Diplomacy & Defense Think Tank News

Even though China’s role in South Asia has grown in the last decade, Beijing has little by way of policy or strategy when it comes to dealing with political instability in the region. 

This is Pakistan’s most significant formal defense pact in decades, and the first such agreement that an Arab Gulf state has inked with a nuclear-armed partner.

Leaked documents from Geedge Networks and GoLaxy show how repression in China is both a political imperative and a profitable business.

That Indonesia’s costly high-speed rail is operating at a loss right out of the gate has raised some eyebrows.

Kim Jong Un showed his tit-for-tat move against the latest South Korea-U.S. military drills.

In ihrer Außen- und Sicherheitspolitik verfolgte die Bundesrepublik Deutschland von Anfang an ein Rollenkonzept, das sich an drei grund­legenden Prämissen orientierte: »Nie wieder« (Absage an die Gräuel des nationalsozialistischen Deutschland), »niemals im Alleingang« (prinzi­pieller Multilateralismus), »Diplomatie statt Gewalt«. Dieses Rollenkonzept lässt sich als das einer »Zivilmacht« beschreiben. Die im Verfolg dieses Konzeptes insgesamt überaus erfolgreiche Politik verdankte sich innen- und außenpolitischen Voraussetzungen, die im Verlauf der beiden letzten Jahrzehnte zusehends erodierten. Innenpolitisch ermöglicht wurden die Erfolge der Zivilmacht Deutschland durch kluge Machtpolitik: Die Bundesrepublik entfaltete ein auf ihr Rol­lenkonzept zugeschnittenes Portfolio an Machtressourcen, die letztlich der friedlichen Wiedervereinigung eines in ein vereintes Europa einge­betteten Deutschlands den Weg bahnten. Die Kultivierung dieser Macht­ressourcen und eine konsequente strategische Ausrichtung am Konzept der Zivilmacht wurden in jüngster Zeit jedoch vernachlässigt. Außenpolitisch beruhten die Erfolge auf der Verfügbarkeit von inner- und außereuropäischen Partnern, leistungsfähigen internationalen Orga­nisationen und einem internationalen Umfeld, das insgesamt in gewissem Ausmaß »zivilisiert« war: Die Anwendung von Gewalt wurde in den zwischenstaatlichen Beziehungen durch das nukleare Patt in Europa ein­gehegt. In seinen Grundzügen ist das Rollenkonzept der Zivilmacht für Deutsch­land auch unter den gegenwärtigen, wesentlich ungünstigeren äußeren Bedingungen unverzichtbar: Es ist außen- wie innenpolitisch zutiefst verwoben mit der Identität und Verfassung der Bundesrepublik als liberale Demokratie. Die Bundesregierung sollte daher auf eine konsequente Wahrung und Mehrung ihres spezifischen Machtportfolios setzen und dabei insbesondere die (Fort-)Entwicklung der internationalen und supranationalen Zusammenarbeit mit anderen liberalen Demokratien und gleichgesinnten Partnern vorantreiben.

Die im DIW Berlin angesiedelte forschungsbasierte Infrastruktureinrichtung Sozio-oekonomisches Panel (SOEP) sucht zum nächstmöglichen Zeitpunkt eine studentische Hilfskraft (w/m/div) für 12 Wochenstunden.

Die am DIW Berlin angesiedelte forschungsbasierte Infrastruktureinrichtung Sozio-oekonomisches Panel (SOEP) ist eine der größten und am längsten laufenden multidisziplinären Panelstudien weltweit, für die derzeit jährlich etwa 30.000 Menschen in knapp 15.000 Haushalten befragt werden. Das SOEP hat den Anspruch den gesellschaftlichen Wandel zu erfassen und steht immer neuen vielfältigen Themen- und Aufgabenfeldern gegenüber.

Ihre Aufgabe ist die Mitwirkung bei der Fragebogentestung, Datenaufbereitung und -prüfung sowie Berichtslegung und wissenschaftliche Recherche. Dabei lernen Sie das reichhaltige Angebot der SOEP-Daten in seiner ganzen Breite kennen und gewinnen Einblicke in die Arbeit mit empirischen Daten.

The existential catastrophe faced by the population of the Gaza Strip currently looms large in the foreign policy and security debates. The plight of civilians there is par­ticularly acute. Yet, severe crises persist elsewhere too – from Ukraine and Sudan to Myanmar, the Democratic Republic of Congo, and Haiti – where protracted violent conflicts continue to cause grave suffering among civilians. This grim reality is under­scored in the United Nations Secretary-General’s latest annual report, released in May. At the same time, conventional mechanisms for international conflict resolution are failing in an increasing number of contexts. In light of this, it is crucial to system­atically track evolving conflict dynamics and to revise approaches to the protection of civilians accordingly.

The European Union operates largely in accordance with the principles of consensus democracy – that is, it seeks to integrate as many parties spanning the political spec­trum of its member states as possible. Amid the recent growth of far-right parties at both the national and European level, this approach has led to the increased participation of such forces in EU institutions. Analysis of key actors at the EU level shows that since no later than the 2024 European elections, representatives of far-right par­ties have been involved in all major EU decisions. The centres of their influence are the European Council and the Council of the EU, where they participate as leaders or partners in national governments. But they are increasingly becoming more influential in the European Parliament, which has shifted to the right and where alternative majorities are now possible. At the same time, significant differences remain between the far-right parties. Ultimately, the extent of their influence and which far-right trend predominates within the EU system depends mainly on the largest force in European politics – the European People’s Party.

Die Europäische Union operiert in weiten Teilen nach den Prinzipien einer Konsensdemokratie, die darauf ausgerichtet ist, möglichst das komplette politische Spektrum ihrer Mitgliedstaaten zu integrieren. Angesichts der Zuwächse von Rechts­außen­parteien auf nationaler wie auf europäischer Ebene vermehrt sich daher zunehmend auch ihr Einfluss in den EU-Institutionen. Die Analyse der zentralen Akteure auf EU‑Ebene zeigt: Spätestens seit den Europawahlen 2024 sind Vertreter:innen von Rechtsaußenparteien in nahezu allen EU-Entscheidungsprozessen präsent. Die Schwer­punkte ihres Einflusses liegen – aufgrund ihrer Teilhabe an nationalen Regierungen – im Europäischen Rat und im Rat der EU, zunehmend aber auch im nach rechts gerück­ten Europäischen Parlament, in dem inzwischen alternative Mehrheitskonstellationen möglich sind. Gleichzeitig bleiben die Unterschiede innerhalb des Rechtsaußenspektrums groß. Wie prägend dessen Einfluss ist und welche Strömung sich unter den Rechtsaußenparteien durchsetzt, hängt maßgeblich von der Europäischen Volks­partei (EVP) ab.

China’s limited success in managing the political risks of the BRI has resulted in reputational costs and played a role in China’s curtailed flow of BRI lending in recent years.

Far from consolidating, recognition by the international community could expose the group’s rifts over resources, external demands, and identity.

Amidst different global food insecurity challenges, like the COVID-19 pandemic and economic turmoil, this article investigates the potential of machine learning (ML) to enhance food insecurity forecasting. So far, only few existing studies have used pre-shock training data to predict food insecurity and if they did, they have neither done this at the household-level nor systematically tested the performance and robustness of ML algorithms during the shock phase. To address this research gap, we use pre-COVID trained models to predict household-level food insecurity during the COVID-19 pandemic in Uganda and propose a new approach to evaluate the performance and robustness of ML models. The objective of this study is therefore to find high-performance and robust ML algorithms during a shock period, which is both methodologically innovative and practically relevant for food insecurity research. First, we find that ML can work well in a shock context when only pre-shock food security data are available. We can identify 80% of food-insecure households during the COVID-19 pandemic based on pre-shock trained models at the cost of falsely classifying around 40% of food-secure households as food insecure. Second, we show that the extreme gradient boosting algorithm, trained by balanced weighting, works best in terms of prediction quality. We also identify the most important predictors and find that demographic and asset features play a crucial role in predicting food insecurity. Last but not least, we also make a contribution by showing how different ML models should be evaluated in terms of their area under curve (AUC) value, the ability of the model to correctly classify positive and negative cases, and in terms of the change in AUC in different situations.

Amidst different global food insecurity challenges, like the COVID-19 pandemic and economic turmoil, this article investigates the potential of machine learning (ML) to enhance food insecurity forecasting. So far, only few existing studies have used pre-shock training data to predict food insecurity and if they did, they have neither done this at the household-level nor systematically tested the performance and robustness of ML algorithms during the shock phase. To address this research gap, we use pre-COVID trained models to predict household-level food insecurity during the COVID-19 pandemic in Uganda and propose a new approach to evaluate the performance and robustness of ML models. The objective of this study is therefore to find high-performance and robust ML algorithms during a shock period, which is both methodologically innovative and practically relevant for food insecurity research. First, we find that ML can work well in a shock context when only pre-shock food security data are available. We can identify 80% of food-insecure households during the COVID-19 pandemic based on pre-shock trained models at the cost of falsely classifying around 40% of food-secure households as food insecure. Second, we show that the extreme gradient boosting algorithm, trained by balanced weighting, works best in terms of prediction quality. We also identify the most important predictors and find that demographic and asset features play a crucial role in predicting food insecurity. Last but not least, we also make a contribution by showing how different ML models should be evaluated in terms of their area under curve (AUC) value, the ability of the model to correctly classify positive and negative cases, and in terms of the change in AUC in different situations.

Amidst different global food insecurity challenges, like the COVID-19 pandemic and economic turmoil, this article investigates the potential of machine learning (ML) to enhance food insecurity forecasting. So far, only few existing studies have used pre-shock training data to predict food insecurity and if they did, they have neither done this at the household-level nor systematically tested the performance and robustness of ML algorithms during the shock phase. To address this research gap, we use pre-COVID trained models to predict household-level food insecurity during the COVID-19 pandemic in Uganda and propose a new approach to evaluate the performance and robustness of ML models. The objective of this study is therefore to find high-performance and robust ML algorithms during a shock period, which is both methodologically innovative and practically relevant for food insecurity research. First, we find that ML can work well in a shock context when only pre-shock food security data are available. We can identify 80% of food-insecure households during the COVID-19 pandemic based on pre-shock trained models at the cost of falsely classifying around 40% of food-secure households as food insecure. Second, we show that the extreme gradient boosting algorithm, trained by balanced weighting, works best in terms of prediction quality. We also identify the most important predictors and find that demographic and asset features play a crucial role in predicting food insecurity. Last but not least, we also make a contribution by showing how different ML models should be evaluated in terms of their area under curve (AUC) value, the ability of the model to correctly classify positive and negative cases, and in terms of the change in AUC in different situations.

It is nearly impossible for even the closest U.S. allies to comply with its byzantine immigration system. That needs to change.

Treasury’s latest sanctions show momentum, but contradictions in U.S. policy risk blunting their impact.

Geopolitics in East Asia is pushing Ulaanbaatar and Tokyo to rely more on each other.

Recent trends cast doubt on the long-standing assumption that young people in Japan are politically irrelevant.

Unlike when the U.S. held unipolar power, times have changed as China and the BRICS countries begin fashioning a non-militarized, multipolar world.