11752&r=net |