(Wie) können Big Data und maschinelles Lernen die Gesundheitsversorgung verbessern?
YES! Team2020-11-11T11:03:37+01:00(Wie) können Big Data und maschinelles Lernen die Gesundheitsversorgung [...]
(Wie) können Big Data und maschinelles Lernen die Gesundheitsversorgung [...]
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The media continually reports about the failure to provide help. For example, the failure to provide help in the case of a deceased pensioner in an Essen bank. The phenomenon can be seen in almost every country and happens in very different situations, for example mobbing, racism, accidents and attacks, where many people do not intervene and help even though they could. In addition to the personal suffering of the victim, failure to assist has social consequences for society as it reduces macroeconomic welfare. ‘Nudges’ could be a way of increasing the willingness to help. A nudge is a method that influences the behaviour of people without having to resort to bans or laws or having to change social incentives. Nudges are becoming a more popular way of influencing people’s behaviour as new laws or regulations do not have to be issued as policy. Nudges could help to overcome individual blocks such as fear or uncertainty concerning responsibility, thus increasing the level of social welfare.
The challenge deals with the role of coal, oil and gas in the era of climate change. The combustion of fossil resources – carbohydrates – is responsible for a significant share of climate-warming greenhouse gas emissions because it emits carbon dioxide (CO2) into the atmosphere. Our modern economies use fossil resources in a multitude of sectors and processes, in particular in the energy sector and in transportation.
Economic forecasts do not necessarily have the best reputation in public. Nobody genuinely understands what they are about, and at the same time, they are prone to error in many people’s eyes. In the course of this challenge, we want to learn the sense – and nonsense – of economic forecasts and develop methods to improve them. Could we use, for example, indications in social media that relate to economic trends? Alternatively, do we need different indicators to provide better forecasts for growth, unemployment rates, and inflation? Moreover, above all, what makes a forecast a useful forecast? Which institutions publish economic forecasts regularly – both in Germany and worldwide? How do these forecasts differ and why are there these differences? What are the main elements of economic forecasts? Which economic parameters must be included? Which methods to forecast an economic development can be differentiated? For which purpose do researchers use these methods respectively?
Women earn less than men, in Germany and all other countries of the world. The so-called gender wage gap (also: gender pay gap), the average gap between the gross hourly wage of men and women, amounts to about 21 percent in Germany. Different explanations have been put forward to explain this gap. For instance, it can be shown that part of the gap is explained by women choosing jobs that pay lower wages. Moreover, women take career breaks much more often and for longer time spans to take care of kids and family. These career breaks are another reason why women are paid on average lower wages than men. But even when we control for such individual characteristics like education, work experience, job choice, working hours etc., unexplained residual remains, a wage gap that cannot be traced back to individual differences in these variables.