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In times of increased political polarization, the continuing existence of a deliberative arena where people with antagonistic views may engage with each other in non-violent ways is critical for democracy to live on. Social media are usually not conceived as such arenas. On the contrary, there has been widespread worry about their role in increasing polarization and political violence. This paper suggests a more positive impact of social media on democracy. Our analysis focuses on the subreddit “r/WallStreetBets” (r/WSB) - a finance-related forum that came under the spotlight when its users coordinated a financial attack on hedge funds during the Gamestop saga in early 2021. Based on an original method attributing partisanship scores to users, we present a network analysis of interactions between users at the opposite sides of the political spectrum on r/WSB. We then develop a content analysis of politically relevant threads in which polarized users participate. Our analyses show that r/WSB provides a rare space where users with antagonistic political leanings engage with each other, debate, and even cooperate.
In more and more situations, artificially intelligent algorithms have to model humans’ (social) preferences on whose behalf they increasingly make decisions. They can learn these preferences through the repeated observation of human behavior in social encounters. In such a context, do individuals adjust the selfishness or prosociality of their behavior when it is common knowledge that their actions produce various externalities through the training of an algorithm? In an online experiment, we let participants’ choices in dictator games train an algorithm. Thereby, they create an externality on future decision making of an intelligent system that affects future participants. We show that individuals who are aware of the consequences of their training on the pay- offs of a future generation behave more prosocially, but only when they bear the risk of being harmed themselves by future algorithmic choices. In that case, the externality of artificially intelligence training induces a significantly higher share of egalitarian decisions in the present.
Debt levels in the eurozone have reached new record highs. The member countries have tried to cushion the economic consequences of the corona pandemic with a massive increase in government spending. End of 2021 public debt in relation to GDP will approach 100% on average. There are various calls to abolish or soften the Maastricht rules of limiting sovereign debt. We see the risk of a new sovereign debt crisis in this decade if it is not possible to bring public debt down to an acceptable level. Our new fiscal rule would be suitable and appropriate for this purpose, because obviously the Maastricht criteria have failed. In contrast to the rigid 3% Maastricht-criterion, our rule is flexible and it addresses the main problem: excessively high public debt ratios. And it lowers the existing incentives for highly indebted governments to exert expansionary pressure on monetary policy. If obeyed strictly, our rule reinforces the snowball effect and reduces the excessively high debt ratios within a manageable period, even if nominal growth is weak. This is confirmed by simulations with different scenarios as well as with the hypothetical application of the new fiscal rule to eurozone economies from 2022 to 2026. Finally, we take up the recent proposal by ESM economists to increase the permissible debt ratio from 60 to 100% of GDP in the eurozone.
Im Zuge der fortlaufenden Digitalisierung im Mobilitätssektor werden aktuell besonders in Großstädten verstärkt geteilte on-demand Fahrdienstleistungen implementiert. Das sog. Ridepooling beschreibt eine dynamische und digitale Form des konventionellen Sammeltaxis, bei welcher durch eine intelligente Algorithmik mehrere voneinander unabhängige, zeitlich korrespondierende Fahrtwünsche in Echtzeit zu einer Route kombiniert werden. So können einander unbekannte Kund*innen gemeinsam und gleichzeitig nach ihren individuellen Bedürfnissen auf Direktverbindungen befördert werden. Viele der Ridepooling-Angebote werden in urban geprägten Raumstrukturen von privaten Verkehrsunternehmen - teilweise sogar eigenwirtschaftlich - betrieben und als nachhaltige Mobilitätsform beworben: Sie soll die sich individualisierenden Mobilitätsbedürfnisse der Bürger*innen befriedigen, dadurch städtische Problematiken wie hohe Luft- und Lärmbelastung, Staubildung sowie Flächenknappheit adressieren und zu einer umweltfreundlichen Verlagerung des lokalen Verkehrsaufkommens (Modal Shift) führen.
Die vorliegende Arbeit untersucht am Beispiel der Großstädte Berlin und Hamburg, wie und unter welchen Zielsetzungen der unterschiedlichen Akteure die neuen Angebotsformen implementiert wurden und welche Auswirkungen sie auf die städtischen Mobilitätssysteme haben.
Durch Expert*innen-Interviews mit städtischen Behörden, öffentlichen und privaten Verkehrsunternehmen, Verkehrsverbünden und Expert*innen für digitale und städtische Mobilität soll der aktuell noch geringe Forschungsstand über die Zielsetzungen, Formen und Auswirkungen von Ridepooling-Angeboten in städtischen Räumen um praxisnahe Betrachtungen und Erkenntnisse erweitert werden. Es kann angenommen werden, dass die unterschiedlichen Ausgestaltungen der untersuchten Angebote von ioki, CleverShuttle, MOIA und BerlKönig dabei durchaus voneinander differierende Effekte auf das Nutzungsverhalten der Kund*innen und die städtische Verkehrsgestaltung sowie deren ökologischen und sozialen Nachhaltigkeitsdimensionen haben.
In a parsimonious regime switching model, we find strong evidence that expected consumption growth varies over time. Adding inflation as a second variable, we uncover two states in which expected consumption growth is low, one with high and one with negative expected inflation. Embedded in a general equilibrium asset pricing model with learning, these dynamics replicate the observed time variation in stock return volatilities and stock- bond return correlations. They also provide an alternative derivation for a measure of time-varying disaster risk suggested by Wachter (2013), implying that both the disaster and the long-run risk paradigm can be extended towards explaining movements in the stock-bond correlation.
This article compares the three initial safety nets spanned by the European Union in response to the Covid-19 crisis: SURE, the Pandemic Crisis Support, and the European Guarantee Fund. It compares their design regarding scope, generosity, target groups, implementation, the types of solidarity and conditionality, and asks how they reflect on core-periphery relations in the EU. The article finds that the most important factor in all three instruments is risk-sharing between member states, even though SURE and the EGF display elements of fiscal solidarity. Finally, the article shows that Euro crisis countries from the South are the main recipients of financial aid, while Central and East European countries receive significantly less assistance and core countries in the North and West have no need for them.