Spreading in Social Systems: Reflections

In this final chapter, we consider the state-of-the-art for spreading in social systems and discuss the future of the field. As part of this reflection, we identify a set of key challenges ahead. The challenges include the following questions: how can we improve the quality, quantity, extent, and accessibility of datasets? How can we extract more information from limited datasets? How can we take individual cognition and decision making processes into account? How can we incorporate other complexity of the real contagion processes? Finally, how can we translate research into positive real-world impact? In the following, we provide more context for each of these open questions.Comment: 7 pages, chapter to appear in "Spreading Dynamics in Social Systems"; Eds. Sune Lehmann and Yong-Yeol Ahn, Springer Natur

From Social Data Mining to Forecasting Socio-Economic Crisis

Socio-economic data mining has a great potential in terms of gaining a better understanding of problems that our economy and society are facing, such as financial instability, shortages of resources, or conflicts. Without large-scale data mining, progress in these areas seems hard or impossible. Therefore, a suitable, distributed data mining infrastructure and research centers should be built in Europe. It also appears appropriate to build a network of Crisis Observatories. They can be imagined as laboratories devoted to the gathering and processing of enormous volumes of data on both natural systems such as the Earth and its ecosystem, as well as on human techno-socio-economic systems, so as to gain early warnings of impending events. Reality mining provides the chance to adapt more quickly and more accurately to changing situations. Further opportunities arise by individually customized services, which however should be provided in a privacy-respecting way. This requires the development of novel ICT (such as a self- organizing Web), but most likely new legal regulations and suitable institutions as well. As long as such regulations are lacking on a world-wide scale, it is in the public interest that scientists explore what can be done with the huge data available. Big data do have the potential to change or even threaten democratic societies. The same applies to sudden and large-scale failures of ICT systems. Therefore, dealing with data must be done with a large degree of responsibility and care. Self-interests of individuals, companies or institutions have limits, where the public interest is affected, and public interest is not a sufficient justification to violate human rights of individuals. Privacy is a high good, as confidentiality is, and damaging it would have serious side effects for society.Comment: 65 pages, 1 figure, Visioneer White Paper, see http://www.visioneer.ethz.c

A Triple Test for Behavioral Economics Models and Public Health Policy

We propose a triple test to evaluate the usefulness of behavioral economics models for public health policy. Test 1 is whether the model provides reasonably new insights. Test 2 is on whether these have been properly applied to policy settings. Test 3 is whether they are corroborated by evidence. Where a test is not passed, this may point to directions for needed further research. We exemplify by considering the cases of social interactions models, self-control models and, in relation to health message framing, prospect theory; out of these, only a correctly applied prospect theory fully passes the tests at present

Hepcidin in iron overload disorders

Hepcidin is the principal regulator of iron absorption in humans. The peptide inhibits cellular iron efflux by binding to the iron export channel ferroportin and inducing its internalization and degradation. Either hepcidin deficiency or alterations in its target, ferroportin, would be expected to result in dysregulated iron absorption, tissue maldistribution of iron, and iron overload. Indeed, hepcidin deficiency has been reported in hereditary hemochromatosis and attributed to mutations in HFE, transferrin receptor 2, hemojuvelin, and the hepcidin gene itself. We measured urinary hepcidin in patients with other genetic causes of iron overload. Hepcidin was found to be suppressed in patients with thalassemia syndromes and congenital dyserythropoietic anemia type 1 and was undetectable in patients with juvenile hemochromatosis with HAMP mutations. Of interest, urine hepcidin levels were significantly elevated in 2 patients with hemochromatosis type 4. These findings extend the spectrum of iron disorders with hepcidin deficiency and underscore the critical importance of the hepcidin–ferroportin interaction in iron homeostasis