EPIM Policy Update April 2018. Elections in Hungary & Italy - A German-Franco alliance? - Progress on CEAS

This EPIM policy update covers the elections in Italy and Hungary, which both highlighted the continued significance of immigration for European electorates. Attempts at forming a new government in Italy and Viktor Orbán’s plans for his new term in Hungary will be closely watched in the weeks and months ahead. In the closer look section, the Migration Policy Group presents a new Europe-wide campaign on migration with the objective of engaging the public and influencing EU migration policy. The policy update’s special focus deals with the coalition agreement in Germany and the proposal for a new immigration bill in France. It also considers to what extent a German-Franco alliance on immigration policies could emerge at EU level and how this would impact on a number of ongoing discussions on EU migration policy reforms. This issue also examines the findings of the Court of Justice in the ‘A and S’ and ‘Pisciotti’ cases. Further sections report on progress made on the drafts of the UN Global Compacts and reflect on the second anniversary of the EU-Turkey Statement. This EPIM policy update looks at the latest developments in the negotiations of the legislative reforms of the Eurodac Regulation, the Dublin Regulation, the Reception Conditions Directive and the Asylum Procedures Directive. Finally, this policy update also includes a list of funding opportunities and calls

Tackling irregular migration through development-a flawed approach? EPC Discussion paper, 22 May 2017

Faced with a large influx of asylum seekers in recent years, but little agreement among member states on how to share the burden, the European Union (EU) is increasingly turning to third countries to stem the flow. This push for external action focuses on tackling the 'root causes of migration' as well as strengthening third countries' migration management efforts. The current approach is based on the premise that increasing development aid to developing countries will reduce the stimulus for emigration. At the same time, the principle of conditionality has emerged as a means of ensuring cooperation on operational matters such as border control and readmission. Development assistance is thus becoming an incentive for third countries to cooperate with the EU on migration management

EPIM Policy Update July 2018

This EPIM policy update covers the June European Council Summit and the developments leading up to it. Whilst the Council conclusions are not ground-breaking, two points deserve attention. Firstly, they include the concept of ‘regional disembarkation platforms’ as a new approach to processing those who are saved in Search and Rescue (SAR) operations outside of the EU. Secondly, they mention ‘controlled centres’ within member states to provide for rapid processing of asylum seekers and other migrants. As further explored in this update’s special focus section, serious questions remain concerning the implications for human rights protection and the feasibility of these new approaches

Measuring Strategic Uncertainty in Coordination Games

Lecture on the first SFB/TR 15 meeting, Gummersbach, July, 18 - 20, 2004This paper explores predictability of behavior in coordination games with multiple equilibria. In a laboratory experiment we measure subjects' certainty equivalents for three coordination games and one lottery. Attitudes towards strategic uncertainty in coordination games are related to risk aversion, experience seeking, gender and age. From the distribution of certainty equivalents among participating students we estimate probabilities for successful coordination in a wide range of coordination games. For many games success of coordination is predictable with a reasonable error rate. The best response of a risk neutral player is close to the global-game solution. Comparing choices in coordination games with revealed risk aversion, we estimate subjective probabilities for successful coordination. In games with a low coordination requirement, most subjects underestimate the probability of success. In games with a high coordination requirement, most subjects overestimate this probability. Data indicate that subjects have probabilistic beliefs about success or failure of coordination rather than beliefs about individual behavior of other players

Competition between species can stabilize public-goods cooperation within a species

Competition between species is a major ecological force that can drive evolution. Here, we test the effect of this force on the evolution of cooperation within a species. We use sucrose metabolism of budding yeast, Saccharomyces cerevisiae, as a model cooperative system that is subject to social parasitism by cheater strategies. We find that when cocultured with a bacterial competitor, Escherichia coli, the frequency of cooperator phenotypes in yeast populations increases dramatically as compared with isolated yeast populations. Bacterial competition stabilizes cooperation within yeast by limiting the yeast population density and also by depleting the public goods produced by cooperating yeast cells. Both of these changes induced by bacterial competition increase the cooperator frequency because cooperator yeast cells have a small preferential access to the public goods they produce; this preferential access becomes more important when the public good is scarce. Our results indicate that a thorough understanding of species interactions is crucial for explaining the maintenance and evolution of cooperation in nature.United States. National Institutes of Health (GM085279‐02)National Science Foundation (U.S.) (PHY‐1055154)Alfred P. Sloan Foundation (BR2011‐066

Deep learning model for detection of pain intensity from facial expression

Many people who are suffering from a chronic pain face pe- riods of acute pain and resulting problems during their illness and ade- quate reporting of symptoms is necessary for treatment. Some patients have difficulties in adequately alerting caregivers to their pain or describ- ing the intensity which can impact on effective treatment. Pain and its intensity can be noticeable in ones face. Movements in facial muscles can depict ones current emotional state. Machine learning algorithms can detect pain intensity from facial expressions. The algorithm can ex- tract and classify facial expression of pain among patients. In this paper, we propose a new deep learning model for detection of pain intensity from facial expressions. This automatic pain detection system may help clinicians to detect pain and its intensity in patients and by doing this healthcare organizations may have access to more complete and more regular information of patients regarding their pain