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on Network Economics |
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Issue of 2017‒08‒27
four papers chosen by Pedro CL Souza Pontifícia Universidade Católica do Rio de Janeiro |
| By: | Christoph Aymanns; Jakob Foerster; Co-Pierre Georg |
| Abstract: | We model the spread of news as a social learning game on a network. Agents can either endorse or oppose a claim made in a piece of news, which itself may be either true or false. Agents base their decision on a private signal and their neighbors' past actions. Given these inputs, agents follow strategies derived via multi-agent deep reinforcement learning and receive utility from acting in accordance with the veracity of claims. Our framework yields strategies with agent utility close to a theoretical, Bayes optimal benchmark, while remaining flexible to model re-specification. Optimized strategies allow agents to correctly identify most false claims, when all agents receive unbiased private signals. However, an adversary's attempt to spread fake news by targeting a subset of agents with a biased private signal can be successful. Even more so when the adversary has information about agents' network position or private signal. When agents are aware of the presence of an adversary they re-optimize their strategies in the training stage and the adversary's attack is less effective. Hence, exposing agents to the possibility of fake news can be an effective way to curtail the spread of fake news in social networks. Our results also highlight that information about the users' private beliefs and their social network structure can be extremely valuable to adversaries and should be well protected. |
| Date: | 2017–08 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1708.06233&r=net |
| By: | Francis X. Diebold; Laura Liu; Kamil Yilmaz |
| Abstract: | We use variance decompositions from high-dimensional vector autoregressions to characterize connectedness in 19 key commodity return volatilities, 2011-2016. We study both static (full-sample) and dynamic (rolling-sample) connectedness. We summarize and visualize the results using tools from network analysis. The results reveal clear clustering of commodities into groups that match traditional industry groupings, but with some notable differences. The energy sector is most important in terms of sending shocks to others, and energy, industrial metals, and precious metals are themselves tightly connected. |
| JEL: | G1 |
| Date: | 2017–08 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:23685&r=net |
| By: | Ravi Goyal; Victor De Gruttola |
| Abstract: | Analysis of sexual history data intended to describe sexual networks presents many challenges arising from the fact that most surveys collect information on only a very small fraction of the population of interest. |
| Keywords: | Network Statistics, Network Space, Sexual History |
| JEL: | I |
| URL: | http://d.repec.org/n?u=RePEc:mpr:mprres:f43351e224534f6386f37ba58e7271d9&r=net |
| By: | John Cawley; Euna Han; Jiyoon (June) Kim; Edward C. Norton |
| Abstract: | Estimating peer effects is notoriously difficult because of the reflection problem and the endogeneity of peer group formation. This paper tests for peer effects in obesity in a novel way that addresses these challenges. It addresses the reflection problem by using the alter’s genetic risk score for obesity, which is a significant predictor of obesity, is determined prior to birth, and cannot be affected by the behavior of others. It addresses the endogeneity of peer group formation by examining peers who are not self-selected: full siblings. Using data from the National Longitudinal Survey of Adolescent Health, we find evidence of positive peer effects in weight and obesity; having a sibling with a high genetic predisposition raises one’s risk of obesity, even controlling for one’s own genetic predisposition to obesity. Implications of the findings include that peer effects may be an explanation for continued worldwide increases in weight, and that, because of social multipliers, the cost-effectiveness of obesity treatment and prevention programs may have been underestimated. |
| JEL: | D1 I1 I12 I18 J1 Z18 |
| Date: | 2017–08 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:23719&r=net |